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Sample records for time series ts

  1. BiGGEsTS: integrated environment for biclustering analysis of time series gene expression data

    Directory of Open Access Journals (Sweden)

    Madeira Sara C

    2009-07-01

    Full Text Available Abstract Background The ability to monitor changes in expression patterns over time, and to observe the emergence of coherent temporal responses using expression time series, is critical to advance our understanding of complex biological processes. Biclustering has been recognized as an effective method for discovering local temporal expression patterns and unraveling potential regulatory mechanisms. The general biclustering problem is NP-hard. In the case of time series this problem is tractable, and efficient algorithms can be used. However, there is still a need for specialized applications able to take advantage of the temporal properties inherent to expression time series, both from a computational and a biological perspective. Findings BiGGEsTS makes available state-of-the-art biclustering algorithms for analyzing expression time series. Gene Ontology (GO annotations are used to assess the biological relevance of the biclusters. Methods for preprocessing expression time series and post-processing results are also included. The analysis is additionally supported by a visualization module capable of displaying informative representations of the data, including heatmaps, dendrograms, expression charts and graphs of enriched GO terms. Conclusion BiGGEsTS is a free open source graphical software tool for revealing local coexpression of genes in specific intervals of time, while integrating meaningful information on gene annotations. It is freely available at: http://kdbio.inesc-id.pt/software/biggests. We present a case study on the discovery of transcriptional regulatory modules in the response of Saccharomyces cerevisiae to heat stress.

  2. Modeling Time Series Data for Supervised Learning

    Science.gov (United States)

    Baydogan, Mustafa Gokce

    2012-01-01

    Temporal data are increasingly prevalent and important in analytics. Time series (TS) data are chronological sequences of observations and an important class of temporal data. Fields such as medicine, finance, learning science and multimedia naturally generate TS data. Each series provide a high-dimensional data vector that challenges the learning…

  3. Time Series Observations in the North Indian Ocean

    Digital Repository Service at National Institute of Oceanography (India)

    Shenoy, D.M.; Naik, H.; Kurian, S.; Naqvi, S.W.A.; Khare, N.

    Ocean and the ongoing time series study (Candolim Time Series; CaTS) off Goa. In addition, this article also focuses on the new time series initiative in the Arabian Sea and the Bay of Bengal under Sustained Indian Ocean Biogeochemistry and Ecosystem...

  4. Visual time series analysis

    DEFF Research Database (Denmark)

    Fischer, Paul; Hilbert, Astrid

    2012-01-01

    We introduce a platform which supplies an easy-to-handle, interactive, extendable, and fast analysis tool for time series analysis. In contrast to other software suits like Maple, Matlab, or R, which use a command-line-like interface and where the user has to memorize/look-up the appropriate...... commands, our application is select-and-click-driven. It allows to derive many different sequences of deviations for a given time series and to visualize them in different ways in order to judge their expressive power and to reuse the procedure found. For many transformations or model-ts, the user may...... choose between manual and automated parameter selection. The user can dene new transformations and add them to the system. The application contains efficient implementations of advanced and recent techniques for time series analysis including techniques related to extreme value analysis and filtering...

  5. Time series patterns and language support in DBMS

    Science.gov (United States)

    Telnarova, Zdenka

    2017-07-01

    This contribution is focused on pattern type Time Series as a rich in semantics representation of data. Some example of implementation of this pattern type in traditional Data Base Management Systems is briefly presented. There are many approaches how to manipulate with patterns and query patterns. Crucial issue can be seen in systematic approach to pattern management and specific pattern query language which takes into consideration semantics of patterns. Query language SQL-TS for manipulating with patterns is shown on Time Series data.

  6. Stochastic modeling for time series InSAR: with emphasis on atmospheric effects

    Science.gov (United States)

    Cao, Yunmeng; Li, Zhiwei; Wei, Jianchao; Hu, Jun; Duan, Meng; Feng, Guangcai

    2018-02-01

    Despite the many applications of time series interferometric synthetic aperture radar (TS-InSAR) techniques in geophysical problems, error analysis and assessment have been largely overlooked. Tropospheric propagation error is still the dominant error source of InSAR observations. However, the spatiotemporal variation of atmospheric effects is seldom considered in the present standard TS-InSAR techniques, such as persistent scatterer interferometry and small baseline subset interferometry. The failure to consider the stochastic properties of atmospheric effects not only affects the accuracy of the estimators, but also makes it difficult to assess the uncertainty of the final geophysical results. To address this issue, this paper proposes a network-based variance-covariance estimation method to model the spatiotemporal variation of tropospheric signals, and to estimate the temporal variance-covariance matrix of TS-InSAR observations. The constructed stochastic model is then incorporated into the TS-InSAR estimators both for parameters (e.g., deformation velocity, topography residual) estimation and uncertainty assessment. It is an incremental and positive improvement to the traditional weighted least squares methods to solve the multitemporal InSAR time series. The performance of the proposed method is validated by using both simulated and real datasets.

  7. A novel stabilization condition for T-S polynomial fuzzy system with time-delay:A sum-of-squares approach

    OpenAIRE

    Tsai, Shun Hung; Chen, Yu-An; Chen, Yu-Wen; Lo, Ji-Chang; Lam, Hak-Keung

    2017-01-01

    A novel stabilization problem for T-S polynomial fuzzy system with time-delay is investigated in this paper. Firstly, a polynomial fuzzy controller for T-S polynomial fuzzy system with time-delay is proposed. In addition, based on polynomial Lyapunov-Krasovskii function and the developed polynomial slack variable matrices, a novel stabilization condition for T-S polynomial fuzzy system with time-delay is presented in terms of sum-of-square (SOS) form. Lastly, nonlinear system with time-delay ...

  8. Agent Based Fuzzy T-S Multi-Model System and Its Applications

    Directory of Open Access Journals (Sweden)

    Xiaopeng Zhao

    2015-11-01

    Full Text Available Based on the basic concepts of agent and fuzzy T-S model, an agent based fuzzy T-S multi-model (ABFT-SMM system is proposed in this paper. Different from the traditional method, the parameters and the membership value of the agent can be adjusted along with the process. In this system, each agent can be described as a dynamic equation, which can be seen as the local part of the multi-model, and it can execute the task alone or collaborate with other agents to accomplish a fixed goal. It is proved in this paper that the agent based fuzzy T-S multi-model system can approximate any linear or nonlinear system at arbitrary accuracy. The applications to the benchmark problem of chaotic time series prediction, water heater system and waste heat utilizing process illustrate the viability and the efficiency of the mentioned approach. At the same time, the method can be easily used to a number of engineering fields, including identification, nonlinear control, fault diagnostics and performance analysis.

  9. MODIS-derived EVI, NDVI and WDRVI time series to estimate phenological metrics in French deciduous forests

    Science.gov (United States)

    Testa, S.; Soudani, K.; Boschetti, L.; Borgogno Mondino, E.

    2018-02-01

    Monitoring forest phenology allows us to study the effects of climate change on vegetated land surfaces. Daily and composite time series (TS) of several vegetation indices (VIs) from MODerate resolution Imaging Spectroradiometer (MODIS) data have been widely used in scientific works for phenological studies since the beginning of the MODIS mission. The objective of this work was to use MODIS data to find the best VI/TS combination to estimate start-of-season (SOS) and end-of-season (EOS) dates across 50 temperate deciduous forests. Our research used as inputs 2001-2012 daily reflectance from MOD09GQ/MOD09GA products and 16-day composite VIs from the MOD13Q1 dataset. The 50 pixels centered on the 50 forest plots were extracted from the above-mentioned MODIS imagery; we then generated 5 different types of TS (1 daily from MOD09 and 4 composite from MOD13Q1) and used all of them to implement 6 VIs, obtaining 30 VI/TS combinations. SOS and EOS estimates were determined for each pixel/year and each VI/TS combination. SOS/EOS estimations were then validated against ground phenological observations. Results showed that, in our test areas, composite TS, if actual acquisition date is considered, performed mostly better than daily TS. EVI, WDRVI0.20 and NDVI were more suitable to SOS estimation, while WDRVI0.05 and EVI were more convenient in estimating early and advanced EOS, respectively.

  10. How to determine life expectancy change of air pollution mortality: a time series study

    Directory of Open Access Journals (Sweden)

    Chau PYK

    2011-03-01

    Full Text Available Abstract Background Information on life expectancy (LE change is of great concern for policy makers, as evidenced by discussions of the "harvesting" (or "mortality displacement" issue, i.e. how large an LE loss corresponds to the mortality results of time series (TS studies. Whereas loss of LE attributable to chronic air pollution exposure can be determined from cohort studies, using life table methods, conventional TS studies have identified only deaths due to acute exposure, during the immediate past (typically the preceding one to five days, and they provide no information about the LE loss per death. Methods We show how to obtain information on population-average LE loss by extending the observation window (largest "lag" of TS to include a sufficient number of "impact coefficients" for past exposures ("lags". We test several methods for determining these coefficients. Once all of the coefficients have been determined, the LE change is calculated as time integral of the relative risk change after a permanent step change in exposure. Results The method is illustrated with results for daily data of non-accidental mortality from Hong Kong for 1985 - 2005, regressed against PM10 and SO2 with observation windows up to 5 years. The majority of the coefficients is statistically significant. The magnitude of the SO2 coefficients is comparable to those for PM10. But a window of 5 years is not sufficient and the results for LE change are only a lower bound; it is consistent with what is implied by other studies of long term impacts. Conclusions A TS analysis can determine the LE loss, but if the observation window is shorter than the relevant exposures one obtains only a lower bound.

  11. H∞ Filtering for Discrete Markov Jump Singular Systems with Mode-Dependent Time Delay Based on T-S Fuzzy Model

    Directory of Open Access Journals (Sweden)

    Cheng Gong

    2014-01-01

    Full Text Available This paper investigates the H∞ filtering problem of discrete singular Markov jump systems (SMJSs with mode-dependent time delay based on T-S fuzzy model. First, by Lyapunov-Krasovskii functional approach, a delay-dependent sufficient condition on H∞-disturbance attenuation is presented, in which both stability and prescribed H∞ performance are required to be achieved for the filtering-error systems. Then, based on the condition, the delay-dependent H∞ filter design scheme for SMJSs with mode-dependent time delay based on T-S fuzzy model is developed in term of linear matrix inequality (LMI. Finally, an example is given to illustrate the effectiveness of the result.

  12. Delay-Dependent Finite-Time H∞ Controller Design for a Kind of Nonlinear Descriptor Systems via a T-S Fuzzy Model

    Directory of Open Access Journals (Sweden)

    Baoyan Zhu

    2015-01-01

    Full Text Available Delay-dependent finite-time H∞ controller design problems are investigated for a kind of nonlinear descriptor system via a T-S fuzzy model in this paper. The solvable conditions of finite-time H∞ controller are given to guarantee that the loop-closed system is impulse-free and finite-time bounded and holds the H∞ performance to a prescribed disturbance attenuation level γ. The method given is the ability to eliminate the impulsive behavior caused by descriptor systems in a finite-time interval, which confirms the existence and uniqueness of solutions in the interval. By constructing a nonsingular matrix, we overcome the difficulty that results in an infeasible linear matrix inequality (LMI. Using the FEASP solver and GEVP solver of the LMI toolbox, we perform simulations to validate the proposed methods for a nonlinear descriptor system via the T-S fuzzy model, which shows the application of the T-S fuzzy method in studying the finite-time control problem of a nonlinear system. Meanwhile the method was also applied to the biological economy system to eliminate impulsive behavior at the bifurcation value, stabilize the loop-closed system in a finite-time interval, and achieve a H∞ performance level.

  13. TS-1 and TS-2 transient overpower tests on FFTF fuel

    International Nuclear Information System (INIS)

    Pitner, A.L.; Ferrell, P.C.; Culley, G.E.; Weber, E.T.

    1985-01-01

    The TS-1 and TS-2 TREAT transient experiments subjected a low burnup (2 MWd/kg) and a medium burnup (58 MWd/kg), respectively, FFTF irradiated fuel pin to unprotected 5 cents/s overpower transient conditions. The fuel pin failure response was similar in the two tests, which demonstrated a large margin to failure (P/P 0 > 3) and a favorable upper level failure location. Thus, for these transient conditions, burnup effects on transient performance appeared to be minimal in the range tested. Pin disruption in the medium burnup TS-2 test was more severe due to the higher fission gas pressurization, but failure occurred at only a 5% lower power level than for the low burnup TS-1 fuel pin. Both tests exhibited axial extrusion of molten fuel to the region above the fuel column several seconds before pin failure, demonstrating a potentially beneficial inherent safety mechanism to delay failure and mitigate accident consequences

  14. Movable Thomson scattering system based on optical fiber (TS-probe)

    International Nuclear Information System (INIS)

    Narihara, K.; Hayashi, H.

    2009-01-01

    This paper proposes a movable compact Thomson scattering (TS) system based on optical fibers (TS-probe). A TS-probe consists of a probe head, optical fiber, a laser-diode, polychromators and lock-in amplifiers. A laser beam optics and light collection optics are mounted rigidly on a probe head with a fixed scattering position. Laser light and scattered light are transmitted by flexible optical fibers, enabling us to move the TS-prove head freely during plasma discharge. The light signal scattered from an amplitude-modulated laser is detected against the plasma light based on the principle of the lock-in amplifier. With a modulated laser power of 300W, the scattered signal from a sheet plasma of 15 mm depth and n e -10 19 m -3 will be measured with 10% accuracy by setting the integrating time to 0.1 s. The TS-probe head is like a 1/20 model of the currently operating LHD-TS. (author)

  15. GPS Position Time Series @ JPL

    Science.gov (United States)

    Owen, Susan; Moore, Angelyn; Kedar, Sharon; Liu, Zhen; Webb, Frank; Heflin, Mike; Desai, Shailen

    2013-01-01

    Different flavors of GPS time series analysis at JPL - Use same GPS Precise Point Positioning Analysis raw time series - Variations in time series analysis/post-processing driven by different users. center dot JPL Global Time Series/Velocities - researchers studying reference frame, combining with VLBI/SLR/DORIS center dot JPL/SOPAC Combined Time Series/Velocities - crustal deformation for tectonic, volcanic, ground water studies center dot ARIA Time Series/Coseismic Data Products - Hazard monitoring and response focused center dot ARIA data system designed to integrate GPS and InSAR - GPS tropospheric delay used for correcting InSAR - Caltech's GIANT time series analysis uses GPS to correct orbital errors in InSAR - Zhen Liu's talking tomorrow on InSAR Time Series analysis

  16. [Construction of a system for proper TS-1 application].

    Science.gov (United States)

    Azuma, Sayaka; Morimoto, Shigefumi; Kagawa, Kunihiko; Katada, Yoshinori; Hasegawa, Kenji; Tabuse, Katsuyoshi

    2010-03-01

    TS-1 is an oral anti-tumor drug, which contains 5-chloro-2, 4-dihydroxypyridine (CDHP), a compound mainly excreted in urine. Since the CDHP concentration is increased among patients with impaired renal function, the frequency of side effects of TS-1 increases in such patients. Therefore, we constructed a computer-aided system that enables prompt monitoring of creatinine clearance (Ccr) calculated from the serum creatinine levels of patients prescribed TS-1 at the time pharmacists prepare the medicine. With this system, we found two cases who were prescribed TS-1, despite their decreased Ccr. One was a patient whose estimated Ccr was less than 30 mL/min/m2. With such renal malfunction, pharmacokinetics of the drug was considerably changed compared with normal control, and the dosage should be reduced. The other case presented with severe jaundice and had only a mild decrease of renal function (Ccr: 50 mL/min/m2). So we measured the concentration of uracil in the urine and performed a drug lymphocyte stimulation test for further investigation of concomitant affecting factors. Our system is useful because it can show pharmacists both the dosage TS-1 patients take and their renal function at a glance in real time. This system can be adapted for every medicine which might accumulate in patients with renal dysfunction.

  17. Time series analysis time series analysis methods and applications

    CERN Document Server

    Rao, Tata Subba; Rao, C R

    2012-01-01

    The field of statistics not only affects all areas of scientific activity, but also many other matters such as public policy. It is branching rapidly into so many different subjects that a series of handbooks is the only way of comprehensively presenting the various aspects of statistical methodology, applications, and recent developments. The Handbook of Statistics is a series of self-contained reference books. Each volume is devoted to a particular topic in statistics, with Volume 30 dealing with time series. The series is addressed to the entire community of statisticians and scientists in various disciplines who use statistical methodology in their work. At the same time, special emphasis is placed on applications-oriented techniques, with the applied statistician in mind as the primary audience. Comprehensively presents the various aspects of statistical methodology Discusses a wide variety of diverse applications and recent developments Contributors are internationally renowened experts in their respect...

  18. Highly comparative time-series analysis: the empirical structure of time series and their methods.

    Science.gov (United States)

    Fulcher, Ben D; Little, Max A; Jones, Nick S

    2013-06-06

    The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines.

  19. Introduction to Time Series Modeling

    CERN Document Server

    Kitagawa, Genshiro

    2010-01-01

    In time series modeling, the behavior of a certain phenomenon is expressed in relation to the past values of itself and other covariates. Since many important phenomena in statistical analysis are actually time series and the identification of conditional distribution of the phenomenon is an essential part of the statistical modeling, it is very important and useful to learn fundamental methods of time series modeling. Illustrating how to build models for time series using basic methods, "Introduction to Time Series Modeling" covers numerous time series models and the various tools f

  20. A Comparison of Tropical Storm (TS) and Non-TS Gust Factors for Assessing Peak Wind Probabilities at the Eastern Range

    Science.gov (United States)

    Merceret, Francis J.; Crawford, Winifred C.

    2010-01-01

    Peak wind speed is an important forecast element to ensure the safety of personnel and flight hardware at Kennedy Space Center (KSC) and the Cape Canaveral Air Force Station (CCAFS) in East-Central Florida. The 45th Weather Squadron (45 WS), the organization that issues forecasts for the KSC/CCAFS area, finds that peak winds are more difficult to forecast than mean winds. This difficulty motivated the 45 WS to request two independent studies. The first (Merceret 2009) was the development of a reliable model for gust factors (GF) relating the peak to the mean wind speed in tropical storms (TS). The second (Lambert et al. 2008) was a climatological study of non-TS cool season (October-April) mean and peak wind speeds by the Applied Meteorology Unit (AMU; Bauman et al. 2004) without the use of GF. Both studies presented their statistics as functions of mean wind speed and height. Most of the few comparisons of TS and non-TS GF in the literature suggest that non-TS GF at a given height and mean wind speed are smaller than the corresponding TS GF. The investigation reported here converted the non-TS peak wind statistics calculated by the AMU to the equivalent GF statistics and compared them with the previous TS GF results. The advantage of this effort over all previously reported studies of its kind is that the TS and non-TS data were taken from the same towers in the same locations. This eliminates differing surface attributes, including roughness length and thermal properties, as a major source of variance in the comparison. The goal of this study is two-fold: to determine the relationship between the non-TS and TS GF and their standard deviations (GFSD) and to determine if models similar to those developed for TS data in Merceret (2009) could be developed for the non-TS environment. The results are consistent with the literature, but include much more detailed, quantitative information on the nature of the relationship between TS and non-TS GF and GFSD as a function

  1. International Work-Conference on Time Series

    CERN Document Server

    Pomares, Héctor; Valenzuela, Olga

    2017-01-01

    This volume of selected and peer-reviewed contributions on the latest developments in time series analysis and forecasting updates the reader on topics such as analysis of irregularly sampled time series, multi-scale analysis of univariate and multivariate time series, linear and non-linear time series models, advanced time series forecasting methods, applications in time series analysis and forecasting, advanced methods and online learning in time series and high-dimensional and complex/big data time series. The contributions were originally presented at the International Work-Conference on Time Series, ITISE 2016, held in Granada, Spain, June 27-29, 2016. The series of ITISE conferences provides a forum for scientists, engineers, educators and students to discuss the latest ideas and implementations in the foundations, theory, models and applications in the field of time series analysis and forecasting.  It focuses on interdisciplinary and multidisciplinary rese arch encompassing the disciplines of comput...

  2. SAS4A and FPIN2X validation for slow ramp TOP accidents: experiments TS-1 and TS-2

    International Nuclear Information System (INIS)

    Hill, D.J.

    1986-01-01

    The purpose of this paper is to present further results in the series of experimental analyses being performed using SAS4A and FPIN2X in order to provide a systematic validation of these codes. The two experiments discussed here, TS-1 and TS-2, were performed by Westinghouse Hanford/Hanford Engineering Development Laboratory (WHC/HEDL) in the Transient Reactor Test (TREAT) Facility. They were slow ramp transient overpowers (TOPs) of ∼ 5 cent/s equivalent Fast Flux Test Facility (FFTF) ramp rate, single-pin experiments in flowing sodium loops. The good agreement found here adds significantly to the experimental data base that provides the foundation for SAS4A and FPIN2X validation. It also shows that prefailure internal fuel motion is a phenomenon that has to be correctly accounted for, not only as a potential inherent safety mechanism, but also before any accurate prediction of fuel failure and subsequent fuel motion and the associated reactivity effects can be made. This is also true for metal-fueled pins. This capability is provided by PINACLE, which is being incorporated into SAS4A

  3. High-Speed Solution of Spacecraft Trajectory Problems Using Taylor Series Integration

    Science.gov (United States)

    Scott, James R.; Martini, Michael C.

    2010-01-01

    It has been known for some time that Taylor series (TS) integration is among the most efficient and accurate numerical methods in solving differential equations. However, the full benefit of the method has yet to be realized in calculating spacecraft trajectories, for two main reasons. First, most applications of Taylor series to trajectory propagation have focused on relatively simple problems of orbital motion or on specific problems and have not provided general applicability. Second, applications that have been more general have required use of a preprocessor, which inevitably imposes constraints on computational efficiency. The latter approach includes the work of Berryman et al., who solved the planetary n-body problem with relativistic effects. Their work specifically noted the computational inefficiencies arising from use of a preprocessor and pointed out the potential benefit of manually coding derivative routines. In this Engineering Note, we report on a systematic effort to directly implement Taylor series integration in an operational trajectory propagation code: the Spacecraft N-Body Analysis Program (SNAP). The present Taylor series implementation is unique in that it applies to spacecraft virtually anywhere in the solar system and can be used interchangeably with another integration method. SNAP is a high-fidelity trajectory propagator that includes force models for central body gravitation with N X N harmonics, other body gravitation with N X N harmonics, solar radiation pressure, atmospheric drag (for Earth orbits), and spacecraft thrusting (including shadowing). The governing equations are solved using an eighth-order Runge-Kutta Fehlberg (RKF) single-step method with variable step size control. In the present effort, TS is implemented by way of highly integrated subroutines that can be used interchangeably with RKF. This makes it possible to turn TS on or off during various phases of a mission. Current TS force models include central body

  4. From Networks to Time Series

    Science.gov (United States)

    Shimada, Yutaka; Ikeguchi, Tohru; Shigehara, Takaomi

    2012-10-01

    In this Letter, we propose a framework to transform a complex network to a time series. The transformation from complex networks to time series is realized by the classical multidimensional scaling. Applying the transformation method to a model proposed by Watts and Strogatz [Nature (London) 393, 440 (1998)], we show that ring lattices are transformed to periodic time series, small-world networks to noisy periodic time series, and random networks to random time series. We also show that these relationships are analytically held by using the circulant-matrix theory and the perturbation theory of linear operators. The results are generalized to several high-dimensional lattices.

  5. Duality between Time Series and Networks

    Science.gov (United States)

    Campanharo, Andriana S. L. O.; Sirer, M. Irmak; Malmgren, R. Dean; Ramos, Fernando M.; Amaral, Luís A. Nunes.

    2011-01-01

    Studying the interaction between a system's components and the temporal evolution of the system are two common ways to uncover and characterize its internal workings. Recently, several maps from a time series to a network have been proposed with the intent of using network metrics to characterize time series. Although these maps demonstrate that different time series result in networks with distinct topological properties, it remains unclear how these topological properties relate to the original time series. Here, we propose a map from a time series to a network with an approximate inverse operation, making it possible to use network statistics to characterize time series and time series statistics to characterize networks. As a proof of concept, we generate an ensemble of time series ranging from periodic to random and confirm that application of the proposed map retains much of the information encoded in the original time series (or networks) after application of the map (or its inverse). Our results suggest that network analysis can be used to distinguish different dynamic regimes in time series and, perhaps more importantly, time series analysis can provide a powerful set of tools that augment the traditional network analysis toolkit to quantify networks in new and useful ways. PMID:21858093

  6. Long time series

    DEFF Research Database (Denmark)

    Hisdal, H.; Holmqvist, E.; Hyvärinen, V.

    Awareness that emission of greenhouse gases will raise the global temperature and change the climate has led to studies trying to identify such changes in long-term climate and hydrologic time series. This report, written by the......Awareness that emission of greenhouse gases will raise the global temperature and change the climate has led to studies trying to identify such changes in long-term climate and hydrologic time series. This report, written by the...

  7. A Course in Time Series Analysis

    CERN Document Server

    Peña, Daniel; Tsay, Ruey S

    2011-01-01

    New statistical methods and future directions of research in time series A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, a

  8. TS/EL ELECTRICAL WORK REQUEST FORM

    CERN Multimedia

    TS Department

    2008-01-01

    To facilitate the processing of requests for minor electrical installation work to be carried out by the TS/EL group, a new procedure designed to improve contacts and exchange of information with customers has been set up. The procedure comprises a check to determine whether the TS/EL or the TS/FM group is responsible for the area where the electrical work is to be done. If the work is to be performed by the EL group, an on-line request form must be completed. The following steps must be completed: On a web browser, use the link http://ts-dep.web.cern.ch/ts-dep/groups/el/el.htm In the left-hand menu choose ‘Demande de Travaux Electriques’ Enter the building number to check which group to contact and click ‘FIND’ If the area is: Under FM’s responsibility:\tCall 77777 Under EL’s responsibility:\tClick the link to the work form, complete it and send it (click ‘ENVOYER’) IMPORTANT This form is for minor electrical installation requests only. Please call 72201 in t...

  9. Assessment of TS-1, a thick cane mutant

    International Nuclear Information System (INIS)

    Shama Rao, H.K.

    1979-01-01

    A true breeding thick cane mutant TS-1, induced by radiations, was obtained in variety Co-419. TS-1 was found to be superior to Co-419 with respect to cane size, weight, yield and juice quality. The thick canes of TS-1 were found to be solid even at 14 months age and so also their ratoons. The tillering habit of TS-1 has a definite advantage over other varieties with respect to easy intercultural field operations. TS-1 is now being tested under various agroclimatic zones in Karnataka, Maharashtra and U.P. (auth.)

  10. Kolmogorov Space in Time Series Data

    OpenAIRE

    Kanjamapornkul, K.; Pinčák, R.

    2016-01-01

    We provide the proof that the space of time series data is a Kolmogorov space with $T_{0}$-separation axiom using the loop space of time series data. In our approach we define a cyclic coordinate of intrinsic time scale of time series data after empirical mode decomposition. A spinor field of time series data comes from the rotation of data around price and time axis by defining a new extradimension to time series data. We show that there exist hidden eight dimensions in Kolmogorov space for ...

  11. On the Use of Running Trends as Summary Statistics for Univariate Time Series and Time Series Association

    OpenAIRE

    Trottini, Mario; Vigo, Isabel; Belda, Santiago

    2015-01-01

    Given a time series, running trends analysis (RTA) involves evaluating least squares trends over overlapping time windows of L consecutive time points, with overlap by all but one observation. This produces a new series called the “running trends series,” which is used as summary statistics of the original series for further analysis. In recent years, RTA has been widely used in climate applied research as summary statistics for time series and time series association. There is no doubt that ...

  12. Multiple Indicator Stationary Time Series Models.

    Science.gov (United States)

    Sivo, Stephen A.

    2001-01-01

    Discusses the propriety and practical advantages of specifying multivariate time series models in the context of structural equation modeling for time series and longitudinal panel data. For time series data, the multiple indicator model specification improves on classical time series analysis. For panel data, the multiple indicator model…

  13. Taylor Series-Based Long-Term Creep-Life Prediction of Alloy 617

    International Nuclear Information System (INIS)

    Yin, Song Nan; Kim, Woo Gon; Kim, Yong Wan; Park, Jae Young; Kim, Soen Jin

    2010-01-01

    In this study, a Taylor series (T-S) model based on the Arrhenius, McVetty, and Monkman-Grant equations was developed using a mathematical analysis. In order to reduce fitting errors, the McVetty equation was transformed by considering the first three terms of the Taylor series equation. The model parameters were accurately determined by a statistical technique of maximum likelihood estimation, and this model was applied to the creep data of alloy 617. The T-S model results showed better agreement with the experimental data than other models such as the Eno, exponential, and L-M models. In particular, the T-S model was converted into an isothermal Taylor series (IT-S) model that can predict the creep strength at a given temperature. It was identified that the estimations obtained using the converted ITS model was better than that obtained using the T-S model for predicting the long-term creep life of alloy 617

  14. The Irminger Sea and the Iceland Sea time series measurements of sea water carbon and nutrient chemistry 1983–2008

    Directory of Open Access Journals (Sweden)

    J. Olafsson

    2010-03-01

    Full Text Available This paper describes the ways and means of assembling and quality controling the Irminger Sea and Iceland Sea time-series biogeochemical data which are included in the CARINA data set. The Irminger Sea and the Iceland Sea are hydrographically different regions where measurements of sea water carbon and nutrient chemistry were started in 1983. The sampling is seasonal, four times a year. The carbon chemistry is studied with measurements of the partial pressure of carbon dioxide in seawater, pCO2, and total dissolved inorganic carbon, TCO2. The carbon chemistry data are for surface waters only until 1991 when water column sampling was initiated. Other measured parameters are salinity, dissolved oxygen and the inorganic nutrients nitrate, phosphate and silicate. Because of the CARINA criteria for secondary quality control, depth >1500 m, the IRM-TS could not be included in the routine QC and the IS-TS only in a limited way. However, with the information provided here, the quality of the data can be assessed, e.g. on the basis of the results obtained with the use of reference materials.

  15. Transcutaneous Spinal Direct Current Stimulation (tsDCS

    Directory of Open Access Journals (Sweden)

    Filippo eCogiamanian

    2012-07-01

    Full Text Available In the past ten years renewed interest has centered on non-invasive transcutaneous weak direct currents applied over the scalp to modulate cortical excitability (brain polarization or transcranial direct current stimulation, tDCS. Extensive literature shows that tDCS induces marked changes in cortical excitability that outlast stimulation.Aiming at developing a new, non invasive, approach to spinal cord neuromodulation we assessed the after-effects of thoracic transcutaneous spinal DC stimulation (tsDCS on somatosensory potentials (SEPs evoked in healthy subjects by posterior tibial nerve (PTN stimulation. Our findings showed that thoracic anodal tsDCS depresses the cervico-medullary PTN-SEP component (P30 without eliciting adverse effects. tsDCS also modulates post-activation H-reflex dynamics. Later works further confirmed that transcutaneous electric fields modulate spinal cord function. Subsequent studies in our laboratory showed that tsDCS modulates the flexion reflex in the human lower limb. Besides influencing the laser evoked potentials, tsDCS increases pain tolerance in healthy subjects. Hence, though the underlying mechanisms remain speculative, tsDCS modulates activity in lemniscal, spinothalamic and segmental motor systems.Here we review currently available experimental evidence that non-invasive spinal cord stimulation influences spinal function in humans and argue that, by focally modulating spinal excitability, tsDCS could provide a novel therapeutic tool complementary to drugs and invasive spinal cord stimulation in managing various pathologic conditions, including pain.

  16. Time Series Momentum

    DEFF Research Database (Denmark)

    Moskowitz, Tobias J.; Ooi, Yao Hua; Heje Pedersen, Lasse

    2012-01-01

    We document significant “time series momentum” in equity index, currency, commodity, and bond futures for each of the 58 liquid instruments we consider. We find persistence in returns for one to 12 months that partially reverses over longer horizons, consistent with sentiment theories of initial...... under-reaction and delayed over-reaction. A diversified portfolio of time series momentum strategies across all asset classes delivers substantial abnormal returns with little exposure to standard asset pricing factors and performs best during extreme markets. Examining the trading activities...

  17. International Work-Conference on Time Series

    CERN Document Server

    Pomares, Héctor

    2016-01-01

    This volume presents selected peer-reviewed contributions from The International Work-Conference on Time Series, ITISE 2015, held in Granada, Spain, July 1-3, 2015. It discusses topics in time series analysis and forecasting, advanced methods and online learning in time series, high-dimensional and complex/big data time series as well as forecasting in real problems. The International Work-Conferences on Time Series (ITISE) provide a forum for scientists, engineers, educators and students to discuss the latest ideas and implementations in the foundations, theory, models and applications in the field of time series analysis and forecasting. It focuses on interdisciplinary and multidisciplinary research encompassing the disciplines of computer science, mathematics, statistics and econometrics.

  18. Stochastic models for time series

    CERN Document Server

    Doukhan, Paul

    2018-01-01

    This book presents essential tools for modelling non-linear time series. The first part of the book describes the main standard tools of probability and statistics that directly apply to the time series context to obtain a wide range of modelling possibilities. Functional estimation and bootstrap are discussed, and stationarity is reviewed. The second part describes a number of tools from Gaussian chaos and proposes a tour of linear time series models. It goes on to address nonlinearity from polynomial or chaotic models for which explicit expansions are available, then turns to Markov and non-Markov linear models and discusses Bernoulli shifts time series models. Finally, the volume focuses on the limit theory, starting with the ergodic theorem, which is seen as the first step for statistics of time series. It defines the distributional range to obtain generic tools for limit theory under long or short-range dependences (LRD/SRD) and explains examples of LRD behaviours. More general techniques (central limit ...

  19. Graphical Data Analysis on the Circle: Wrap-Around Time Series Plots for (Interrupted) Time Series Designs.

    Science.gov (United States)

    Rodgers, Joseph Lee; Beasley, William Howard; Schuelke, Matthew

    2014-01-01

    Many data structures, particularly time series data, are naturally seasonal, cyclical, or otherwise circular. Past graphical methods for time series have focused on linear plots. In this article, we move graphical analysis onto the circle. We focus on 2 particular methods, one old and one new. Rose diagrams are circular histograms and can be produced in several different forms using the RRose software system. In addition, we propose, develop, illustrate, and provide software support for a new circular graphical method, called Wrap-Around Time Series Plots (WATS Plots), which is a graphical method useful to support time series analyses in general but in particular in relation to interrupted time series designs. We illustrate the use of WATS Plots with an interrupted time series design evaluating the effect of the Oklahoma City bombing on birthrates in Oklahoma County during the 10 years surrounding the bombing of the Murrah Building in Oklahoma City. We compare WATS Plots with linear time series representations and overlay them with smoothing and error bands. Each method is shown to have advantages in relation to the other; in our example, the WATS Plots more clearly show the existence and effect size of the fertility differential.

  20. Time Series with Long Memory

    OpenAIRE

    西埜, 晴久

    2004-01-01

    The paper investigates an application of long-memory processes to economic time series. We show properties of long-memory processes, which are motivated to model a long-memory phenomenon in economic time series. An FARIMA model is described as an example of long-memory model in statistical terms. The paper explains basic limit theorems and estimation methods for long-memory processes in order to apply long-memory models to economic time series.

  1. Visibility Graph Based Time Series Analysis.

    Science.gov (United States)

    Stephen, Mutua; Gu, Changgui; Yang, Huijie

    2015-01-01

    Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it's microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq) and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks.

  2. Visibility Graph Based Time Series Analysis.

    Directory of Open Access Journals (Sweden)

    Mutua Stephen

    Full Text Available Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it's microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks.

  3. Network structure of multivariate time series.

    Science.gov (United States)

    Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

    2015-10-21

    Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

  4. What marketing scholars should know about time series analysis : time series applications in marketing

    NARCIS (Netherlands)

    Horváth, Csilla; Kornelis, Marcel; Leeflang, Peter S.H.

    2002-01-01

    In this review, we give a comprehensive summary of time series techniques in marketing, and discuss a variety of time series analysis (TSA) techniques and models. We classify them in the sets (i) univariate TSA, (ii) multivariate TSA, and (iii) multiple TSA. We provide relevant marketing

  5. “And time future contained in time past”: British Modernist Poetry from T.S. Eliot to Ted Hughes

    Directory of Open Access Journals (Sweden)

    Tatiana N. Krasavchenko

    2017-09-01

    Full Text Available It seems that T.S. Eliot (1888–1965 and Ted Hughes (1930–1998 are poets of different individual talents and epochs. Hughes, a poet of the second half of the 20 th century, concentrated on the conflict between Nature and the human being destroying Nature and thus preparing the end of the humanity. T.S. Eliot was a poet of urban civilization, a reformer of the Anglophone modernist poetry. In his days, the “poetry of Nature” rooted in the pastoral poetry of the 17 th –18 th centuries and in Romanticism, existed but only at the margins of mainstream poetic trends. Nevertheless, one can find it even in Eliot’s poetry, particularly in his Landscapes — poems written at the beginning of the 1930s. Also, both Eliot and Hughes were modernists. Eliot standing at the beginning of modernism, Hughes at its end; though the latter lived in the time of postmodernism, he was not a postmodernist himself since he was not engaged in the deconstruction and fragmentation of the “world model.” Like Eliot, he was a poet with integral Weltanschauung and a bright individual talent. Like Eliot, he was a traditionalist, though Eliot being an American was a “conscious traditionalist,” while Hughes, an Englishman, was most likely a “born” traditionalist: instead of deconstructing tradition, he was bearing on it. His “great tradition” included Shakespeare, Blake, Coleridge, Kipling, Graves, and Dylan Thomas. Both Eliot and Hughes are poets of eschatological trend and mythologists. However, one will not find Eliot among the authorities enlisted by Hughes in his “great tradition.” In the time of Hughes, Eliot was rejected due to the regular change of mainstreams trends. British poetry in the second half of the 20 th century is “tired” of urban poetry, of “Nature denial,” so it comes back to its track, and the motives that existed in Eliot’s poetry only in the background are now brought to the forefront.

  6. Data mining in time series databases

    CERN Document Server

    Kandel, Abraham; Bunke, Horst

    2004-01-01

    Adding the time dimension to real-world databases produces Time SeriesDatabases (TSDB) and introduces new aspects and difficulties to datamining and knowledge discovery. This book covers the state-of-the-artmethodology for mining time series databases. The novel data miningmethods presented in the book include techniques for efficientsegmentation, indexing, and classification of noisy and dynamic timeseries. A graph-based method for anomaly detection in time series isdescribed and the book also studies the implications of a novel andpotentially useful representation of time series as strings. Theproblem of detecting changes in data mining models that are inducedfrom temporal databases is additionally discussed.

  7. Models for dependent time series

    CERN Document Server

    Tunnicliffe Wilson, Granville; Haywood, John

    2015-01-01

    Models for Dependent Time Series addresses the issues that arise and the methodology that can be applied when the dependence between time series is described and modeled. Whether you work in the economic, physical, or life sciences, the book shows you how to draw meaningful, applicable, and statistically valid conclusions from multivariate (or vector) time series data.The first four chapters discuss the two main pillars of the subject that have been developed over the last 60 years: vector autoregressive modeling and multivariate spectral analysis. These chapters provide the foundational mater

  8. A Review of Subsequence Time Series Clustering

    Directory of Open Access Journals (Sweden)

    Seyedjamal Zolhavarieh

    2014-01-01

    Full Text Available Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.

  9. A review of subsequence time series clustering.

    Science.gov (United States)

    Zolhavarieh, Seyedjamal; Aghabozorgi, Saeed; Teh, Ying Wah

    2014-01-01

    Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.

  10. A Review of Subsequence Time Series Clustering

    Science.gov (United States)

    Teh, Ying Wah

    2014-01-01

    Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies. PMID:25140332

  11. Analysis of Heavy-Tailed Time Series

    DEFF Research Database (Denmark)

    Xie, Xiaolei

    This thesis is about analysis of heavy-tailed time series. We discuss tail properties of real-world equity return series and investigate the possibility that a single tail index is shared by all return series of actively traded equities in a market. Conditions for this hypothesis to be true...... are identified. We study the eigenvalues and eigenvectors of sample covariance and sample auto-covariance matrices of multivariate heavy-tailed time series, and particularly for time series with very high dimensions. Asymptotic approximations of the eigenvalues and eigenvectors of such matrices are found...... and expressed in terms of the parameters of the dependence structure, among others. Furthermore, we study an importance sampling method for estimating rare-event probabilities of multivariate heavy-tailed time series generated by matrix recursion. We show that the proposed algorithm is efficient in the sense...

  12. LIDAR TS for ITER core plasma. Part II: simultaneous two wavelength LIDAR TS

    Science.gov (United States)

    Gowers, C.; Nielsen, P.; Salzmann, H.

    2017-12-01

    We have shown recently, and in more detail at this conference (Salzmann et al) that the LIDAR approach to ITER core TS measurements requires only two mirrors in the inaccessible port plug area of the machine. This leads to simplified and robust alignment, lower risk of mirror damage by plasma contamination and much simpler calibration, compared with the awkward and vulnerable optical geometry of the conventional imaging TS approach, currently under development by ITER. In the present work we have extended the simulation code used previously to include the case of launching two laser pulses, of different wavelengths, simultaneously in LIDAR geometry. The aim of this approach is to broaden the choice of lasers available for the diagnostic. In the simulation code it is assumed that two short duration (300 ps) laser pulses of different wavelengths, from an Nd:YAG laser are launched through the plasma simultaneously. The temperature and density profiles are deduced in the usual way but from the resulting combined scattered signals in the different spectral channels of the single spectrometer. The spectral response and quantum efficiencies of the detectors used in the simulation are taken from catalogue data for commercially available Hamamatsu MCP-PMTs. The response times, gateability and tolerance to stray light levels of this type of photomultiplier have already been demonstrated in the JET LIDAR system and give sufficient spatial resolution to meet the ITER specification. Here we present the new simulation results from the code. They demonstrate that when the detectors are combined with this two laser, LIDAR approach, the full range of the specified ITER core plasma Te and ne can be measured with sufficient accuracy. So, with commercially available detectors and a simple modification of a Nd:YAG laser similar to that currently being used in the design of the conventional ITER core TS design mentioned above, the ITER requirements can be met.

  13. Adaptive time-variant models for fuzzy-time-series forecasting.

    Science.gov (United States)

    Wong, Wai-Keung; Bai, Enjian; Chu, Alice Wai-Ching

    2010-12-01

    A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, and other domains. Related studies mainly focus on three factors, namely, the partition of discourse, the content of forecasting rules, and the methods of defuzzification, all of which greatly influence the prediction accuracy of forecasting models. These studies use fixed analysis window sizes for forecasting. In this paper, an adaptive time-variant fuzzy-time-series forecasting model (ATVF) is proposed to improve forecasting accuracy. The proposed model automatically adapts the analysis window size of fuzzy time series based on the prediction accuracy in the training phase and uses heuristic rules to generate forecasting values in the testing phase. The performance of the ATVF model is tested using both simulated and actual time series including the enrollments at the University of Alabama, Tuscaloosa, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The experiment results show that the proposed ATVF model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series forecasting models.

  14. Time Series Analysis and Forecasting by Example

    CERN Document Server

    Bisgaard, Soren

    2011-01-01

    An intuition-based approach enables you to master time series analysis with ease Time Series Analysis and Forecasting by Example provides the fundamental techniques in time series analysis using various examples. By introducing necessary theory through examples that showcase the discussed topics, the authors successfully help readers develop an intuitive understanding of seemingly complicated time series models and their implications. The book presents methodologies for time series analysis in a simplified, example-based approach. Using graphics, the authors discuss each presented example in

  15. Time series with tailored nonlinearities

    Science.gov (United States)

    Räth, C.; Laut, I.

    2015-10-01

    It is demonstrated how to generate time series with tailored nonlinearities by inducing well-defined constraints on the Fourier phases. Correlations between the phase information of adjacent phases and (static and dynamic) measures of nonlinearities are established and their origin is explained. By applying a set of simple constraints on the phases of an originally linear and uncorrelated Gaussian time series, the observed scaling behavior of the intensity distribution of empirical time series can be reproduced. The power law character of the intensity distributions being typical for, e.g., turbulence and financial data can thus be explained in terms of phase correlations.

  16. Clustering of financial time series

    Science.gov (United States)

    D'Urso, Pierpaolo; Cappelli, Carmela; Di Lallo, Dario; Massari, Riccardo

    2013-05-01

    This paper addresses the topic of classifying financial time series in a fuzzy framework proposing two fuzzy clustering models both based on GARCH models. In general clustering of financial time series, due to their peculiar features, needs the definition of suitable distance measures. At this aim, the first fuzzy clustering model exploits the autoregressive representation of GARCH models and employs, in the framework of a partitioning around medoids algorithm, the classical autoregressive metric. The second fuzzy clustering model, also based on partitioning around medoids algorithm, uses the Caiado distance, a Mahalanobis-like distance, based on estimated GARCH parameters and covariances that takes into account the information about the volatility structure of time series. In order to illustrate the merits of the proposed fuzzy approaches an application to the problem of classifying 29 time series of Euro exchange rates against international currencies is presented and discussed, also comparing the fuzzy models with their crisp version.

  17. Comparative genomic analysis reveals a novel mitochondrial isoform of human rTS protein and unusual phylogenetic distribution of the rTS gene

    Directory of Open Access Journals (Sweden)

    McGuire John J

    2005-09-01

    Full Text Available Abstract Background The rTS gene (ENOSF1, first identified in Homo sapiens as a gene complementary to the thymidylate synthase (TYMS mRNA, is known to encode two protein isoforms, rTSα and rTSβ. The rTSβ isoform appears to be an enzyme responsible for the synthesis of signaling molecules involved in the down-regulation of thymidylate synthase, but the exact cellular functions of rTS genes are largely unknown. Results Through comparative genomic sequence analysis, we predicted the existence of a novel protein isoform, rTS, which has a 27 residue longer N-terminus by virtue of utilizing an alternative start codon located upstream of the start codon in rTSβ. We observed that a similar extended N-terminus could be predicted in all rTS genes for which genomic sequences are available and the extended regions are conserved from bacteria to human. Therefore, we reasoned that the protein with the extended N-terminus might represent an ancestral form of the rTS protein. Sequence analysis strongly predicts a mitochondrial signal sequence in the extended N-terminal of human rTSγ, which is absent in rTSβ. We confirmed the existence of rTS in human mitochondria experimentally by demonstrating the presence of both rTSγ and rTSβ proteins in mitochondria isolated by subcellular fractionation. In addition, our comprehensive analysis of rTS orthologous sequences reveals an unusual phylogenetic distribution of this gene, which suggests the occurrence of one or more horizontal gene transfer events. Conclusion The presence of two rTS isoforms in mitochondria suggests that the rTS signaling pathway may be active within mitochondria. Our report also presents an example of identifying novel protein isoforms and for improving gene annotation through comparative genomic analysis.

  18. Comparative genomic analysis reveals a novel mitochondrial isoform of human rTS protein and unusual phylogenetic distribution of the rTS gene

    Science.gov (United States)

    Liang, Ping; Nair, Jayakumar R; Song, Lei; McGuire, John J; Dolnick, Bruce J

    2005-01-01

    Background The rTS gene (ENOSF1), first identified in Homo sapiens as a gene complementary to the thymidylate synthase (TYMS) mRNA, is known to encode two protein isoforms, rTSα and rTSβ. The rTSβ isoform appears to be an enzyme responsible for the synthesis of signaling molecules involved in the down-regulation of thymidylate synthase, but the exact cellular functions of rTS genes are largely unknown. Results Through comparative genomic sequence analysis, we predicted the existence of a novel protein isoform, rTS, which has a 27 residue longer N-terminus by virtue of utilizing an alternative start codon located upstream of the start codon in rTSβ. We observed that a similar extended N-terminus could be predicted in all rTS genes for which genomic sequences are available and the extended regions are conserved from bacteria to human. Therefore, we reasoned that the protein with the extended N-terminus might represent an ancestral form of the rTS protein. Sequence analysis strongly predicts a mitochondrial signal sequence in the extended N-terminal of human rTSγ, which is absent in rTSβ. We confirmed the existence of rTS in human mitochondria experimentally by demonstrating the presence of both rTSγ and rTSβ proteins in mitochondria isolated by subcellular fractionation. In addition, our comprehensive analysis of rTS orthologous sequences reveals an unusual phylogenetic distribution of this gene, which suggests the occurrence of one or more horizontal gene transfer events. Conclusion The presence of two rTS isoforms in mitochondria suggests that the rTS signaling pathway may be active within mitochondria. Our report also presents an example of identifying novel protein isoforms and for improving gene annotation through comparative genomic analysis. PMID:16162288

  19. Improved Stabilization Conditions for Nonlinear Systems with Input and State Delays via T-S Fuzzy Model

    Directory of Open Access Journals (Sweden)

    Chang Che

    2018-01-01

    Full Text Available This paper focuses on the problem of nonlinear systems with input and state delays. The considered nonlinear systems are represented by Takagi-Sugeno (T-S fuzzy model. A new state feedback control approach is introduced for T-S fuzzy systems with input delay and state delays. A new Lyapunov-Krasovskii functional is employed to derive less conservative stability conditions by incorporating a recently developed Wirtinger-based integral inequality. Based on the Lyapunov stability criterion, a series of linear matrix inequalities (LMIs are obtained by using the slack variables and integral inequality, which guarantees the asymptotic stability of the closed-loop system. Several numerical examples are given to show the advantages of the proposed results.

  20. Data Mining Smart Energy Time Series

    Directory of Open Access Journals (Sweden)

    Janina POPEANGA

    2015-07-01

    Full Text Available With the advent of smart metering technology the amount of energy data will increase significantly and utilities industry will have to face another big challenge - to find relationships within time-series data and even more - to analyze such huge numbers of time series to find useful patterns and trends with fast or even real-time response. This study makes a small review of the literature in the field, trying to demonstrate how essential is the application of data mining techniques in the time series to make the best use of this large quantity of data, despite all the difficulties. Also, the most important Time Series Data Mining techniques are presented, highlighting their applicability in the energy domain.

  1. Academic Training: Real Time Process Control - Lecture series

    CERN Multimedia

    Françoise Benz

    2004-01-01

    ACADEMIC TRAINING LECTURE REGULAR PROGRAMME 7, 8 and 9 June From 11:00 hrs to 12:00 hrs - Main Auditorium bldg. 500 Real Time Process Control T. Riesco / CERN-TS What exactly is meant by Real-time? There are several definitions of real-time, most of them contradictory. Unfortunately the topic is controversial, and there does not seem to be 100% agreement over the terminology. Real-time applications are becoming increasingly important in our daily lives and can be found in diverse environments such as the automatic braking system on an automobile, a lottery ticket system, or robotic environmental samplers on a space station. These lectures will introduce concepts and theory like basic concepts timing constraints, task scheduling, periodic server mechanisms, hard and soft real-time.ENSEIGNEMENT ACADEMIQUE ACADEMIC TRAINING Françoise Benz 73127 academic.training@cern.ch

  2. Predicting chaotic time series

    International Nuclear Information System (INIS)

    Farmer, J.D.; Sidorowich, J.J.

    1987-01-01

    We present a forecasting technique for chaotic data. After embedding a time series in a state space using delay coordinates, we ''learn'' the induced nonlinear mapping using local approximation. This allows us to make short-term predictions of the future behavior of a time series, using information based only on past values. We present an error estimate for this technique, and demonstrate its effectiveness by applying it to several examples, including data from the Mackey-Glass delay differential equation, Rayleigh-Benard convection, and Taylor-Couette flow

  3. Measuring multiscaling in financial time-series

    International Nuclear Information System (INIS)

    Buonocore, R.J.; Aste, T.; Di Matteo, T.

    2016-01-01

    We discuss the origin of multiscaling in financial time-series and investigate how to best quantify it. Our methodology consists in separating the different sources of measured multifractality by analyzing the multi/uni-scaling behavior of synthetic time-series with known properties. We use the results from the synthetic time-series to interpret the measure of multifractality of real log-returns time-series. The main finding is that the aggregation horizon of the returns can introduce a strong bias effect on the measure of multifractality. This effect can become especially important when returns distributions have power law tails with exponents in the range (2, 5). We discuss the right aggregation horizon to mitigate this bias.

  4. Time averaging, ageing and delay analysis of financial time series

    Science.gov (United States)

    Cherstvy, Andrey G.; Vinod, Deepak; Aghion, Erez; Chechkin, Aleksei V.; Metzler, Ralf

    2017-06-01

    We introduce three strategies for the analysis of financial time series based on time averaged observables. These comprise the time averaged mean squared displacement (MSD) as well as the ageing and delay time methods for varying fractions of the financial time series. We explore these concepts via statistical analysis of historic time series for several Dow Jones Industrial indices for the period from the 1960s to 2015. Remarkably, we discover a simple universal law for the delay time averaged MSD. The observed features of the financial time series dynamics agree well with our analytical results for the time averaged measurables for geometric Brownian motion, underlying the famed Black-Scholes-Merton model. The concepts we promote here are shown to be useful for financial data analysis and enable one to unveil new universal features of stock market dynamics.

  5. Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection

    Science.gov (United States)

    Cabrera-Vives, Guillermo; Reyes, Ignacio; Förster, Francisco; Estévez, Pablo A.; Maureira, Juan-Carlos

    2017-02-01

    We introduce Deep-HiTS, a rotation-invariant convolutional neural network (CNN) model for classifying images of transient candidates into artifacts or real sources for the High cadence Transient Survey (HiTS). CNNs have the advantage of learning the features automatically from the data while achieving high performance. We compare our CNN model against a feature engineering approach using random forests (RFs). We show that our CNN significantly outperforms the RF model, reducing the error by almost half. Furthermore, for a fixed number of approximately 2000 allowed false transient candidates per night, we are able to reduce the misclassified real transients by approximately one-fifth. To the best of our knowledge, this is the first time CNNs have been used to detect astronomical transient events. Our approach will be very useful when processing images from next generation instruments such as the Large Synoptic Survey Telescope. We have made all our code and data available to the community for the sake of allowing further developments and comparisons at https://github.com/guille-c/Deep-HiTS. Deep-HiTS is licensed under the terms of the GNU General Public License v3.0.

  6. Applied time series analysis

    CERN Document Server

    Woodward, Wayne A; Elliott, Alan C

    2011-01-01

    ""There is scarcely a standard technique that the reader will find left out … this book is highly recommended for those requiring a ready introduction to applicable methods in time series and serves as a useful resource for pedagogical purposes.""-International Statistical Review (2014), 82""Current time series theory for practice is well summarized in this book.""-Emmanuel Parzen, Texas A&M University""What an extraordinary range of topics covered, all very insightfully. I like [the authors'] innovations very much, such as the AR factor table.""-David Findley, U.S. Census Bureau (retired)""…

  7. Entropic Analysis of Electromyography Time Series

    Science.gov (United States)

    Kaufman, Miron; Sung, Paul

    2005-03-01

    We are in the process of assessing the effectiveness of fractal and entropic measures for the diagnostic of low back pain from surface electromyography (EMG) time series. Surface electromyography (EMG) is used to assess patients with low back pain. In a typical EMG measurement, the voltage is measured every millisecond. We observed back muscle fatiguing during one minute, which results in a time series with 60,000 entries. We characterize the complexity of time series by computing the Shannon entropy time dependence. The analysis of the time series from different relevant muscles from healthy and low back pain (LBP) individuals provides evidence that the level of variability of back muscle activities is much larger for healthy individuals than for individuals with LBP. In general the time dependence of the entropy shows a crossover from a diffusive regime to a regime characterized by long time correlations (self organization) at about 0.01s.

  8. Quantifying memory in complex physiological time-series.

    Science.gov (United States)

    Shirazi, Amir H; Raoufy, Mohammad R; Ebadi, Haleh; De Rui, Michele; Schiff, Sami; Mazloom, Roham; Hajizadeh, Sohrab; Gharibzadeh, Shahriar; Dehpour, Ahmad R; Amodio, Piero; Jafari, G Reza; Montagnese, Sara; Mani, Ali R

    2013-01-01

    In a time-series, memory is a statistical feature that lasts for a period of time and distinguishes the time-series from a random, or memory-less, process. In the present study, the concept of "memory length" was used to define the time period, or scale over which rare events within a physiological time-series do not appear randomly. The method is based on inverse statistical analysis and provides empiric evidence that rare fluctuations in cardio-respiratory time-series are 'forgotten' quickly in healthy subjects while the memory for such events is significantly prolonged in pathological conditions such as asthma (respiratory time-series) and liver cirrhosis (heart-beat time-series). The memory length was significantly higher in patients with uncontrolled asthma compared to healthy volunteers. Likewise, it was significantly higher in patients with decompensated cirrhosis compared to those with compensated cirrhosis and healthy volunteers. We also observed that the cardio-respiratory system has simple low order dynamics and short memory around its average, and high order dynamics around rare fluctuations.

  9. Effective Feature Preprocessing for Time Series Forecasting

    DEFF Research Database (Denmark)

    Zhao, Junhua; Dong, Zhaoyang; Xu, Zhao

    2006-01-01

    Time series forecasting is an important area in data mining research. Feature preprocessing techniques have significant influence on forecasting accuracy, therefore are essential in a forecasting model. Although several feature preprocessing techniques have been applied in time series forecasting...... performance in time series forecasting. It is demonstrated in our experiment that, effective feature preprocessing can significantly enhance forecasting accuracy. This research can be a useful guidance for researchers on effectively selecting feature preprocessing techniques and integrating them with time...... series forecasting models....

  10. Development of an Operational TS Dataset Production System for the Data Assimilation System

    Science.gov (United States)

    Kim, Sung Dae; Park, Hyuk Min; Kim, Young Ho; Park, Kwang Soon

    2017-04-01

    An operational TS (Temperature and Salinity) dataset production system was developed to provide near real-time data to the data assimilation system periodically. It collects the latest 15 days' TS data of the north western pacific area (20°N - 55°N, 110°E - 150°E), applies QC tests to the archived data and supplies them to numerical prediction models of KIOST (Korea Institute of Ocean Science and Technology). The latest real-time TS data are collected from Argo GDAC and GTSPP data server every week. Argo data are downloaded from /latest_data directory of Argo GDAC. Because many duplicated data exist when all profile data are extracted from all Argo netCDF files, DB system is used to avoid duplication. All metadata (float ID, location, observation date and time, etc) of all Argo floats is stored into Database system and a Matlab program was developed to manipulate DB data, to check the duplication and to exclude duplicated data. GTSPP data are downloaded from /realtime directory of GTSPP data service. The latest data except ARGO data are extracted from the original data. Another Matlab program was coded to inspect all collected data using 10 QC tests and produce final dataset which can be used by the assimilation system. Three regional range tests to inspect annual, seasonal and monthly variations are included in the QC procedures. The C program was developed to provide regional ranges to data managers. It can calculate upper limit and lower limit of temperature and salinity at depth from 0 to 1550m. The final TS dataset contains the latest 15 days' TS data in netCDF format. It is updated every week and transmitted to numerical modeler of KIOST for operational use.

  11. Statistical criteria for characterizing irradiance time series.

    Energy Technology Data Exchange (ETDEWEB)

    Stein, Joshua S.; Ellis, Abraham; Hansen, Clifford W.

    2010-10-01

    We propose and examine several statistical criteria for characterizing time series of solar irradiance. Time series of irradiance are used in analyses that seek to quantify the performance of photovoltaic (PV) power systems over time. Time series of irradiance are either measured or are simulated using models. Simulations of irradiance are often calibrated to or generated from statistics for observed irradiance and simulations are validated by comparing the simulation output to the observed irradiance. Criteria used in this comparison should derive from the context of the analyses in which the simulated irradiance is to be used. We examine three statistics that characterize time series and their use as criteria for comparing time series. We demonstrate these statistics using observed irradiance data recorded in August 2007 in Las Vegas, Nevada, and in June 2009 in Albuquerque, New Mexico.

  12. Homogenising time series: beliefs, dogmas and facts

    Science.gov (United States)

    Domonkos, P.

    2011-06-01

    In the recent decades various homogenisation methods have been developed, but the real effects of their application on time series are still not known sufficiently. The ongoing COST action HOME (COST ES0601) is devoted to reveal the real impacts of homogenisation methods more detailed and with higher confidence than earlier. As a part of the COST activity, a benchmark dataset was built whose characteristics approach well the characteristics of real networks of observed time series. This dataset offers much better opportunity than ever before to test the wide variety of homogenisation methods, and analyse the real effects of selected theoretical recommendations. Empirical results show that real observed time series usually include several inhomogeneities of different sizes. Small inhomogeneities often have similar statistical characteristics than natural changes caused by climatic variability, thus the pure application of the classic theory that change-points of observed time series can be found and corrected one-by-one is impossible. However, after homogenisation the linear trends, seasonal changes and long-term fluctuations of time series are usually much closer to the reality than in raw time series. Some problems around detecting multiple structures of inhomogeneities, as well as that of time series comparisons within homogenisation procedures are discussed briefly in the study.

  13. Ocean time-series near Bermuda: Hydrostation S and the US JGOFS Bermuda Atlantic time-series study

    Science.gov (United States)

    Michaels, Anthony F.; Knap, Anthony H.

    1992-01-01

    Bermuda is the site of two ocean time-series programs. At Hydrostation S, the ongoing biweekly profiles of temperature, salinity and oxygen now span 37 years. This is one of the longest open-ocean time-series data sets and provides a view of decadal scale variability in ocean processes. In 1988, the U.S. JGOFS Bermuda Atlantic Time-series Study began a wide range of measurements at a frequency of 14-18 cruises each year to understand temporal variability in ocean biogeochemistry. On each cruise, the data range from chemical analyses of discrete water samples to data from electronic packages of hydrographic and optics sensors. In addition, a range of biological and geochemical rate measurements are conducted that integrate over time-periods of minutes to days. This sampling strategy yields a reasonable resolution of the major seasonal patterns and of decadal scale variability. The Sargasso Sea also has a variety of episodic production events on scales of days to weeks and these are only poorly resolved. In addition, there is a substantial amount of mesoscale variability in this region and some of the perceived temporal patterns are caused by the intersection of the biweekly sampling with the natural spatial variability. In the Bermuda time-series programs, we have added a series of additional cruises to begin to assess these other sources of variation and their impacts on the interpretation of the main time-series record. However, the adequate resolution of higher frequency temporal patterns will probably require the introduction of new sampling strategies and some emerging technologies such as biogeochemical moorings and autonomous underwater vehicles.

  14. Multivariate Time Series Decomposition into Oscillation Components.

    Science.gov (United States)

    Matsuda, Takeru; Komaki, Fumiyasu

    2017-08-01

    Many time series are considered to be a superposition of several oscillation components. We have proposed a method for decomposing univariate time series into oscillation components and estimating their phases (Matsuda & Komaki, 2017 ). In this study, we extend that method to multivariate time series. We assume that several oscillators underlie the given multivariate time series and that each variable corresponds to a superposition of the projections of the oscillators. Thus, the oscillators superpose on each variable with amplitude and phase modulation. Based on this idea, we develop gaussian linear state-space models and use them to decompose the given multivariate time series. The model parameters are estimated from data using the empirical Bayes method, and the number of oscillators is determined using the Akaike information criterion. Therefore, the proposed method extracts underlying oscillators in a data-driven manner and enables investigation of phase dynamics in a given multivariate time series. Numerical results show the effectiveness of the proposed method. From monthly mean north-south sunspot number data, the proposed method reveals an interesting phase relationship.

  15. Forecasting Enrollments with Fuzzy Time Series.

    Science.gov (United States)

    Song, Qiang; Chissom, Brad S.

    The concept of fuzzy time series is introduced and used to forecast the enrollment of a university. Fuzzy time series, an aspect of fuzzy set theory, forecasts enrollment using a first-order time-invariant model. To evaluate the model, the conventional linear regression technique is applied and the predicted values obtained are compared to the…

  16. Forecasting Cryptocurrencies Financial Time Series

    DEFF Research Database (Denmark)

    Catania, Leopoldo; Grassi, Stefano; Ravazzolo, Francesco

    2018-01-01

    This paper studies the predictability of cryptocurrencies time series. We compare several alternative univariate and multivariate models in point and density forecasting of four of the most capitalized series: Bitcoin, Litecoin, Ripple and Ethereum. We apply a set of crypto–predictors and rely...

  17. Forecasting Cryptocurrencies Financial Time Series

    OpenAIRE

    Catania, Leopoldo; Grassi, Stefano; Ravazzolo, Francesco

    2018-01-01

    This paper studies the predictability of cryptocurrencies time series. We compare several alternative univariate and multivariate models in point and density forecasting of four of the most capitalized series: Bitcoin, Litecoin, Ripple and Ethereum. We apply a set of crypto–predictors and rely on Dynamic Model Averaging to combine a large set of univariate Dynamic Linear Models and several multivariate Vector Autoregressive models with different forms of time variation. We find statistical si...

  18. Time series modeling, computation, and inference

    CERN Document Server

    Prado, Raquel

    2010-01-01

    The authors systematically develop a state-of-the-art analysis and modeling of time series. … this book is well organized and well written. The authors present various statistical models for engineers to solve problems in time series analysis. Readers no doubt will learn state-of-the-art techniques from this book.-Hsun-Hsien Chang, Computing Reviews, March 2012My favorite chapters were on dynamic linear models and vector AR and vector ARMA models.-William Seaver, Technometrics, August 2011… a very modern entry to the field of time-series modelling, with a rich reference list of the current lit

  19. Time Series Analysis Forecasting and Control

    CERN Document Server

    Box, George E P; Reinsel, Gregory C

    2011-01-01

    A modernized new edition of one of the most trusted books on time series analysis. Since publication of the first edition in 1970, Time Series Analysis has served as one of the most influential and prominent works on the subject. This new edition maintains its balanced presentation of the tools for modeling and analyzing time series and also introduces the latest developments that have occurred n the field over the past decade through applications from areas such as business, finance, and engineering. The Fourth Edition provides a clearly written exploration of the key methods for building, cl

  20. The TS 600: automatic control system for eddy currents

    International Nuclear Information System (INIS)

    Poulet, J.P.

    1986-10-01

    In the scope of fabrication and in service inspection of the PWR steam generator tubing bendle, FRAMATOME developed an automatic Eddy Current testing system: TS600. Based on a mini-computer, TS600 allows to digitize, to store and to process data in various ways, so it is possible to perform several kinds of inspection: conventional inservice inspection, roll area profilometry...... TS600 can also be used to develop new methods of examination [fr

  1. Costationarity of Locally Stationary Time Series Using costat

    OpenAIRE

    Cardinali, Alessandro; Nason, Guy P.

    2013-01-01

    This article describes the R package costat. This package enables a user to (i) perform a test for time series stationarity; (ii) compute and plot time-localized autocovariances, and (iii) to determine and explore any costationary relationship between two locally stationary time series. Two locally stationary time series are said to be costationary if there exists two time-varying combination functions such that the linear combination of the two series with the functions produces another time...

  2. Detecting nonlinear structure in time series

    International Nuclear Information System (INIS)

    Theiler, J.

    1991-01-01

    We describe an approach for evaluating the statistical significance of evidence for nonlinearity in a time series. The formal application of our method requires the careful statement of a null hypothesis which characterizes a candidate linear process, the generation of an ensemble of ''surrogate'' data sets which are similar to the original time series but consistent with the null hypothesis, and the computation of a discriminating statistic for the original and for each of the surrogate data sets. The idea is to test the original time series against the null hypothesis by checking whether the discriminating statistic computed for the original time series differs significantly from the statistics computed for each of the surrogate sets. While some data sets very cleanly exhibit low-dimensional chaos, there are many cases where the evidence is sketchy and difficult to evaluate. We hope to provide a framework within which such claims of nonlinearity can be evaluated. 5 refs., 4 figs

  3. Introduction to time series and forecasting

    CERN Document Server

    Brockwell, Peter J

    2016-01-01

    This book is aimed at the reader who wishes to gain a working knowledge of time series and forecasting methods as applied to economics, engineering and the natural and social sciences. It assumes knowledge only of basic calculus, matrix algebra and elementary statistics. This third edition contains detailed instructions for the use of the professional version of the Windows-based computer package ITSM2000, now available as a free download from the Springer Extras website. The logic and tools of time series model-building are developed in detail. Numerous exercises are included and the software can be used to analyze and forecast data sets of the user's own choosing. The book can also be used in conjunction with other time series packages such as those included in R. The programs in ITSM2000 however are menu-driven and can be used with minimal investment of time in the computational details. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space mod...

  4. The use of chloroaluminium phthalocyanine tetrasulfonate (AlPcTS) for time-delayed fluorescence imaging

    International Nuclear Information System (INIS)

    Gundy, Sarah; Putten, Wil van der; Shearer, Andy; Buckton, Daniel; Ryder, Alan G; Ball, Michael

    2004-01-01

    Phthalocyanine derivatives are currently under investigation for use in photodynamic therapy, which is a promising cancer treatment. These materials, which display preferential uptake in cancerous cells, also exhibit high fluorescence yields and can be used for tumour detection. Problems with steady-state fluorescence techniques such as excitation scatter and background autofluorescence can be eliminated by using time-resolved imaging techniques without the need for filters. A tissue phantom was assembled to test a constructed time-gated imaging system by drilling 36 wells of varying diameter and depth (10 mm to 1 mm) into a block of polymethyl methacrylate (PMMA). The system was used to record images of chloroaluminium phthalocyanine tetrasulfonate (AlPcTS) at differing concentrations in neat aqueous solvent and cell suspensions within the wells. A mixture of Intralipid (to mimic tissue scatter) and Evan's blue (to mimic tissue absorption) of depths ranging from 1 mm to 10 mm was placed on top of the PMMA block. The ensuing images were analysed using signal-to-noise ratios and contrast-detail curves. The results indicate that the time-gated imaging system can prevent background excitation scatter from distorting the fluorescence signal from a longer-lived photosensitizer without the need for filters

  5. TIME SERIES ANALYSIS USING A UNIQUE MODEL OF TRANSFORMATION

    Directory of Open Access Journals (Sweden)

    Goran Klepac

    2007-12-01

    Full Text Available REFII1 model is an authorial mathematical model for time series data mining. The main purpose of that model is to automate time series analysis, through a unique transformation model of time series. An advantage of this approach of time series analysis is the linkage of different methods for time series analysis, linking traditional data mining tools in time series, and constructing new algorithms for analyzing time series. It is worth mentioning that REFII model is not a closed system, which means that we have a finite set of methods. At first, this is a model for transformation of values of time series, which prepares data used by different sets of methods based on the same model of transformation in a domain of problem space. REFII model gives a new approach in time series analysis based on a unique model of transformation, which is a base for all kind of time series analysis. The advantage of REFII model is its possible application in many different areas such as finance, medicine, voice recognition, face recognition and text mining.

  6. Frontiers in Time Series and Financial Econometrics

    OpenAIRE

    Ling, S.; McAleer, M.J.; Tong, H.

    2015-01-01

    __Abstract__ Two of the fastest growing frontiers in econometrics and quantitative finance are time series and financial econometrics. Significant theoretical contributions to financial econometrics have been made by experts in statistics, econometrics, mathematics, and time series analysis. The purpose of this special issue of the journal on “Frontiers in Time Series and Financial Econometrics” is to highlight several areas of research by leading academics in which novel methods have contrib...

  7. Scale-dependent intrinsic entropies of complex time series.

    Science.gov (United States)

    Yeh, Jia-Rong; Peng, Chung-Kang; Huang, Norden E

    2016-04-13

    Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal's complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease. © 2016 The Author(s).

  8. Elements of nonlinear time series analysis and forecasting

    CERN Document Server

    De Gooijer, Jan G

    2017-01-01

    This book provides an overview of the current state-of-the-art of nonlinear time series analysis, richly illustrated with examples, pseudocode algorithms and real-world applications. Avoiding a “theorem-proof” format, it shows concrete applications on a variety of empirical time series. The book can be used in graduate courses in nonlinear time series and at the same time also includes interesting material for more advanced readers. Though it is largely self-contained, readers require an understanding of basic linear time series concepts, Markov chains and Monte Carlo simulation methods. The book covers time-domain and frequency-domain methods for the analysis of both univariate and multivariate (vector) time series. It makes a clear distinction between parametric models on the one hand, and semi- and nonparametric models/methods on the other. This offers the reader the option of concentrating exclusively on one of these nonlinear time series analysis methods. To make the book as user friendly as possible...

  9. An Energy-Based Similarity Measure for Time Series

    Directory of Open Access Journals (Sweden)

    Pierre Brunagel

    2007-11-01

    Full Text Available A new similarity measure, called SimilB, for time series analysis, based on the cross-ΨB-energy operator (2004, is introduced. ΨB is a nonlinear measure which quantifies the interaction between two time series. Compared to Euclidean distance (ED or the Pearson correlation coefficient (CC, SimilB includes the temporal information and relative changes of the time series using the first and second derivatives of the time series. SimilB is well suited for both nonstationary and stationary time series and particularly those presenting discontinuities. Some new properties of ΨB are presented. Particularly, we show that ΨB as similarity measure is robust to both scale and time shift. SimilB is illustrated with synthetic time series and an artificial dataset and compared to the CC and the ED measures.

  10. Detecting chaos in irregularly sampled time series.

    Science.gov (United States)

    Kulp, C W

    2013-09-01

    Recently, Wiebe and Virgin [Chaos 22, 013136 (2012)] developed an algorithm which detects chaos by analyzing a time series' power spectrum which is computed using the Discrete Fourier Transform (DFT). Their algorithm, like other time series characterization algorithms, requires that the time series be regularly sampled. Real-world data, however, are often irregularly sampled, thus, making the detection of chaotic behavior difficult or impossible with those methods. In this paper, a characterization algorithm is presented, which effectively detects chaos in irregularly sampled time series. The work presented here is a modification of Wiebe and Virgin's algorithm and uses the Lomb-Scargle Periodogram (LSP) to compute a series' power spectrum instead of the DFT. The DFT is not appropriate for irregularly sampled time series. However, the LSP is capable of computing the frequency content of irregularly sampled data. Furthermore, a new method of analyzing the power spectrum is developed, which can be useful for differentiating between chaotic and non-chaotic behavior. The new characterization algorithm is successfully applied to irregularly sampled data generated by a model as well as data consisting of observations of variable stars.

  11. Building Chaotic Model From Incomplete Time Series

    Science.gov (United States)

    Siek, Michael; Solomatine, Dimitri

    2010-05-01

    This paper presents a number of novel techniques for building a predictive chaotic model from incomplete time series. A predictive chaotic model is built by reconstructing the time-delayed phase space from observed time series and the prediction is made by a global model or adaptive local models based on the dynamical neighbors found in the reconstructed phase space. In general, the building of any data-driven models depends on the completeness and quality of the data itself. However, the completeness of the data availability can not always be guaranteed since the measurement or data transmission is intermittently not working properly due to some reasons. We propose two main solutions dealing with incomplete time series: using imputing and non-imputing methods. For imputing methods, we utilized the interpolation methods (weighted sum of linear interpolations, Bayesian principle component analysis and cubic spline interpolation) and predictive models (neural network, kernel machine, chaotic model) for estimating the missing values. After imputing the missing values, the phase space reconstruction and chaotic model prediction are executed as a standard procedure. For non-imputing methods, we reconstructed the time-delayed phase space from observed time series with missing values. This reconstruction results in non-continuous trajectories. However, the local model prediction can still be made from the other dynamical neighbors reconstructed from non-missing values. We implemented and tested these methods to construct a chaotic model for predicting storm surges at Hoek van Holland as the entrance of Rotterdam Port. The hourly surge time series is available for duration of 1990-1996. For measuring the performance of the proposed methods, a synthetic time series with missing values generated by a particular random variable to the original (complete) time series is utilized. There exist two main performance measures used in this work: (1) error measures between the actual

  12. Multivariate Time Series Search

    Data.gov (United States)

    National Aeronautics and Space Administration — Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical...

  13. Analysing Stable Time Series

    National Research Council Canada - National Science Library

    Adler, Robert

    1997-01-01

    We describe how to take a stable, ARMA, time series through the various stages of model identification, parameter estimation, and diagnostic checking, and accompany the discussion with a goodly number...

  14. Electromagnetic noise spectrum at John TS [transmission station

    International Nuclear Information System (INIS)

    Hatanaka, G.K.

    1992-01-01

    Canadian National (CN) is proposing the development of a commercial office tower on a site directly south of John Transmission Station (TS). CN is concerned about the potential effects of fields originating from John TS and other nearby sources on the operation of sensitive equipment and occupants of the building. Potential equipment and tenants might include data processing equipment and television and radio broadcasting companies. A study was conducted to characterize the severity of the electromagnetic environment at the site in order to address these concerns. Measurements of the electromagnetic spectrum from 100 kHz to 300 MHz were performed from a mobile test facility that features a 5 kW diesel generator and an extendible antenna mast. Peak measurements were made using a selectable measurement time of 0.05 s. It was found that the highest noise levels result from micro-gap discharges under dry weather conditions. The micro-gap discharges are characteristic of defect noise sources associated with substation hardware. At 0.5 MHz the noise levels are typical of median noise levels expected for the International Radio Consultive Committee (CCIR) defined business environment. At 74 MHz the noise levels are more severe than the expected levels for this type of environment. However, levels more representative of the business environment will be achieved by eliminating the micro-gap noise sources attributed to John TS. 6 refs., 11 figs., 2 tabs

  15. Neural Network Models for Time Series Forecasts

    OpenAIRE

    Tim Hill; Marcus O'Connor; William Remus

    1996-01-01

    Neural networks have been advocated as an alternative to traditional statistical forecasting methods. In the present experiment, time series forecasts produced by neural networks are compared with forecasts from six statistical time series methods generated in a major forecasting competition (Makridakis et al. [Makridakis, S., A. Anderson, R. Carbone, R. Fildes, M. Hibon, R. Lewandowski, J. Newton, E. Parzen, R. Winkler. 1982. The accuracy of extrapolation (time series) methods: Results of a ...

  16. Synthesis of mesoporous TS-1 using a hybrid SiO2–TiO2 xerogel for catalytic oxidative desulfurization

    International Nuclear Information System (INIS)

    Yang, Seung-Tae; Jeong, Kwang-Eun; Jeong, Soon-Yong; Ahn, Wha-Seung

    2012-01-01

    Graphical abstract: Display Omitted Highlights: ► Meso-TS-1 catalyst was synthesized using a SiO 2 –TiO 2 xerogel with an organosilane precursor. ► Hierarchical pore structure was confirmed by characterization of the materials. ► Catalytic activity was tested using oxidative desulfurization of the model sulfur compounds. ► Meso-TS-1 demonstrated significantly improved catalytic activity than TS-1. -- Abstract: Mesoporous TS-1 (M-TS-1) was synthesized using a hybrid SiO 2 –TiO 2 xerogel combined with an organosilane precursor. Prepared samples were characterized by XRD, UV–vis spectroscopy, SEM, and N 2 adsorption–desorption measurement. M-TS-1, prepared in 2 days, showed high crystallinity and the best textural properties among the samples. The N 2 adsorption–desorption isotherms of M-TS-1 exhibited a hysteresis loop at pressure higher than P/P 0 = 0.4, clearly indicating the existence of mesopores. M-TS-1 has significantly larger mesopore volume (0.48 cm 3 /g) than that of conventional TS-1 (0.07 cm 3 /g), and showed a narrow peak centered at ca. 6.3 nm. In the oxidative desulfurization reaction, M-TS-1 was more active than conventional TS-1 at the same Ti-loading; M-TS-1 produced a dibenzothiophene (DBT) conversion of 96%, whereas conventional TS-1 produced a final DBT conversion of 5.6% after a reaction time of 180 min. Oxidative desulfurization over TS-1 was influenced both by electron density and steric hindrance in the sulfur compounds tested.

  17. Geometric noise reduction for multivariate time series.

    Science.gov (United States)

    Mera, M Eugenia; Morán, Manuel

    2006-03-01

    We propose an algorithm for the reduction of observational noise in chaotic multivariate time series. The algorithm is based on a maximum likelihood criterion, and its goal is to reduce the mean distance of the points of the cleaned time series to the attractor. We give evidence of the convergence of the empirical measure associated with the cleaned time series to the underlying invariant measure, implying the possibility to predict the long run behavior of the true dynamics.

  18. BRITS: Bidirectional Recurrent Imputation for Time Series

    OpenAIRE

    Cao, Wei; Wang, Dong; Li, Jian; Zhou, Hao; Li, Lei; Li, Yitan

    2018-01-01

    Time series are widely used as signals in many classification/regression tasks. It is ubiquitous that time series contains many missing values. Given multiple correlated time series data, how to fill in missing values and to predict their class labels? Existing imputation methods often impose strong assumptions of the underlying data generating process, such as linear dynamics in the state space. In this paper, we propose BRITS, a novel method based on recurrent neural networks for missing va...

  19. Efficient Algorithms for Segmentation of Item-Set Time Series

    Science.gov (United States)

    Chundi, Parvathi; Rosenkrantz, Daniel J.

    We propose a special type of time series, which we call an item-set time series, to facilitate the temporal analysis of software version histories, email logs, stock market data, etc. In an item-set time series, each observed data value is a set of discrete items. We formalize the concept of an item-set time series and present efficient algorithms for segmenting a given item-set time series. Segmentation of a time series partitions the time series into a sequence of segments where each segment is constructed by combining consecutive time points of the time series. Each segment is associated with an item set that is computed from the item sets of the time points in that segment, using a function which we call a measure function. We then define a concept called the segment difference, which measures the difference between the item set of a segment and the item sets of the time points in that segment. The segment difference values are required to construct an optimal segmentation of the time series. We describe novel and efficient algorithms to compute segment difference values for each of the measure functions described in the paper. We outline a dynamic programming based scheme to construct an optimal segmentation of the given item-set time series. We use the item-set time series segmentation techniques to analyze the temporal content of three different data sets—Enron email, stock market data, and a synthetic data set. The experimental results show that an optimal segmentation of item-set time series data captures much more temporal content than a segmentation constructed based on the number of time points in each segment, without examining the item set data at the time points, and can be used to analyze different types of temporal data.

  20. Studies on time series applications in environmental sciences

    CERN Document Server

    Bărbulescu, Alina

    2016-01-01

    Time series analysis and modelling represent a large study field, implying the approach from the perspective of the time and frequency, with applications in different domains. Modelling hydro-meteorological time series is difficult due to the characteristics of these series, as long range dependence, spatial dependence, the correlation with other series. Continuous spatial data plays an important role in planning, risk assessment and decision making in environmental management. In this context, in this book we present various statistical tests and modelling techniques used for time series analysis, as well as applications to hydro-meteorological series from Dobrogea, a region situated in the south-eastern part of Romania, less studied till now. Part of the results are accompanied by their R code. .

  1. Global Population Density Grid Time Series Estimates

    Data.gov (United States)

    National Aeronautics and Space Administration — Global Population Density Grid Time Series Estimates provide a back-cast time series of population density grids based on the year 2000 population grid from SEDAC's...

  2. Prediction and Geometry of Chaotic Time Series

    National Research Council Canada - National Science Library

    Leonardi, Mary

    1997-01-01

    This thesis examines the topic of chaotic time series. An overview of chaos, dynamical systems, and traditional approaches to time series analysis is provided, followed by an examination of state space reconstruction...

  3. Sensor-Generated Time Series Events: A Definition Language

    Science.gov (United States)

    Anguera, Aurea; Lara, Juan A.; Lizcano, David; Martínez, Maria Aurora; Pazos, Juan

    2012-01-01

    There are now a great many domains where information is recorded by sensors over a limited time period or on a permanent basis. This data flow leads to sequences of data known as time series. In many domains, like seismography or medicine, time series analysis focuses on particular regions of interest, known as events, whereas the remainder of the time series contains hardly any useful information. In these domains, there is a need for mechanisms to identify and locate such events. In this paper, we propose an events definition language that is general enough to be used to easily and naturally define events in time series recorded by sensors in any domain. The proposed language has been applied to the definition of time series events generated within the branch of medicine dealing with balance-related functions in human beings. A device, called posturograph, is used to study balance-related functions. The platform has four sensors that record the pressure intensity being exerted on the platform, generating four interrelated time series. As opposed to the existing ad hoc proposals, the results confirm that the proposed language is valid, that is generally applicable and accurate, for identifying the events contained in the time series.

  4. Cell cycle phase dependent emergence of thymidylate synthase studied by monoclonal antibody (M-TS-4).

    Science.gov (United States)

    Shibui, S; Hoshino, T; Iwasaki, K; Nomura, K; Jastreboff, M M

    1989-05-01

    A method of identifying thymidylate synthase (TS) at the cellular level was developed using anti-TS monoclonal antibody (M-TS-4), a monoclonal antibody created against purified TS from a HeLa cell line. In HeLa cells and four human glioma cell lines (U-251, U-87, 343-MGA, and SF-188), TS was identified primarily in the cytoplasm. Autoradiographic and flow cytometric studies showed that TS appeared mainly in the G1 phase and subsided early in the S phase; thus, the G1 phase can be divided into TS-positive and -negative fractions. Nuclear TS was not demonstrated unequivocally with M-TS-4, and the relationship between nuclear TS and DNA synthesis could not be determined. Although the percentage of TS-positive cells was larger than the S-phase fraction measured by autoradiography after a pulse of tritiated thymidine or by the immunoperoxidase method using BUdR, the ratios were within a similar range (1.2-1.4) in all cell lines studied. Therefore, the S-phase fraction can be estimated indirectly from the percentage of TS-positive cells measured by M-TS-4. Because the emergence of TS detected by our method is cell cycle dependent, M-TS-4 may be useful for biochemical studies of TS and for cytokinetic analysis.

  5. Correlation and multifractality in climatological time series

    International Nuclear Information System (INIS)

    Pedron, I T

    2010-01-01

    Climate can be described by statistical analysis of mean values of atmospheric variables over a period. It is possible to detect correlations in climatological time series and to classify its behavior. In this work the Hurst exponent, which can characterize correlation and persistence in time series, is obtained by using the Detrended Fluctuation Analysis (DFA) method. Data series of temperature, precipitation, humidity, solar radiation, wind speed, maximum squall, atmospheric pressure and randomic series are studied. Furthermore, the multifractality of such series is analyzed applying the Multifractal Detrended Fluctuation Analysis (MF-DFA) method. The results indicate presence of correlation (persistent character) in all climatological series and multifractality as well. A larger set of data, and longer, could provide better results indicating the universality of the exponents.

  6. Time Series Forecasting with Missing Values

    Directory of Open Access Journals (Sweden)

    Shin-Fu Wu

    2015-11-01

    Full Text Available Time series prediction has become more popular in various kinds of applications such as weather prediction, control engineering, financial analysis, industrial monitoring, etc. To deal with real-world problems, we are often faced with missing values in the data due to sensor malfunctions or human errors. Traditionally, the missing values are simply omitted or replaced by means of imputation methods. However, omitting those missing values may cause temporal discontinuity. Imputation methods, on the other hand, may alter the original time series. In this study, we propose a novel forecasting method based on least squares support vector machine (LSSVM. We employ the input patterns with the temporal information which is defined as local time index (LTI. Time series data as well as local time indexes are fed to LSSVM for doing forecasting without imputation. We compare the forecasting performance of our method with other imputation methods. Experimental results show that the proposed method is promising and is worth further investigations.

  7. Reconstruction of ensembles of coupled time-delay systems from time series.

    Science.gov (United States)

    Sysoev, I V; Prokhorov, M D; Ponomarenko, V I; Bezruchko, B P

    2014-06-01

    We propose a method to recover from time series the parameters of coupled time-delay systems and the architecture of couplings between them. The method is based on a reconstruction of model delay-differential equations and estimation of statistical significance of couplings. It can be applied to networks composed of nonidentical nodes with an arbitrary number of unidirectional and bidirectional couplings. We test our method on chaotic and periodic time series produced by model equations of ensembles of diffusively coupled time-delay systems in the presence of noise, and apply it to experimental time series obtained from electronic oscillators with delayed feedback coupled by resistors.

  8. Stability Analysis and Stabilization of T-S Fuzzy Delta Operator Systems with Time-Varying Delay via an Input-Output Approach

    Directory of Open Access Journals (Sweden)

    Zhixiong Zhong

    2013-01-01

    Full Text Available The stability analysis and stabilization of Takagi-Sugeno (T-S fuzzy delta operator systems with time-varying delay are investigated via an input-output approach. A model transformation method is employed to approximate the time-varying delay. The original system is transformed into a feedback interconnection form which has a forward subsystem with constant delays and a feedback one with uncertainties. By applying the scaled small gain (SSG theorem to deal with this new system, and based on a Lyapunov Krasovskii functional (LKF in delta operator domain, less conservative stability analysis and stabilization conditions are obtained. Numerical examples are provided to illustrate the advantages of the proposed method.

  9. The analysis of time series: an introduction

    National Research Council Canada - National Science Library

    Chatfield, Christopher

    1989-01-01

    .... A variety of practical examples are given to support the theory. The book covers a wide range of time-series topics, including probability models for time series, Box-Jenkins forecasting, spectral analysis, linear systems and system identification...

  10. Time series modeling in traffic safety research.

    Science.gov (United States)

    Lavrenz, Steven M; Vlahogianni, Eleni I; Gkritza, Konstantina; Ke, Yue

    2018-08-01

    The use of statistical models for analyzing traffic safety (crash) data has been well-established. However, time series techniques have traditionally been underrepresented in the corresponding literature, due to challenges in data collection, along with a limited knowledge of proper methodology. In recent years, new types of high-resolution traffic safety data, especially in measuring driver behavior, have made time series modeling techniques an increasingly salient topic of study. Yet there remains a dearth of information to guide analysts in their use. This paper provides an overview of the state of the art in using time series models in traffic safety research, and discusses some of the fundamental techniques and considerations in classic time series modeling. It also presents ongoing and future opportunities for expanding the use of time series models, and explores newer modeling techniques, including computational intelligence models, which hold promise in effectively handling ever-larger data sets. The information contained herein is meant to guide safety researchers in understanding this broad area of transportation data analysis, and provide a framework for understanding safety trends that can influence policy-making. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. Time series prediction: statistical and neural techniques

    Science.gov (United States)

    Zahirniak, Daniel R.; DeSimio, Martin P.

    1996-03-01

    In this paper we compare the performance of nonlinear neural network techniques to those of linear filtering techniques in the prediction of time series. Specifically, we compare the results of using the nonlinear systems, known as multilayer perceptron and radial basis function neural networks, with the results obtained using the conventional linear Wiener filter, Kalman filter and Widrow-Hoff adaptive filter in predicting future values of stationary and non- stationary time series. Our results indicate the performance of each type of system is heavily dependent upon the form of the time series being predicted and the size of the system used. In particular, the linear filters perform adequately for linear or near linear processes while the nonlinear systems perform better for nonlinear processes. Since the linear systems take much less time to be developed, they should be tried prior to using the nonlinear systems when the linearity properties of the time series process are unknown.

  12. Effectiveness of Multivariate Time Series Classification Using Shapelets

    Directory of Open Access Journals (Sweden)

    A. P. Karpenko

    2015-01-01

    Full Text Available Typically, time series classifiers require signal pre-processing (filtering signals from noise and artifact removal, etc., enhancement of signal features (amplitude, frequency, spectrum, etc., classification of signal features in space using the classical techniques and classification algorithms of multivariate data. We consider a method of classifying time series, which does not require enhancement of the signal features. The method uses the shapelets of time series (time series shapelets i.e. small fragments of this series, which reflect properties of one of its classes most of all.Despite the significant number of publications on the theory and shapelet applications for classification of time series, the task to evaluate the effectiveness of this technique remains relevant. An objective of this publication is to study the effectiveness of a number of modifications of the original shapelet method as applied to the multivariate series classification that is a littlestudied problem. The paper presents the problem statement of multivariate time series classification using the shapelets and describes the shapelet–based basic method of binary classification, as well as various generalizations and proposed modification of the method. It also offers the software that implements a modified method and results of computational experiments confirming the effectiveness of the algorithmic and software solutions.The paper shows that the modified method and the software to use it allow us to reach the classification accuracy of about 85%, at best. The shapelet search time increases in proportion to input data dimension.

  13. TS-05: 150 lines of java with high architectural complexity

    DEFF Research Database (Denmark)

    Christensen, Henrik Bærbak

    2005-01-01

    In the short time span available in a software architecture course, it is difficult to find a software system that is both interesting from an architectural perspective and so small that it does not overwhelm the students.We present TS-05 which is a bare 150 line Java "toy-system" that never...

  14. Time-series-analysis techniques applied to nuclear-material accounting

    International Nuclear Information System (INIS)

    Pike, D.H.; Morrison, G.W.; Downing, D.J.

    1982-05-01

    This document is designed to introduce the reader to the applications of Time Series Analysis techniques to Nuclear Material Accountability data. Time series analysis techniques are designed to extract information from a collection of random variables ordered by time by seeking to identify any trends, patterns, or other structure in the series. Since nuclear material accountability data is a time series, one can extract more information using time series analysis techniques than by using other statistical techniques. Specifically, the objective of this document is to examine the applicability of time series analysis techniques to enhance loss detection of special nuclear materials. An introductory section examines the current industry approach which utilizes inventory differences. The error structure of inventory differences is presented. Time series analysis techniques discussed include the Shewhart Control Chart, the Cumulative Summation of Inventory Differences Statistics (CUSUM) and the Kalman Filter and Linear Smoother

  15. Clinical and epidemiological rounds. Time series

    Directory of Open Access Journals (Sweden)

    León-Álvarez, Alba Luz

    2016-07-01

    Full Text Available Analysis of time series is a technique that implicates the study of individuals or groups observed in successive moments in time. This type of analysis allows the study of potential causal relationships between different variables that change over time and relate to each other. It is the most important technique to make inferences about the future, predicting, on the basis or what has happened in the past and it is applied in different disciplines of knowledge. Here we discuss different components of time series, the analysis technique and specific examples in health research.

  16. Integer-valued time series

    NARCIS (Netherlands)

    van den Akker, R.

    2007-01-01

    This thesis adresses statistical problems in econometrics. The first part contributes statistical methodology for nonnegative integer-valued time series. The second part of this thesis discusses semiparametric estimation in copula models and develops semiparametric lower bounds for a large class of

  17. Robust Forecasting of Non-Stationary Time Series

    NARCIS (Netherlands)

    Croux, C.; Fried, R.; Gijbels, I.; Mahieu, K.

    2010-01-01

    This paper proposes a robust forecasting method for non-stationary time series. The time series is modelled using non-parametric heteroscedastic regression, and fitted by a localized MM-estimator, combining high robustness and large efficiency. The proposed method is shown to produce reliable

  18. Tegafur/gimeracil/oteracil (TS-1 induced Stevens–Johnson syndrome: Case report

    Directory of Open Access Journals (Sweden)

    Satoko Minakawa

    2013-09-01

    Full Text Available TS-1 is an oral fluoropyrimidine anticancer drug that contains tegafur, gimeracil, and oteracil. A 78-year-old Japanese male who was diagnosed with carcinoma of the oral floor (rT4aN0M0 was prescribed a standard dose of TS-1 (80 mg/day. On Day 8 after administration of TS-1, an eruption developed. There was erythema, along with vesicles and erosions involving the lip, face, neck, trunk, limbs, and genitals. The drug-induced lymphocyte stimulation test (DLST for TS-1 was negative on the 23rd day, but positive on the 43rd day (20 days after discontinuing prednisolone. The condition was diagnosed as Stevens–Johnson syndrome due to TS-1 because of the clinical course and laboratory results. This case and 24 cases previously reported in the literature were analyzed. The types of drug eruption were drug-related lupus (9 cases, acral erythema (7 cases, scleroderma-like skin lesion (2 cases, Stevens–Johnson syndrome (2 cases, lichenoid eruption (1 case, purpura (1 case, lichen planus (1 case, erythema multiforme (1 case, hypopigmentation (1 case and toxic epidermal necrolysis (1 case, respectively. In view of the increasing usage of TS-1 in several common cancers, clinicians should be aware of drug eruptions due to TS-1.

  19. Characterizing time series via complexity-entropy curves

    Science.gov (United States)

    Ribeiro, Haroldo V.; Jauregui, Max; Zunino, Luciano; Lenzi, Ervin K.

    2017-06-01

    The search for patterns in time series is a very common task when dealing with complex systems. This is usually accomplished by employing a complexity measure such as entropies and fractal dimensions. However, such measures usually only capture a single aspect of the system dynamics. Here, we propose a family of complexity measures for time series based on a generalization of the complexity-entropy causality plane. By replacing the Shannon entropy by a monoparametric entropy (Tsallis q entropy) and after considering the proper generalization of the statistical complexity (q complexity), we build up a parametric curve (the q -complexity-entropy curve) that is used for characterizing and classifying time series. Based on simple exact results and numerical simulations of stochastic processes, we show that these curves can distinguish among different long-range, short-range, and oscillating correlated behaviors. Also, we verify that simulated chaotic and stochastic time series can be distinguished based on whether these curves are open or closed. We further test this technique in experimental scenarios related to chaotic laser intensity, stock price, sunspot, and geomagnetic dynamics, confirming its usefulness. Finally, we prove that these curves enhance the automatic classification of time series with long-range correlations and interbeat intervals of healthy subjects and patients with heart disease.

  20. Complex network approach to fractional time series

    Energy Technology Data Exchange (ETDEWEB)

    Manshour, Pouya [Physics Department, Persian Gulf University, Bushehr 75169 (Iran, Islamic Republic of)

    2015-10-15

    In order to extract correlation information inherited in stochastic time series, the visibility graph algorithm has been recently proposed, by which a time series can be mapped onto a complex network. We demonstrate that the visibility algorithm is not an appropriate one to study the correlation aspects of a time series. We then employ the horizontal visibility algorithm, as a much simpler one, to map fractional processes onto complex networks. The degree distributions are shown to have parabolic exponential forms with Hurst dependent fitting parameter. Further, we take into account other topological properties such as maximum eigenvalue of the adjacency matrix and the degree assortativity, and show that such topological quantities can also be used to predict the Hurst exponent, with an exception for anti-persistent fractional Gaussian noises. To solve this problem, we take into account the Spearman correlation coefficient between nodes' degrees and their corresponding data values in the original time series.

  1. Introduction to time series analysis and forecasting

    CERN Document Server

    Montgomery, Douglas C; Kulahci, Murat

    2008-01-01

    An accessible introduction to the most current thinking in and practicality of forecasting techniques in the context of time-oriented data. Analyzing time-oriented data and forecasting are among the most important problems that analysts face across many fields, ranging from finance and economics to production operations and the natural sciences. As a result, there is a widespread need for large groups of people in a variety of fields to understand the basic concepts of time series analysis and forecasting. Introduction to Time Series Analysis and Forecasting presents the time series analysis branch of applied statistics as the underlying methodology for developing practical forecasts, and it also bridges the gap between theory and practice by equipping readers with the tools needed to analyze time-oriented data and construct useful, short- to medium-term, statistically based forecasts.

  2. Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance.

    Science.gov (United States)

    Liu, Yongli; Chen, Jingli; Wu, Shuai; Liu, Zhizhong; Chao, Hao

    2018-01-01

    Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy.

  3. Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance

    Science.gov (United States)

    Chen, Jingli; Wu, Shuai; Liu, Zhizhong; Chao, Hao

    2018-01-01

    Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy. PMID:29795600

  4. The foundations of modern time series analysis

    CERN Document Server

    Mills, Terence C

    2011-01-01

    This book develops the analysis of Time Series from its formal beginnings in the 1890s through to the publication of Box and Jenkins' watershed publication in 1970, showing how these methods laid the foundations for the modern techniques of Time Series analysis that are in use today.

  5. Time series clustering in large data sets

    Directory of Open Access Journals (Sweden)

    Jiří Fejfar

    2011-01-01

    Full Text Available The clustering of time series is a widely researched area. There are many methods for dealing with this task. We are actually using the Self-organizing map (SOM with the unsupervised learning algorithm for clustering of time series. After the first experiment (Fejfar, Weinlichová, Šťastný, 2009 it seems that the whole concept of the clustering algorithm is correct but that we have to perform time series clustering on much larger dataset to obtain more accurate results and to find the correlation between configured parameters and results more precisely. The second requirement arose in a need for a well-defined evaluation of results. It seems useful to use sound recordings as instances of time series again. There are many recordings to use in digital libraries, many interesting features and patterns can be found in this area. We are searching for recordings with the similar development of information density in this experiment. It can be used for musical form investigation, cover songs detection and many others applications.The objective of the presented paper is to compare clustering results made with different parameters of feature vectors and the SOM itself. We are describing time series in a simplistic way evaluating standard deviations for separated parts of recordings. The resulting feature vectors are clustered with the SOM in batch training mode with different topologies varying from few neurons to large maps.There are other algorithms discussed, usable for finding similarities between time series and finally conclusions for further research are presented. We also present an overview of the related actual literature and projects.

  6. Molecular series-tunneling junctions.

    Science.gov (United States)

    Liao, Kung-Ching; Hsu, Liang-Yan; Bowers, Carleen M; Rabitz, Herschel; Whitesides, George M

    2015-05-13

    Charge transport through junctions consisting of insulating molecular units is a quantum phenomenon that cannot be described adequately by classical circuit laws. This paper explores tunneling current densities in self-assembled monolayer (SAM)-based junctions with the structure Ag(TS)/O2C-R1-R2-H//Ga2O3/EGaIn, where Ag(TS) is template-stripped silver and EGaIn is the eutectic alloy of gallium and indium; R1 and R2 refer to two classes of insulating molecular units-(CH2)n and (C6H4)m-that are connected in series and have different tunneling decay constants in the Simmons equation. These junctions can be analyzed as a form of series-tunneling junctions based on the observation that permuting the order of R1 and R2 in the junction does not alter the overall rate of charge transport. By using the Ag/O2C interface, this system decouples the highest occupied molecular orbital (HOMO, which is localized on the carboxylate group) from strong interactions with the R1 and R2 units. The differences in rates of tunneling are thus determined by the electronic structure of the groups R1 and R2; these differences are not influenced by the order of R1 and R2 in the SAM. In an electrical potential model that rationalizes this observation, R1 and R2 contribute independently to the height of the barrier. This model explicitly assumes that contributions to rates of tunneling from the Ag(TS)/O2C and H//Ga2O3 interfaces are constant across the series examined. The current density of these series-tunneling junctions can be described by J(V) = J0(V) exp(-β1d1 - β2d2), where J(V) is the current density (A/cm(2)) at applied voltage V and βi and di are the parameters describing the attenuation of the tunneling current through a rectangular tunneling barrier, with width d and a height related to the attenuation factor β.

  7. Transmission of linear regression patterns between time series: from relationship in time series to complex networks.

    Science.gov (United States)

    Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

    2014-07-01

    The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

  8. Lag space estimation in time series modelling

    DEFF Research Database (Denmark)

    Goutte, Cyril

    1997-01-01

    The purpose of this article is to investigate some techniques for finding the relevant lag-space, i.e. input information, for time series modelling. This is an important aspect of time series modelling, as it conditions the design of the model through the regressor vector a.k.a. the input layer...

  9. Time-series prediction and applications a machine intelligence approach

    CERN Document Server

    Konar, Amit

    2017-01-01

    This book presents machine learning and type-2 fuzzy sets for the prediction of time-series with a particular focus on business forecasting applications. It also proposes new uncertainty management techniques in an economic time-series using type-2 fuzzy sets for prediction of the time-series at a given time point from its preceding value in fluctuating business environments. It employs machine learning to determine repetitively occurring similar structural patterns in the time-series and uses stochastic automaton to predict the most probabilistic structure at a given partition of the time-series. Such predictions help in determining probabilistic moves in a stock index time-series Primarily written for graduate students and researchers in computer science, the book is equally useful for researchers/professionals in business intelligence and stock index prediction. A background of undergraduate level mathematics is presumed, although not mandatory, for most of the sections. Exercises with tips are provided at...

  10. A Time Series Forecasting Method

    Directory of Open Access Journals (Sweden)

    Wang Zhao-Yu

    2017-01-01

    Full Text Available This paper proposes a novel time series forecasting method based on a weighted self-constructing clustering technique. The weighted self-constructing clustering processes all the data patterns incrementally. If a data pattern is not similar enough to an existing cluster, it forms a new cluster of its own. However, if a data pattern is similar enough to an existing cluster, it is removed from the cluster it currently belongs to and added to the most similar cluster. During the clustering process, weights are learned for each cluster. Given a series of time-stamped data up to time t, we divide it into a set of training patterns. By using the weighted self-constructing clustering, the training patterns are grouped into a set of clusters. To estimate the value at time t + 1, we find the k nearest neighbors of the input pattern and use these k neighbors to decide the estimation. Experimental results are shown to demonstrate the effectiveness of the proposed approach.

  11. CATALYTIC PERFORMANCES OF Fe2O3/TS-1 CATALYST IN PHENOL HYDROXYLATION REACTION

    Directory of Open Access Journals (Sweden)

    Didik Prasetyoko

    2010-07-01

    Full Text Available Hydroxylation reaction of phenol into diphenol, such as hydroquinone and catechol, has a great role in many industrial applications. Phenol hydroxylation reaction can be carried out using Titanium Silicalite-1 (TS-1 as catalyst and H2O2 as an oxidant. TS-1 catalyst shows high activity and selectivity for phenol hydroxylation reaction. However, its hydrophobic sites lead to slow H2O2 adsorption toward the active site of TS-1. Consequently, the reaction rate of phenol hydroxylation reaction is tends to be low. Addition of metal oxide Fe2O3 enhanced hydrophilicity of TS-1 catalyst. Liquid phase catalytic phenol hydroxylation using hydrogen peroxide as oxidant was carried out over iron (III oxide-modified TS-1 catalyst (Fe2O3/TS-1, that were prepared by impregnation method using iron (III nitrate as precursor and characterized by X-ray diffraction, infrared spectroscopy, nitrogen adsorption, pyridine adsorption, and hydrophilicity techniques. Catalysts 1Fe2O3/TS-1 showed maximum catalytic activity of hydroquinone product. In this research, the increase of hydroquinone formation rate is due to the higher hydrophilicity of Fe2O3/TS-1 catalysts compare to the parent catalyst, TS-1.   Keywords: Fe2O3/TS-1, hydrophilic site, phenol hydroxylation

  12. Stochastic nature of series of waiting times

    Science.gov (United States)

    Anvari, Mehrnaz; Aghamohammadi, Cina; Dashti-Naserabadi, H.; Salehi, E.; Behjat, E.; Qorbani, M.; Khazaei Nezhad, M.; Zirak, M.; Hadjihosseini, Ali; Peinke, Joachim; Tabar, M. Reza Rahimi

    2013-06-01

    Although fluctuations in the waiting time series have been studied for a long time, some important issues such as its long-range memory and its stochastic features in the presence of nonstationarity have so far remained unstudied. Here we find that the “waiting times” series for a given increment level have long-range correlations with Hurst exponents belonging to the interval 1/2time distribution. We find that the logarithmic difference of waiting times series has a short-range correlation, and then we study its stochastic nature using the Markovian method and determine the corresponding Kramers-Moyal coefficients. As an example, we analyze the velocity fluctuations in high Reynolds number turbulence and determine the level dependence of Markov time scales, as well as the drift and diffusion coefficients. We show that the waiting time distributions exhibit power law tails, and we were able to model the distribution with a continuous time random walk.

  13. Efficient Approximate OLAP Querying Over Time Series

    DEFF Research Database (Denmark)

    Perera, Kasun Baruhupolage Don Kasun Sanjeewa; Hahmann, Martin; Lehner, Wolfgang

    2016-01-01

    The ongoing trend for data gathering not only produces larger volumes of data, but also increases the variety of recorded data types. Out of these, especially time series, e.g. various sensor readings, have attracted attention in the domains of business intelligence and decision making. As OLAP...... queries play a major role in these domains, it is desirable to also execute them on time series data. While this is not a problem on the conceptual level, it can become a bottleneck with regards to query run-time. In general, processing OLAP queries gets more computationally intensive as the volume...... of data grows. This is a particular problem when querying time series data, which generally contains multiple measures recorded at fine time granularities. Usually, this issue is addressed either by scaling up hardware or by employing workload based query optimization techniques. However, these solutions...

  14. A Dynamic Fuzzy Cluster Algorithm for Time Series

    Directory of Open Access Journals (Sweden)

    Min Ji

    2013-01-01

    clustering time series by introducing the definition of key point and improving FCM algorithm. The proposed algorithm works by determining those time series whose class labels are vague and further partitions them into different clusters over time. The main advantage of this approach compared with other existing algorithms is that the property of some time series belonging to different clusters over time can be partially revealed. Results from simulation-based experiments on geographical data demonstrate the excellent performance and the desired results have been obtained. The proposed algorithm can be applied to solve other clustering problems in data mining.

  15. A novel weight determination method for time series data aggregation

    Science.gov (United States)

    Xu, Paiheng; Zhang, Rong; Deng, Yong

    2017-09-01

    Aggregation in time series is of great importance in time series smoothing, predicting and other time series analysis process, which makes it crucial to address the weights in times series correctly and reasonably. In this paper, a novel method to obtain the weights in time series is proposed, in which we adopt induced ordered weighted aggregation (IOWA) operator and visibility graph averaging (VGA) operator and linearly combine the weights separately generated by the two operator. The IOWA operator is introduced to the weight determination of time series, through which the time decay factor is taken into consideration. The VGA operator is able to generate weights with respect to the degree distribution in the visibility graph constructed from the corresponding time series, which reflects the relative importance of vertices in time series. The proposed method is applied to two practical datasets to illustrate its merits. The aggregation of Construction Cost Index (CCI) demonstrates the ability of proposed method to smooth time series, while the aggregation of The Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) illustrate how proposed method maintain the variation tendency of original data.

  16. Foundations of Sequence-to-Sequence Modeling for Time Series

    OpenAIRE

    Kuznetsov, Vitaly; Mariet, Zelda

    2018-01-01

    The availability of large amounts of time series data, paired with the performance of deep-learning algorithms on a broad class of problems, has recently led to significant interest in the use of sequence-to-sequence models for time series forecasting. We provide the first theoretical analysis of this time series forecasting framework. We include a comparison of sequence-to-sequence modeling to classical time series models, and as such our theory can serve as a quantitative guide for practiti...

  17. Synthesis of mesoporous TS-1 using a hybrid SiO{sub 2}–TiO{sub 2} xerogel for catalytic oxidative desulfurization

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Seung-Tae [Department of Chemical Engineering, Inha University, Incheon 402-751 (Korea, Republic of); Jeong, Kwang-Eun; Jeong, Soon-Yong [Research Center for Green Catalysis, Division of Green Chemistry and Engineering Research, Korea Research Institute of Chemical Technology, P.O. Box 107, 141 Gajeong-ro, Yuseong, Daejeon 305-600 (Korea, Republic of); Ahn, Wha-Seung, E-mail: whasahn@inha.ac.kr [Department of Chemical Engineering, Inha University, Incheon 402-751 (Korea, Republic of)

    2012-12-15

    Graphical abstract: Display Omitted Highlights: ► Meso-TS-1 catalyst was synthesized using a SiO{sub 2}–TiO{sub 2} xerogel with an organosilane precursor. ► Hierarchical pore structure was confirmed by characterization of the materials. ► Catalytic activity was tested using oxidative desulfurization of the model sulfur compounds. ► Meso-TS-1 demonstrated significantly improved catalytic activity than TS-1. -- Abstract: Mesoporous TS-1 (M-TS-1) was synthesized using a hybrid SiO{sub 2}–TiO{sub 2} xerogel combined with an organosilane precursor. Prepared samples were characterized by XRD, UV–vis spectroscopy, SEM, and N{sub 2} adsorption–desorption measurement. M-TS-1, prepared in 2 days, showed high crystallinity and the best textural properties among the samples. The N{sub 2} adsorption–desorption isotherms of M-TS-1 exhibited a hysteresis loop at pressure higher than P/P{sub 0} = 0.4, clearly indicating the existence of mesopores. M-TS-1 has significantly larger mesopore volume (0.48 cm{sup 3}/g) than that of conventional TS-1 (0.07 cm{sup 3}/g), and showed a narrow peak centered at ca. 6.3 nm. In the oxidative desulfurization reaction, M-TS-1 was more active than conventional TS-1 at the same Ti-loading; M-TS-1 produced a dibenzothiophene (DBT) conversion of 96%, whereas conventional TS-1 produced a final DBT conversion of 5.6% after a reaction time of 180 min. Oxidative desulfurization over TS-1 was influenced both by electron density and steric hindrance in the sulfur compounds tested.

  18. Climate Prediction Center (CPC) Global Precipitation Time Series

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The global precipitation time series provides time series charts showing observations of daily precipitation as well as accumulated precipitation compared to normal...

  19. Climate Prediction Center (CPC) Global Temperature Time Series

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The global temperature time series provides time series charts using station based observations of daily temperature. These charts provide information about the...

  20. Not in Traffic Surveillance (NiTS)

    Data.gov (United States)

    Department of Transportation — The Not-in-Traffic Surveillance (NiTS) system is a virtual data collection system designed to provide counts and details regarding fatalities and injuries that occur...

  1. Recurrent Neural Network Applications for Astronomical Time Series

    Science.gov (United States)

    Protopapas, Pavlos

    2017-06-01

    The benefits of good predictive models in astronomy lie in early event prediction systems and effective resource allocation. Current time series methods applicable to regular time series have not evolved to generalize for irregular time series. In this talk, I will describe two Recurrent Neural Network methods, Long Short-Term Memory (LSTM) and Echo State Networks (ESNs) for predicting irregular time series. Feature engineering along with a non-linear modeling proved to be an effective predictor. For noisy time series, the prediction is improved by training the network on error realizations using the error estimates from astronomical light curves. In addition to this, we propose a new neural network architecture to remove correlation from the residuals in order to improve prediction and compensate for the noisy data. Finally, I show how to set hyperparameters for a stable and performant solution correctly. In this work, we circumvent this obstacle by optimizing ESN hyperparameters using Bayesian optimization with Gaussian Process priors. This automates the tuning procedure, enabling users to employ the power of RNN without needing an in-depth understanding of the tuning procedure.

  2. T.S. Eliot i nordisk efterkrigstidsdigtning

    DEFF Research Database (Denmark)

    Larsen, Peter Stein

    2009-01-01

    andet indkredses den særlige fascinationskraft ved Eliots æstetik, som den kommer til udtryk i The Waste Land (1922), nemlig at der er tale om en uhyre sofistikeret brug af montageformen. For det tredje peges der gennem et par digtlæsninger på, hvordan Eliots montage-æstetik kan følges i nordisk poesi......Hvad er årsagen til, at T.S. Eliot måske mere end nogen anden digter har haft indflydelse på nordiske digtere i det 20. århundrede? Dette vil blive undersøgt i tre tempi i denne artikel. For det første gives der et generelt rids af, hvor man finder T.S. Eliots aftryk i nordisk litteratur. For det...

  3. Reconstitution of Qbeta RNA replicase from a covalently bonded elongation factor Tu-Ts complex

    DEFF Research Database (Denmark)

    Brown, S; Blumenthal, T

    1976-01-01

    of these polypeptides, protein synthesis elongation factors EF-Tu and EF-Ts, can be covalently crosslinked with dimethyl suberimidate to form a complex which lacks the ability to catalyze the known host functions catalyzed by the individual elongation factors. Using a previously developed reconstitution system we have...... examined the effects of crosslinking the EF-Tu-Ts complex on reconstituted replicase activity. Renaturation is significantly more efficient when exogenously added native EF-Tu-Ts is crosslinked than when it is not. Crosslinked EF-Tu-Ts can be purified from a crude crosslinked postribosomal supernatant...... by its ability to replace EF-Tu and EF-Ts in the renaturation of denatured Qbeta replicase. A sample of Qbeta replicase with crosslinked EF-Tu-Ts replacing the individual elongation factors was prepared. Although it lacked EF-Tu and EF-Ts activities, it could initiate transcription of both poly...

  4. Transition Icons for Time-Series Visualization and Exploratory Analysis.

    Science.gov (United States)

    Nickerson, Paul V; Baharloo, Raheleh; Wanigatunga, Amal A; Manini, Todd M; Tighe, Patrick J; Rashidi, Parisa

    2018-03-01

    The modern healthcare landscape has seen the rapid emergence of techniques and devices that temporally monitor and record physiological signals. The prevalence of time-series data within the healthcare field necessitates the development of methods that can analyze the data in order to draw meaningful conclusions. Time-series behavior is notoriously difficult to intuitively understand due to its intrinsic high-dimensionality, which is compounded in the case of analyzing groups of time series collected from different patients. Our framework, which we call transition icons, renders common patterns in a visual format useful for understanding the shared behavior within groups of time series. Transition icons are adept at detecting and displaying subtle differences and similarities, e.g., between measurements taken from patients receiving different treatment strategies or stratified by demographics. We introduce various methods that collectively allow for exploratory analysis of groups of time series, while being free of distribution assumptions and including simple heuristics for parameter determination. Our technique extracts discrete transition patterns from symbolic aggregate approXimation representations, and compiles transition frequencies into a bag of patterns constructed for each group. These transition frequencies are normalized and aligned in icon form to intuitively display the underlying patterns. We demonstrate the transition icon technique for two time-series datasets-postoperative pain scores, and hip-worn accelerometer activity counts. We believe transition icons can be an important tool for researchers approaching time-series data, as they give rich and intuitive information about collective time-series behaviors.

  5. Biochemical characterization of the triticale TsPAP1, a new type of plant prolyl aminopeptidase, and its impact on proline content and flowering time in transgenic Arabidopsis plants.

    Science.gov (United States)

    Zdunek-Zastocka, Edyta; Grabowska, Agnieszka; Branicki, Tomasz; Michniewska, Beata

    2017-07-01

    Proline aminopeptidase (PAP, EC 3.4.11.5) is the only enzyme that effectively releases proline from the N-termini of peptides. The amino acid sequence of the PAP from Triticosecale, TsPAP1, comprises conserved regions, characteristic of the monomeric forms of PAP found in bacteria but not yet identified in plants. Therefore, we aimed to obtain and biochemically characterize the TsPAP1 protein. The recombinant TsPAP1 protein was received through heterologous expression of the TsPAP1 coding sequence in a bacterial expression system and purified with affinity chromatography. Gel filtration chromatography and SDS electrophoresis revealed that TsPAP1 is a monomer with a molecular mass of 37.5 kDa. TsPAP1 prefers substrates with proline at the N-terminus but is also capable of hydrolyzing β-naphthylamides of hydroxyproline and alanine. Among the peptides tested, the most preferred were di- and tripeptides, especially those with glycine in the Y position. The use of diagnostic inhibitors indicated that TsPAP1 is a serine peptidase; however, further characterization revealed that the SH residues are also important for maintaining its activity. To examine the role of TsPAP1 under physiological conditions, we developed transgenic Arabidopsis plants overexpressing TsPAP1. Compared with wild-type plants, the transgenic lines accumulated more proline, flowered an average of 3.5 days earlier, and developed more siliques than did untransformed controls. Our paper is the first to describe the biochemical properties of a novel monomeric plant PAP and contributes to the functional characterization of PAP proteins in plants. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

  6. Study of the Integration of the CNU-TS-1 Mobile Tunnel Monitoring System.

    Science.gov (United States)

    Du, Liming; Zhong, Ruofei; Sun, Haili; Zhu, Qiang; Zhang, Zhen

    2018-02-01

    A rapid, precise and automated means for the regular inspection and maintenance of a large number of tunnels is needed. Based on the depth study of the tunnel monitoring method, the CNU-TS-1 mobile tunnel monitoring system (TS1) is developed and presented. It can efficiently obtain the cross-sections that are orthogonal to the tunnel in a dynamic way, and the control measurements that depend on design data are eliminated. By using odometers to locate the cross-sections and correcting the data based on longitudinal joints of tunnel segment lining, the cost of the system has been significantly reduced, and the interval between adjacent cross-sections can reach 1-2 cm when pushed to collect data at a normal walking speed. Meanwhile, the relative deformation of tunnel can be analyzed by selecting cross-sections from original data. Through the measurement of the actual tunnel, the applicability of the system for tunnel deformation detection is verified, and the system is shown to be 15 times more efficient than that of the total station. The simulation experiment of the tunnel deformation indicates that the measurement accuracy of TS1 for cross-sections is 1.1 mm. Compared with the traditional method, TS1 improves the efficiency as well as increases the density of the obtained points.

  7. Study of the Integration of the CNU-TS-1 Mobile Tunnel Monitoring System

    Directory of Open Access Journals (Sweden)

    Liming Du

    2018-02-01

    Full Text Available A rapid, precise and automated means for the regular inspection and maintenance of a large number of tunnels is needed. Based on the depth study of the tunnel monitoring method, the CNU-TS-1 mobile tunnel monitoring system (TS1 is developed and presented. It can efficiently obtain the cross-sections that are orthogonal to the tunnel in a dynamic way, and the control measurements that depend on design data are eliminated. By using odometers to locate the cross-sections and correcting the data based on longitudinal joints of tunnel segment lining, the cost of the system has been significantly reduced, and the interval between adjacent cross-sections can reach 1–2 cm when pushed to collect data at a normal walking speed. Meanwhile, the relative deformation of tunnel can be analyzed by selecting cross-sections from original data. Through the measurement of the actual tunnel, the applicability of the system for tunnel deformation detection is verified, and the system is shown to be 15 times more efficient than that of the total station. The simulation experiment of the tunnel deformation indicates that the measurement accuracy of TS1 for cross-sections is 1.1 mm. Compared with the traditional method, TS1 improves the efficiency as well as increases the density of the obtained points.

  8. Multifractal analysis of visibility graph-based Ito-related connectivity time series.

    Science.gov (United States)

    Czechowski, Zbigniew; Lovallo, Michele; Telesca, Luciano

    2016-02-01

    In this study, we investigate multifractal properties of connectivity time series resulting from the visibility graph applied to normally distributed time series generated by the Ito equations with multiplicative power-law noise. We show that multifractality of the connectivity time series (i.e., the series of numbers of links outgoing any node) increases with the exponent of the power-law noise. The multifractality of the connectivity time series could be due to the width of connectivity degree distribution that can be related to the exit time of the associated Ito time series. Furthermore, the connectivity time series are characterized by persistence, although the original Ito time series are random; this is due to the procedure of visibility graph that, connecting the values of the time series, generates persistence but destroys most of the nonlinear correlations. Moreover, the visibility graph is sensitive for detecting wide "depressions" in input time series.

  9. Mathematical foundations of time series analysis a concise introduction

    CERN Document Server

    Beran, Jan

    2017-01-01

    This book provides a concise introduction to the mathematical foundations of time series analysis, with an emphasis on mathematical clarity. The text is reduced to the essential logical core, mostly using the symbolic language of mathematics, thus enabling readers to very quickly grasp the essential reasoning behind time series analysis. It appeals to anybody wanting to understand time series in a precise, mathematical manner. It is suitable for graduate courses in time series analysis but is equally useful as a reference work for students and researchers alike.

  10. Time series analysis in the social sciences the fundamentals

    CERN Document Server

    Shin, Youseop

    2017-01-01

    Times Series Analysis in the Social Sciences is a practical and highly readable introduction written exclusively for students and researchers whose mathematical background is limited to basic algebra. The book focuses on fundamental elements of time series analysis that social scientists need to understand so they can employ time series analysis for their research and practice. Through step-by-step explanations and using monthly violent crime rates as case studies, this book explains univariate time series from the preliminary visual analysis through the modeling of seasonality, trends, and re

  11. Data imputation analysis for Cosmic Rays time series

    Science.gov (United States)

    Fernandes, R. C.; Lucio, P. S.; Fernandez, J. H.

    2017-05-01

    The occurrence of missing data concerning Galactic Cosmic Rays time series (GCR) is inevitable since loss of data is due to mechanical and human failure or technical problems and different periods of operation of GCR stations. The aim of this study was to perform multiple dataset imputation in order to depict the observational dataset. The study has used the monthly time series of GCR Climax (CLMX) and Roma (ROME) from 1960 to 2004 to simulate scenarios of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80% and 90% of missing data compared to observed ROME series, with 50 replicates. Then, the CLMX station as a proxy for allocation of these scenarios was used. Three different methods for monthly dataset imputation were selected: AMÉLIA II - runs the bootstrap Expectation Maximization algorithm, MICE - runs an algorithm via Multivariate Imputation by Chained Equations and MTSDI - an Expectation Maximization algorithm-based method for imputation of missing values in multivariate normal time series. The synthetic time series compared with the observed ROME series has also been evaluated using several skill measures as such as RMSE, NRMSE, Agreement Index, R, R2, F-test and t-test. The results showed that for CLMX and ROME, the R2 and R statistics were equal to 0.98 and 0.96, respectively. It was observed that increases in the number of gaps generate loss of quality of the time series. Data imputation was more efficient with MTSDI method, with negligible errors and best skill coefficients. The results suggest a limit of about 60% of missing data for imputation, for monthly averages, no more than this. It is noteworthy that CLMX, ROME and KIEL stations present no missing data in the target period. This methodology allowed reconstructing 43 time series.

  12. Tabu search for the redundancy allocation problem of homogenous series-parallel multi-state systems

    International Nuclear Information System (INIS)

    Ouzineb, Mohamed; Nourelfath, Mustapha; Gendreau, Michel

    2008-01-01

    This paper develops an efficient tabu search (TS) heuristic to solve the redundancy allocation problem for multi-state series-parallel systems. The system has a range of performance levels from perfect functioning to complete failure. Identical redundant elements are included in order to achieve a desirable level of availability. The elements of the system are characterized by their cost, performance and availability. These elements are chosen from a list of products available in the market. System availability is defined as the ability to satisfy consumer demand, which is represented as a piecewise cumulative load curve. A universal generating function technique is applied to evaluate system availability. The proposed TS heuristic determines the minimal cost system configuration under availability constraints. An originality of our approach is that it proceeds by dividing the search space into a set of disjoint subsets, and then by applying TS to each subset. The design problem, solved in this study, has been previously analyzed using genetic algorithms (GAs). Numerical results for the test problems from previous research are reported, and larger test problems are randomly generated. Comparisons show that the proposed TS out-performs GA solutions, in terms of both the solution quality and the execution time

  13. Algorithm for Compressing Time-Series Data

    Science.gov (United States)

    Hawkins, S. Edward, III; Darlington, Edward Hugo

    2012-01-01

    An algorithm based on Chebyshev polynomials effects lossy compression of time-series data or other one-dimensional data streams (e.g., spectral data) that are arranged in blocks for sequential transmission. The algorithm was developed for use in transmitting data from spacecraft scientific instruments to Earth stations. In spite of its lossy nature, the algorithm preserves the information needed for scientific analysis. The algorithm is computationally simple, yet compresses data streams by factors much greater than two. The algorithm is not restricted to spacecraft or scientific uses: it is applicable to time-series data in general. The algorithm can also be applied to general multidimensional data that have been converted to time-series data, a typical example being image data acquired by raster scanning. However, unlike most prior image-data-compression algorithms, this algorithm neither depends on nor exploits the two-dimensional spatial correlations that are generally present in images. In order to understand the essence of this compression algorithm, it is necessary to understand that the net effect of this algorithm and the associated decompression algorithm is to approximate the original stream of data as a sequence of finite series of Chebyshev polynomials. For the purpose of this algorithm, a block of data or interval of time for which a Chebyshev polynomial series is fitted to the original data is denoted a fitting interval. Chebyshev approximation has two properties that make it particularly effective for compressing serial data streams with minimal loss of scientific information: The errors associated with a Chebyshev approximation are nearly uniformly distributed over the fitting interval (this is known in the art as the "equal error property"); and the maximum deviations of the fitted Chebyshev polynomial from the original data have the smallest possible values (this is known in the art as the "min-max property").

  14. Modeling of Volatility with Non-linear Time Series Model

    OpenAIRE

    Kim Song Yon; Kim Mun Chol

    2013-01-01

    In this paper, non-linear time series models are used to describe volatility in financial time series data. To describe volatility, two of the non-linear time series are combined into form TAR (Threshold Auto-Regressive Model) with AARCH (Asymmetric Auto-Regressive Conditional Heteroskedasticity) error term and its parameter estimation is studied.

  15. Layered Ensemble Architecture for Time Series Forecasting.

    Science.gov (United States)

    Rahman, Md Mustafizur; Islam, Md Monirul; Murase, Kazuyuki; Yao, Xin

    2016-01-01

    Time series forecasting (TSF) has been widely used in many application areas such as science, engineering, and finance. The phenomena generating time series are usually unknown and information available for forecasting is only limited to the past values of the series. It is, therefore, necessary to use an appropriate number of past values, termed lag, for forecasting. This paper proposes a layered ensemble architecture (LEA) for TSF problems. Our LEA consists of two layers, each of which uses an ensemble of multilayer perceptron (MLP) networks. While the first ensemble layer tries to find an appropriate lag, the second ensemble layer employs the obtained lag for forecasting. Unlike most previous work on TSF, the proposed architecture considers both accuracy and diversity of the individual networks in constructing an ensemble. LEA trains different networks in the ensemble by using different training sets with an aim of maintaining diversity among the networks. However, it uses the appropriate lag and combines the best trained networks to construct the ensemble. This indicates LEAs emphasis on accuracy of the networks. The proposed architecture has been tested extensively on time series data of neural network (NN)3 and NN5 competitions. It has also been tested on several standard benchmark time series data. In terms of forecasting accuracy, our experimental results have revealed clearly that LEA is better than other ensemble and nonensemble methods.

  16. A hybrid GA-TS algorithm for open vehicle routing optimization of coal mines material

    Energy Technology Data Exchange (ETDEWEB)

    Yu, S.W.; Ding, C.; Zhu, K.J. [China University of Geoscience, Wuhan (China)

    2011-08-15

    In the open vehicle routing problem (OVRP), the objective is to minimize the number of vehicles and the total distance (or time) traveled. This study primarily focuses on solving an open vehicle routing problem (OVRP) by applying a novel hybrid genetic algorithm and the Tabu search (GA-TS), which combines the GA's parallel computing and global optimization with TS's Tabu search skill and fast local search. Firstly, the proposed algorithm uses natural number coding according to the customer demands and the captivity of the vehicle for globe optimization. Secondly, individuals of population do TS local search with a certain degree of probability, namely, do the local routing optimization of all customer sites belong to one vehicle. The mechanism not only improves the ability of global optimization, but also ensures the speed of operation. The algorithm was used in Zhengzhou Coal Mine and Power Supply Co., Ltd.'s transport vehicle routing optimization.

  17. A window-based time series feature extraction method.

    Science.gov (United States)

    Katircioglu-Öztürk, Deniz; Güvenir, H Altay; Ravens, Ursula; Baykal, Nazife

    2017-10-01

    This study proposes a robust similarity score-based time series feature extraction method that is termed as Window-based Time series Feature ExtraCtion (WTC). Specifically, WTC generates domain-interpretable results and involves significantly low computational complexity thereby rendering itself useful for densely sampled and populated time series datasets. In this study, WTC is applied to a proprietary action potential (AP) time series dataset on human cardiomyocytes and three precordial leads from a publicly available electrocardiogram (ECG) dataset. This is followed by comparing WTC in terms of predictive accuracy and computational complexity with shapelet transform and fast shapelet transform (which constitutes an accelerated variant of the shapelet transform). The results indicate that WTC achieves a slightly higher classification performance with significantly lower execution time when compared to its shapelet-based alternatives. With respect to its interpretable features, WTC has a potential to enable medical experts to explore definitive common trends in novel datasets. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. T-S Fuzzy Modelling and H∞ Attitude Control for Hypersonic Gliding Vehicles

    Directory of Open Access Journals (Sweden)

    Weidong Zhang

    2017-01-01

    Full Text Available This paper addresses the T-S fuzzy modelling and H∞ attitude control in three channels for hypersonic gliding vehicles (HGVs. First, the control-oriented affine nonlinear model has been established which is transformed from the reentry dynamics. Then, based on Taylor’s expansion approach and the fuzzy linearization approach, the homogeneous T-S local modelling technique for HGVs is proposed. Given the approximation accuracy and controller design complexity, appropriate fuzzy premise variables and operating points of interest are selected to construct the T-S homogeneous submodels. With so-called fuzzy blending, the original plant is transformed into the overall T-S fuzzy model with disturbance. By utilizing Lyapunov functional approach, a state feedback fuzzy controller has been designed based on relaxed linear matrix inequality (LMI conditions to stable the original plants with a prescribed H∞ performance of disturbance. Finally, numerical simulations are performed to demonstrate the effectiveness of the proposed H∞ T-S fuzzy controller for the original attitude dynamics; the superiority of the designed T-S fuzzy controller compared with other local controllers based on the constructed fuzzy model is shown as well.

  19. Prewhitening of hydroclimatic time series? Implications for inferred change and variability across time scales

    Science.gov (United States)

    Razavi, Saman; Vogel, Richard

    2018-02-01

    Prewhitening, the process of eliminating or reducing short-term stochastic persistence to enable detection of deterministic change, has been extensively applied to time series analysis of a range of geophysical variables. Despite the controversy around its utility, methodologies for prewhitening time series continue to be a critical feature of a variety of analyses including: trend detection of hydroclimatic variables and reconstruction of climate and/or hydrology through proxy records such as tree rings. With a focus on the latter, this paper presents a generalized approach to exploring the impact of a wide range of stochastic structures of short- and long-term persistence on the variability of hydroclimatic time series. Through this approach, we examine the impact of prewhitening on the inferred variability of time series across time scales. We document how a focus on prewhitened, residual time series can be misleading, as it can drastically distort (or remove) the structure of variability across time scales. Through examples with actual data, we show how such loss of information in prewhitened time series of tree rings (so-called "residual chronologies") can lead to the underestimation of extreme conditions in climate and hydrology, particularly droughts, reconstructed for centuries preceding the historical period.

  20. DTW-APPROACH FOR UNCORRELATED MULTIVARIATE TIME SERIES IMPUTATION

    OpenAIRE

    Phan , Thi-Thu-Hong; Poisson Caillault , Emilie; Bigand , André; Lefebvre , Alain

    2017-01-01

    International audience; Missing data are inevitable in almost domains of applied sciences. Data analysis with missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). Some well-known methods for multivariate time series imputation require high correlations between series or their features. In this paper , we propose an approach based on the shape-behaviour relation in low/un-correlated multivariate time series under an assumption of...

  1. Evaluation of the egg transmission and pathogenicity of Mycoplasma gallisepticum isolates genotyped as ts-11.

    Science.gov (United States)

    Armour, Natalie K; Ferguson-Noel, Naola

    2015-01-01

    Live Mycoplasma gallisepticum vaccines are used for the control of respiratory disease, egg production losses and egg transmission associated with M. gallisepticum infection in long-lived poultry. The first field case of apparent increased virulence and vertical transmission of ts-11, a live M. gallisepticum vaccine, has been reported. In that study a M. gallisepticum isolate from the broiler progeny of ts-11-vaccinated breeders was genotyped as ts-11 by sequence analysis of four different genetic targets and Random Amplified Polymorphic DNA and found to be significantly more virulent than ts-11 vaccine. The objective of the current study was to evaluate the rate of egg transmission and pathogenicity of ts-11 vaccine and isolates recovered from ts-11-vaccinated breeders (K6222B) and their broiler progeny (K6216D) which had been genotyped as ts-11. Groups of 28-week-old specific pathogen-free chickens at 87% average weekly egg production were inoculated with sterile broth media (negative controls), ts-11 vaccine, K6222B, K6216D or R strain (positive controls) by eye-drop and aerosol. K6216D transmitted via the egg at an average rate of 4.0% in the third and fourth weeks post-infection, while egg transmission of K6222B and ts-11 vaccine was not detected. M. gallisepticum was isolated from the air sacs, ovaries and oviducts of hens infected with K6216D and K6222B, but not from those infected with ts-11 vaccine. K6216D and K6222B both induced respiratory signs and significantly more tracheal colonization and more severe tracheal and air sac lesions than ts-11 vaccine (P ≤ 0.05). There were no substantial differences in the egg production of ts-11, K6216D and K6222B infected groups. These results provide the first conclusive evidence of transovarian transmission of an isolate genotyped as ts-11 and indicate that isolates genotyed as ts-11 vary in their virulence and ability to transmit via the egg.

  2. Variable Selection in Time Series Forecasting Using Random Forests

    Directory of Open Access Journals (Sweden)

    Hristos Tyralis

    2017-10-01

    Full Text Available Time series forecasting using machine learning algorithms has gained popularity recently. Random forest is a machine learning algorithm implemented in time series forecasting; however, most of its forecasting properties have remained unexplored. Here we focus on assessing the performance of random forests in one-step forecasting using two large datasets of short time series with the aim to suggest an optimal set of predictor variables. Furthermore, we compare its performance to benchmarking methods. The first dataset is composed by 16,000 simulated time series from a variety of Autoregressive Fractionally Integrated Moving Average (ARFIMA models. The second dataset consists of 135 mean annual temperature time series. The highest predictive performance of RF is observed when using a low number of recent lagged predictor variables. This outcome could be useful in relevant future applications, with the prospect to achieve higher predictive accuracy.

  3. Real-Time Fault Detection Approach for Nonlinear Systems and its Asynchronous T-S Fuzzy Observer-Based Implementation.

    Science.gov (United States)

    Li, Linlin; Ding, Steven X; Qiu, Jianbin; Yang, Ying

    2017-02-01

    This paper is concerned with a real-time observer-based fault detection (FD) approach for a general type of nonlinear systems in the presence of external disturbances. To this end, in the first part of this paper, we deal with the definition and the design condition for an L ∞ / L 2 type of nonlinear observer-based FD systems. This analytical framework is fundamental for the development of real-time nonlinear FD systems with the aid of some well-established techniques. In the second part, we address the integrated design of the L ∞ / L 2 observer-based FD systems by applying Takagi-Sugeno (T-S) fuzzy dynamic modeling technique as the solution tool. This fuzzy observer-based FD approach is developed via piecewise Lyapunov functions, and can be applied to the case that the premise variables of the FD system is nonsynchronous with the premise variables of the fuzzy model of the plant. In the end, a case study on the laboratory setup of three-tank system is given to show the efficiency of the proposed results.

  4. Operation and consolidation: lifetime of equipment and priorities TS

    CERN Document Server

    Béjar-Alonso, Isabel

    2008-01-01

    Since 1999 the TS department has benchmarked his services with the industry and other research laboratories and has prepared several consolidation plans. An evaluation of the results obtained from the different consolidation plans will be summarized. The difference between the projects achieved and those planned will be analyzed. The impact of the differed consolidation and of the maintenance backlog will be analyzed in order to present the future TS consolidation strategy

  5. Trend time-series modeling and forecasting with neural networks.

    Science.gov (United States)

    Qi, Min; Zhang, G Peter

    2008-05-01

    Despite its great importance, there has been no general consensus on how to model the trends in time-series data. Compared to traditional approaches, neural networks (NNs) have shown some promise in time-series forecasting. This paper investigates how to best model trend time series using NNs. Four different strategies (raw data, raw data with time index, detrending, and differencing) are used to model various trend patterns (linear, nonlinear, deterministic, stochastic, and breaking trend). We find that with NNs differencing often gives meritorious results regardless of the underlying data generating processes (DGPs). This finding is also confirmed by the real gross national product (GNP) series.

  6. Segmentation of Nonstationary Time Series with Geometric Clustering

    DEFF Research Database (Denmark)

    Bocharov, Alexei; Thiesson, Bo

    2013-01-01

    We introduce a non-parametric method for segmentation in regimeswitching time-series models. The approach is based on spectral clustering of target-regressor tuples and derives a switching regression tree, where regime switches are modeled by oblique splits. Such models can be learned efficiently...... from data, where clustering is used to propose one single split candidate at each split level. We use the class of ART time series models to serve as illustration, but because of the non-parametric nature of our segmentation approach, it readily generalizes to a wide range of time-series models that go...

  7. Non-parametric characterization of long-term rainfall time series

    Science.gov (United States)

    Tiwari, Harinarayan; Pandey, Brij Kishor

    2018-03-01

    The statistical study of rainfall time series is one of the approaches for efficient hydrological system design. Identifying, and characterizing long-term rainfall time series could aid in improving hydrological systems forecasting. In the present study, eventual statistics was applied for the long-term (1851-2006) rainfall time series under seven meteorological regions of India. Linear trend analysis was carried out using Mann-Kendall test for the observed rainfall series. The observed trend using the above-mentioned approach has been ascertained using the innovative trend analysis method. Innovative trend analysis has been found to be a strong tool to detect the general trend of rainfall time series. Sequential Mann-Kendall test has also been carried out to examine nonlinear trends of the series. The partial sum of cumulative deviation test is also found to be suitable to detect the nonlinear trend. Innovative trend analysis, sequential Mann-Kendall test and partial cumulative deviation test have potential to detect the general as well as nonlinear trend for the rainfall time series. Annual rainfall analysis suggests that the maximum changes in mean rainfall is 11.53% for West Peninsular India, whereas the maximum fall in mean rainfall is 7.8% for the North Mountainous Indian region. The innovative trend analysis method is also capable of finding the number of change point available in the time series. Additionally, we have performed von Neumann ratio test and cumulative deviation test to estimate the departure from homogeneity. Singular spectrum analysis has been applied in this study to evaluate the order of departure from homogeneity in the rainfall time series. Monsoon season (JS) of North Mountainous India and West Peninsular India zones has higher departure from homogeneity and singular spectrum analysis shows the results to be in coherence with the same.

  8. Do's and Don'ts for Liposuction

    Science.gov (United States)

    ... for Every Season How to Choose the Best Skin Care Products In This Section Dermatologic Surgery What is dermatologic ... for Every Season How to Choose the Best Skin Care Products Do's and don’ts for liposuction Don’t ...

  9. Time Series Decomposition into Oscillation Components and Phase Estimation.

    Science.gov (United States)

    Matsuda, Takeru; Komaki, Fumiyasu

    2017-02-01

    Many time series are naturally considered as a superposition of several oscillation components. For example, electroencephalogram (EEG) time series include oscillation components such as alpha, beta, and gamma. We propose a method for decomposing time series into such oscillation components using state-space models. Based on the concept of random frequency modulation, gaussian linear state-space models for oscillation components are developed. In this model, the frequency of an oscillator fluctuates by noise. Time series decomposition is accomplished by this model like the Bayesian seasonal adjustment method. Since the model parameters are estimated from data by the empirical Bayes' method, the amplitudes and the frequencies of oscillation components are determined in a data-driven manner. Also, the appropriate number of oscillation components is determined with the Akaike information criterion (AIC). In this way, the proposed method provides a natural decomposition of the given time series into oscillation components. In neuroscience, the phase of neural time series plays an important role in neural information processing. The proposed method can be used to estimate the phase of each oscillation component and has several advantages over a conventional method based on the Hilbert transform. Thus, the proposed method enables an investigation of the phase dynamics of time series. Numerical results show that the proposed method succeeds in extracting intermittent oscillations like ripples and detecting the phase reset phenomena. We apply the proposed method to real data from various fields such as astronomy, ecology, tidology, and neuroscience.

  10. Introduction to time series analysis and forecasting

    CERN Document Server

    Montgomery, Douglas C; Kulahci, Murat

    2015-01-01

    Praise for the First Edition ""…[t]he book is great for readers who need to apply the methods and models presented but have little background in mathematics and statistics."" -MAA Reviews Thoroughly updated throughout, Introduction to Time Series Analysis and Forecasting, Second Edition presents the underlying theories of time series analysis that are needed to analyze time-oriented data and construct real-world short- to medium-term statistical forecasts.    Authored by highly-experienced academics and professionals in engineering statistics, the Second Edition features discussions on both

  11. Multi-Scale Dissemination of Time Series Data

    DEFF Research Database (Denmark)

    Guo, Qingsong; Zhou, Yongluan; Su, Li

    2013-01-01

    In this paper, we consider the problem of continuous dissemination of time series data, such as sensor measurements, to a large number of subscribers. These subscribers fall into multiple subscription levels, where each subscription level is specified by the bandwidth constraint of a subscriber......, which is an abstract indicator for both the physical limits and the amount of data that the subscriber would like to handle. To handle this problem, we propose a system framework for multi-scale time series data dissemination that employs a typical tree-based dissemination network and existing time...

  12. RADON CONCENTRATION TIME SERIES MODELING AND APPLICATION DISCUSSION.

    Science.gov (United States)

    Stránský, V; Thinová, L

    2017-11-01

    In the year 2010 a continual radon measurement was established at Mladeč Caves in the Czech Republic using a continual radon monitor RADIM3A. In order to model radon time series in the years 2010-15, the Box-Jenkins Methodology, often used in econometrics, was applied. Because of the behavior of radon concentrations (RCs), a seasonal integrated, autoregressive moving averages model with exogenous variables (SARIMAX) has been chosen to model the measured time series. This model uses the time series seasonality, previously acquired values and delayed atmospheric parameters, to forecast RC. The developed model for RC time series is called regARIMA(5,1,3). Model residuals could be retrospectively compared with seismic evidence of local or global earthquakes, which occurred during the RCs measurement. This technique enables us to asses if continuously measured RC could serve an earthquake precursor. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  13. Similarity estimators for irregular and age uncertain time series

    Science.gov (United States)

    Rehfeld, K.; Kurths, J.

    2013-09-01

    Paleoclimate time series are often irregularly sampled and age uncertain, which is an important technical challenge to overcome for successful reconstruction of past climate variability and dynamics. Visual comparison and interpolation-based linear correlation approaches have been used to infer dependencies from such proxy time series. While the first is subjective, not measurable and not suitable for the comparison of many datasets at a time, the latter introduces interpolation bias, and both face difficulties if the underlying dependencies are nonlinear. In this paper we investigate similarity estimators that could be suitable for the quantitative investigation of dependencies in irregular and age uncertain time series. We compare the Gaussian-kernel based cross correlation (gXCF, Rehfeld et al., 2011) and mutual information (gMI, Rehfeld et al., 2013) against their interpolation-based counterparts and the new event synchronization function (ESF). We test the efficiency of the methods in estimating coupling strength and coupling lag numerically, using ensembles of synthetic stalagmites with short, autocorrelated, linear and nonlinearly coupled proxy time series, and in the application to real stalagmite time series. In the linear test case coupling strength increases are identified consistently for all estimators, while in the nonlinear test case the correlation-based approaches fail. The lag at which the time series are coupled is identified correctly as the maximum of the similarity functions in around 60-55% (in the linear case) to 53-42% (for the nonlinear processes) of the cases when the dating of the synthetic stalagmite is perfectly precise. If the age uncertainty increases beyond 5% of the time series length, however, the true coupling lag is not identified more often than the others for which the similarity function was estimated. Age uncertainty contributes up to half of the uncertainty in the similarity estimation process. Time series irregularity

  14. Similarity estimators for irregular and age-uncertain time series

    Science.gov (United States)

    Rehfeld, K.; Kurths, J.

    2014-01-01

    Paleoclimate time series are often irregularly sampled and age uncertain, which is an important technical challenge to overcome for successful reconstruction of past climate variability and dynamics. Visual comparison and interpolation-based linear correlation approaches have been used to infer dependencies from such proxy time series. While the first is subjective, not measurable and not suitable for the comparison of many data sets at a time, the latter introduces interpolation bias, and both face difficulties if the underlying dependencies are nonlinear. In this paper we investigate similarity estimators that could be suitable for the quantitative investigation of dependencies in irregular and age-uncertain time series. We compare the Gaussian-kernel-based cross-correlation (gXCF, Rehfeld et al., 2011) and mutual information (gMI, Rehfeld et al., 2013) against their interpolation-based counterparts and the new event synchronization function (ESF). We test the efficiency of the methods in estimating coupling strength and coupling lag numerically, using ensembles of synthetic stalagmites with short, autocorrelated, linear and nonlinearly coupled proxy time series, and in the application to real stalagmite time series. In the linear test case, coupling strength increases are identified consistently for all estimators, while in the nonlinear test case the correlation-based approaches fail. The lag at which the time series are coupled is identified correctly as the maximum of the similarity functions in around 60-55% (in the linear case) to 53-42% (for the nonlinear processes) of the cases when the dating of the synthetic stalagmite is perfectly precise. If the age uncertainty increases beyond 5% of the time series length, however, the true coupling lag is not identified more often than the others for which the similarity function was estimated. Age uncertainty contributes up to half of the uncertainty in the similarity estimation process. Time series irregularity

  15. Robust Forecasting of Non-Stationary Time Series

    OpenAIRE

    Croux, C.; Fried, R.; Gijbels, I.; Mahieu, K.

    2010-01-01

    This paper proposes a robust forecasting method for non-stationary time series. The time series is modelled using non-parametric heteroscedastic regression, and fitted by a localized MM-estimator, combining high robustness and large efficiency. The proposed method is shown to produce reliable forecasts in the presence of outliers, non-linearity, and heteroscedasticity. In the absence of outliers, the forecasts are only slightly less precise than those based on a localized Least Squares estima...

  16. Time Series Econometrics for the 21st Century

    Science.gov (United States)

    Hansen, Bruce E.

    2017-01-01

    The field of econometrics largely started with time series analysis because many early datasets were time-series macroeconomic data. As the field developed, more cross-sectional and longitudinal datasets were collected, which today dominate the majority of academic empirical research. In nonacademic (private sector, central bank, and governmental)…

  17. Effectiveness of firefly algorithm based neural network in time series ...

    African Journals Online (AJOL)

    Effectiveness of firefly algorithm based neural network in time series forecasting. ... In the experiments, three well known time series were used to evaluate the performance. Results obtained were compared with ... Keywords: Time series, Artificial Neural Network, Firefly Algorithm, Particle Swarm Optimization, Overfitting ...

  18. Identification and characterization of a Bacillus subtilis strain TS06 ...

    African Journals Online (AJOL)

    Replant disease is a major limitation for strawberry production in greenhouse. Bio-control may be a good way to cope with the replant diseases. Here, we report identification and characterization of a bacterial strain TS06 that may be used as a bio-control agent against the replant diseases in strawberry. TS06 was identified ...

  19. Time Series Analysis of Insar Data: Methods and Trends

    Science.gov (United States)

    Osmanoglu, Batuhan; Sunar, Filiz; Wdowinski, Shimon; Cano-Cabral, Enrique

    2015-01-01

    Time series analysis of InSAR data has emerged as an important tool for monitoring and measuring the displacement of the Earth's surface. Changes in the Earth's surface can result from a wide range of phenomena such as earthquakes, volcanoes, landslides, variations in ground water levels, and changes in wetland water levels. Time series analysis is applied to interferometric phase measurements, which wrap around when the observed motion is larger than one-half of the radar wavelength. Thus, the spatio-temporal ''unwrapping" of phase observations is necessary to obtain physically meaningful results. Several different algorithms have been developed for time series analysis of InSAR data to solve for this ambiguity. These algorithms may employ different models for time series analysis, but they all generate a first-order deformation rate, which can be compared to each other. However, there is no single algorithm that can provide optimal results in all cases. Since time series analyses of InSAR data are used in a variety of applications with different characteristics, each algorithm possesses inherently unique strengths and weaknesses. In this review article, following a brief overview of InSAR technology, we discuss several algorithms developed for time series analysis of InSAR data using an example set of results for measuring subsidence rates in Mexico City.

  20. Interpretation of a compositional time series

    Science.gov (United States)

    Tolosana-Delgado, R.; van den Boogaart, K. G.

    2012-04-01

    Common methods for multivariate time series analysis use linear operations, from the definition of a time-lagged covariance/correlation to the prediction of new outcomes. However, when the time series response is a composition (a vector of positive components showing the relative importance of a set of parts in a total, like percentages and proportions), then linear operations are afflicted of several problems. For instance, it has been long recognised that (auto/cross-)correlations between raw percentages are spurious, more dependent on which other components are being considered than on any natural link between the components of interest. Also, a long-term forecast of a composition in models with a linear trend will ultimately predict negative components. In general terms, compositional data should not be treated in a raw scale, but after a log-ratio transformation (Aitchison, 1986: The statistical analysis of compositional data. Chapman and Hill). This is so because the information conveyed by a compositional data is relative, as stated in their definition. The principle of working in coordinates allows to apply any sort of multivariate analysis to a log-ratio transformed composition, as long as this transformation is invertible. This principle is of full application to time series analysis. We will discuss how results (both auto/cross-correlation functions and predictions) can be back-transformed, viewed and interpreted in a meaningful way. One view is to use the exhaustive set of all possible pairwise log-ratios, which allows to express the results into D(D - 1)/2 separate, interpretable sets of one-dimensional models showing the behaviour of each possible pairwise log-ratios. Another view is the interpretation of estimated coefficients or correlations back-transformed in terms of compositions. These two views are compatible and complementary. These issues are illustrated with time series of seasonal precipitation patterns at different rain gauges of the USA

  1. Capturing Structure Implicitly from Time-Series having Limited Data

    OpenAIRE

    Emaasit, Daniel; Johnson, Matthew

    2018-01-01

    Scientific fields such as insider-threat detection and highway-safety planning often lack sufficient amounts of time-series data to estimate statistical models for the purpose of scientific discovery. Moreover, the available limited data are quite noisy. This presents a major challenge when estimating time-series models that are robust to overfitting and have well-calibrated uncertainty estimates. Most of the current literature in these fields involve visualizing the time-series for noticeabl...

  2. Self-affinity in the dengue fever time series

    Science.gov (United States)

    Azevedo, S. M.; Saba, H.; Miranda, J. G. V.; Filho, A. S. Nascimento; Moret, M. A.

    2016-06-01

    Dengue is a complex public health problem that is common in tropical and subtropical regions. This disease has risen substantially in the last three decades, and the physical symptoms depict the self-affine behavior of the occurrences of reported dengue cases in Bahia, Brazil. This study uses detrended fluctuation analysis (DFA) to verify the scale behavior in a time series of dengue cases and to evaluate the long-range correlations that are characterized by the power law α exponent for different cities in Bahia, Brazil. The scaling exponent (α) presents different long-range correlations, i.e. uncorrelated, anti-persistent, persistent and diffusive behaviors. The long-range correlations highlight the complex behavior of the time series of this disease. The findings show that there are two distinct types of scale behavior. In the first behavior, the time series presents a persistent α exponent for a one-month period. For large periods, the time series signal approaches subdiffusive behavior. The hypothesis of the long-range correlations in the time series of the occurrences of reported dengue cases was validated. The observed self-affinity is useful as a forecasting tool for future periods through extrapolation of the α exponent behavior. This complex system has a higher predictability in a relatively short time (approximately one month), and it suggests a new tool in epidemiological control strategies. However, predictions for large periods using DFA are hidden by the subdiffusive behavior.

  3. On the plurality of times: disunified time and the A-series | Nefdt ...

    African Journals Online (AJOL)

    Then, I attempt to show that disunified time is a problem for a semantics based on the A-series since A-truthmakers are hard to come by in a universe of temporally disconnected time-series. Finally, I provide a novel argument showing that presentists should be particularly fearful of such a universe. South African Journal of ...

  4. Time-series modeling of long-term weight self-monitoring data.

    Science.gov (United States)

    Helander, Elina; Pavel, Misha; Jimison, Holly; Korhonen, Ilkka

    2015-08-01

    Long-term self-monitoring of weight is beneficial for weight maintenance, especially after weight loss. Connected weight scales accumulate time series information over long term and hence enable time series analysis of the data. The analysis can reveal individual patterns, provide more sensitive detection of significant weight trends, and enable more accurate and timely prediction of weight outcomes. However, long term self-weighing data has several challenges which complicate the analysis. Especially, irregular sampling, missing data, and existence of periodic (e.g. diurnal and weekly) patterns are common. In this study, we apply time series modeling approach on daily weight time series from two individuals and describe information that can be extracted from this kind of data. We study the properties of weight time series data, missing data and its link to individuals behavior, periodic patterns and weight series segmentation. Being able to understand behavior through weight data and give relevant feedback is desired to lead to positive intervention on health behaviors.

  5. Time series prediction of apple scab using meteorological ...

    African Journals Online (AJOL)

    A new prediction model for the early warning of apple scab is proposed in this study. The method is based on artificial intelligence and time series prediction. The infection period of apple scab was evaluated as the time series prediction model instead of summation of wetness duration. Also, the relations of different ...

  6. Characterization of time series via Rényi complexity-entropy curves

    Science.gov (United States)

    Jauregui, M.; Zunino, L.; Lenzi, E. K.; Mendes, R. S.; Ribeiro, H. V.

    2018-05-01

    One of the most useful tools for distinguishing between chaotic and stochastic time series is the so-called complexity-entropy causality plane. This diagram involves two complexity measures: the Shannon entropy and the statistical complexity. Recently, this idea has been generalized by considering the Tsallis monoparametric generalization of the Shannon entropy, yielding complexity-entropy curves. These curves have proven to enhance the discrimination among different time series related to stochastic and chaotic processes of numerical and experimental nature. Here we further explore these complexity-entropy curves in the context of the Rényi entropy, which is another monoparametric generalization of the Shannon entropy. By combining the Rényi entropy with the proper generalization of the statistical complexity, we associate a parametric curve (the Rényi complexity-entropy curve) with a given time series. We explore this approach in a series of numerical and experimental applications, demonstrating the usefulness of this new technique for time series analysis. We show that the Rényi complexity-entropy curves enable the differentiation among time series of chaotic, stochastic, and periodic nature. In particular, time series of stochastic nature are associated with curves displaying positive curvature in a neighborhood of their initial points, whereas curves related to chaotic phenomena have a negative curvature; finally, periodic time series are represented by vertical straight lines.

  7. Quantifying Selection with Pool-Seq Time Series Data.

    Science.gov (United States)

    Taus, Thomas; Futschik, Andreas; Schlötterer, Christian

    2017-11-01

    Allele frequency time series data constitute a powerful resource for unraveling mechanisms of adaptation, because the temporal dimension captures important information about evolutionary forces. In particular, Evolve and Resequence (E&R), the whole-genome sequencing of replicated experimentally evolving populations, is becoming increasingly popular. Based on computer simulations several studies proposed experimental parameters to optimize the identification of the selection targets. No such recommendations are available for the underlying parameters selection strength and dominance. Here, we introduce a highly accurate method to estimate selection parameters from replicated time series data, which is fast enough to be applied on a genome scale. Using this new method, we evaluate how experimental parameters can be optimized to obtain the most reliable estimates for selection parameters. We show that the effective population size (Ne) and the number of replicates have the largest impact. Because the number of time points and sequencing coverage had only a minor effect, we suggest that time series analysis is feasible without major increase in sequencing costs. We anticipate that time series analysis will become routine in E&R studies. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

  8. Transformation-cost time-series method for analyzing irregularly sampled data.

    Science.gov (United States)

    Ozken, Ibrahim; Eroglu, Deniz; Stemler, Thomas; Marwan, Norbert; Bagci, G Baris; Kurths, Jürgen

    2015-06-01

    Irregular sampling of data sets is one of the challenges often encountered in time-series analysis, since traditional methods cannot be applied and the frequently used interpolation approach can corrupt the data and bias the subsequence analysis. Here we present the TrAnsformation-Cost Time-Series (TACTS) method, which allows us to analyze irregularly sampled data sets without degenerating the quality of the data set. Instead of using interpolation we consider time-series segments and determine how close they are to each other by determining the cost needed to transform one segment into the following one. Using a limited set of operations-with associated costs-to transform the time series segments, we determine a new time series, that is our transformation-cost time series. This cost time series is regularly sampled and can be analyzed using standard methods. While our main interest is the analysis of paleoclimate data, we develop our method using numerical examples like the logistic map and the Rössler oscillator. The numerical data allows us to test the stability of our method against noise and for different irregular samplings. In addition we provide guidance on how to choose the associated costs based on the time series at hand. The usefulness of the TACTS method is demonstrated using speleothem data from the Secret Cave in Borneo that is a good proxy for paleoclimatic variability in the monsoon activity around the maritime continent.

  9. Transformation-cost time-series method for analyzing irregularly sampled data

    Science.gov (United States)

    Ozken, Ibrahim; Eroglu, Deniz; Stemler, Thomas; Marwan, Norbert; Bagci, G. Baris; Kurths, Jürgen

    2015-06-01

    Irregular sampling of data sets is one of the challenges often encountered in time-series analysis, since traditional methods cannot be applied and the frequently used interpolation approach can corrupt the data and bias the subsequence analysis. Here we present the TrAnsformation-Cost Time-Series (TACTS) method, which allows us to analyze irregularly sampled data sets without degenerating the quality of the data set. Instead of using interpolation we consider time-series segments and determine how close they are to each other by determining the cost needed to transform one segment into the following one. Using a limited set of operations—with associated costs—to transform the time series segments, we determine a new time series, that is our transformation-cost time series. This cost time series is regularly sampled and can be analyzed using standard methods. While our main interest is the analysis of paleoclimate data, we develop our method using numerical examples like the logistic map and the Rössler oscillator. The numerical data allows us to test the stability of our method against noise and for different irregular samplings. In addition we provide guidance on how to choose the associated costs based on the time series at hand. The usefulness of the TACTS method is demonstrated using speleothem data from the Secret Cave in Borneo that is a good proxy for paleoclimatic variability in the monsoon activity around the maritime continent.

  10. A multidisciplinary database for geophysical time series management

    Science.gov (United States)

    Montalto, P.; Aliotta, M.; Cassisi, C.; Prestifilippo, M.; Cannata, A.

    2013-12-01

    The variables collected by a sensor network constitute a heterogeneous data source that needs to be properly organized in order to be used in research and geophysical monitoring. With the time series term we refer to a set of observations of a given phenomenon acquired sequentially in time. When the time intervals are equally spaced one speaks of period or sampling frequency. Our work describes in detail a possible methodology for storage and management of time series using a specific data structure. We designed a framework, hereinafter called TSDSystem (Time Series Database System), in order to acquire time series from different data sources and standardize them within a relational database. The operation of standardization provides the ability to perform operations, such as query and visualization, of many measures synchronizing them using a common time scale. The proposed architecture follows a multiple layer paradigm (Loaders layer, Database layer and Business Logic layer). Each layer is specialized in performing particular operations for the reorganization and archiving of data from different sources such as ASCII, Excel, ODBC (Open DataBase Connectivity), file accessible from the Internet (web pages, XML). In particular, the loader layer performs a security check of the working status of each running software through an heartbeat system, in order to automate the discovery of acquisition issues and other warning conditions. Although our system has to manage huge amounts of data, performance is guaranteed by using a smart partitioning table strategy, that keeps balanced the percentage of data stored in each database table. TSDSystem also contains modules for the visualization of acquired data, that provide the possibility to query different time series on a specified time range, or follow the realtime signal acquisition, according to a data access policy from the users.

  11. Precursor effect on the property and catalytic behavior of Fe-TS-1 in butadiene epoxidation

    Science.gov (United States)

    Wu, Mei; Zhao, Huahua; Yang, Jian; Zhao, Jun; Song, Huanling; Chou, Lingjun

    2017-11-01

    The effect of iron precursor on the property and catalytic behavior of iron modified titanium silicalite molecular sieve (Fe-TS-1) catalysts in butadiene selective epoxidation has been studied. Three Fe-TS-1 catalysts were prepared, using iron nitrate, iron chloride and iron sulfate as precursors, which played an important role in adjusting the textural properties and chemical states of TS-1. Of the prepared Fe-TS-1 catalysts, those modified by iron nitrate (FN-TS-1) exhibited a significant enhanced performance in butadiene selective epoxidation compared to those derived from iron sulfate (FS-TS-1) or iron chloride (FC-TS-1) precursors. To obtain a deep understanding of their structure-performance relationship, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Temperature programmed desorption of NH3 (NH3-TPD), Diffuse reflectance UV-Vis spectra (DR UV-Vis), Fourier transformed infrared spectra (FT-IR) and thermal gravimetric analysis (TGA) were conducted to characterize Fe-TS-1 catalysts. Experimental results indicated that textural structures and acid sites of modified catalysts as well as the type of Fe species influenced by the precursors were all responsible for the activity and product distribution.

  12. Modeling financial time series with S-plus

    CERN Document Server

    Zivot, Eric

    2003-01-01

    The field of financial econometrics has exploded over the last decade This book represents an integration of theory, methods, and examples using the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate the practice of financial econometrics This is the first book to show the power of S-PLUS for the analysis of time series data It is written for researchers and practitioners in the finance industry, academic researchers in economics and finance, and advanced MBA and graduate students in economics and finance Readers are assumed to have a basic knowledge of S-PLUS and a solid grounding in basic statistics and time series concepts Eric Zivot is an associate professor and Gary Waterman Distinguished Scholar in the Economics Department at the University of Washington, and is co-director of the nascent Professional Master's Program in Computational Finance He regularly teaches courses on econometric theory, financial econometrics and time series econometrics, and is the recipient of the He...

  13. Application of Time Series Analysis in Determination of Lag Time in Jahanbin Basin

    Directory of Open Access Journals (Sweden)

    Seied Yahya Mirzaee

    2005-11-01

        One of the important issues that have significant role in study of hydrology of basin is determination of lag time. Lag time has significant role in hydrological studies. Quantity of rainfall related lag time depends on several factors, such as permeability, vegetation cover, catchments slope, rainfall intensity, storm duration and type of rain. Determination of lag time is important parameter in many projects such as dam design and also water resource studies. Lag time of basin could be calculated using various methods. One of these methods is time series analysis of spectral density. The analysis is based on fouries series. The time series is approximated with Sinuous and Cosines functions. In this method harmonically significant quantities with individual frequencies are presented. Spectral density under multiple time series could be used to obtain basin lag time for annual runoff and short-term rainfall fluctuation. A long lag time could be due to snowmelt as well as melting ice due to rainfalls in freezing days. In this research the lag time of Jahanbin basin has been determined using spectral density method. The catchments is subjected to both rainfall and snowfall. For short term rainfall fluctuation with a return period  2, 3, 4 months, the lag times were found 0.18, 0.5 and 0.083 month, respectively.

  14. Empirical method to measure stochasticity and multifractality in nonlinear time series

    Science.gov (United States)

    Lin, Chih-Hao; Chang, Chia-Seng; Li, Sai-Ping

    2013-12-01

    An empirical algorithm is used here to study the stochastic and multifractal nature of nonlinear time series. A parameter can be defined to quantitatively measure the deviation of the time series from a Wiener process so that the stochasticity of different time series can be compared. The local volatility of the time series under study can be constructed using this algorithm, and the multifractal structure of the time series can be analyzed by using this local volatility. As an example, we employ this method to analyze financial time series from different stock markets. The result shows that while developed markets evolve very much like an Ito process, the emergent markets are far from efficient. Differences about the multifractal structures and leverage effects between developed and emergent markets are discussed. The algorithm used here can be applied in a similar fashion to study time series of other complex systems.

  15. Review of $V_{cb}$ and $V_{td}/V_{ts}$

    OpenAIRE

    Forty, Roger W

    1997-01-01

    The current experimental status of the CKM matrix element V_cb and the ratio V_td/V_ts is reviewed. Knowledge of these elements has a strong impact on the unitarity triangle, of interest for studies of CP violation in the B system. The measurements of V_cb from both inclusive semileptonic b decays and the exclusive channel B -> D* l nu are reaching high precision. For V_td/V_ts the strongest upper limit is derived from studies of B-Bbar mixing.

  16. Transport of UF6 in compliance with TS-R-1

    International Nuclear Information System (INIS)

    Dekker, B.G.

    2004-01-01

    The IAEA Regulations TS-R-1 (ST-1, Revised) 1996 Edition include requirements for packages containing uranium hexafluoride (UF6); these are the first and only substance-specific requirements in the IAEA regulations. These requirements have already particularly affected, and will further affect, the transport of non-fissile and fissile excepted UF 6 and the packages used for these transports. Non-fissile and fissile excepted UF6 (ASTM C 787) has been transported worldwide for decades in a safe and reliable manner, using internationally standardised packages. Under the auspices of the World Nuclear Transport Institute (WNTI), an industry working group has been evaluating the existing packages against the requirements in TS-R-1. As new requirements came into effect, there were new challenges for the use of these standard packages, including the free drop test and the thermal requirements. In close cooperation with the WNTI HEXT Industry Working Group, a consortium of UF6 producers/users has worked together on the design and development, testing and certification of technical solutions for modification and optimisation of the existing packages to comply with TS-R-1. This paper reviews the existing standard packages against the requirements in TS-R-1. An update is also given describing the enhancements to the standard packages that have been designed and developed recently. The paper also describes how these solutions have been tested and certified, as well as the status of implementation. Finally, a review is made of the options that are available internationally to transport UF6 in compliance with TS-R-1. (author)

  17. Clinical time series prediction: Toward a hierarchical dynamical system framework.

    Science.gov (United States)

    Liu, Zitao; Hauskrecht, Milos

    2015-09-01

    Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. We tested our framework by first learning the time series model from data for the patients in the training set, and then using it to predict future time series values for the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive performance. Copyright © 2014 Elsevier B.V. All rights reserved.

  18. Clinical time series prediction: towards a hierarchical dynamical system framework

    Science.gov (United States)

    Liu, Zitao; Hauskrecht, Milos

    2014-01-01

    Objective Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Materials and methods Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. Results We tested our framework by first learning the time series model from data for the patient in the training set, and then applying the model in order to predict future time series values on the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. Conclusion A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive

  19. Turbulencelike Behavior of Seismic Time Series

    International Nuclear Information System (INIS)

    Manshour, P.; Saberi, S.; Sahimi, Muhammad; Peinke, J.; Pacheco, Amalio F.; Rahimi Tabar, M. Reza

    2009-01-01

    We report on a stochastic analysis of Earth's vertical velocity time series by using methods originally developed for complex hierarchical systems and, in particular, for turbulent flows. Analysis of the fluctuations of the detrended increments of the series reveals a pronounced transition in their probability density function from Gaussian to non-Gaussian. The transition occurs 5-10 hours prior to a moderate or large earthquake, hence representing a new and reliable precursor for detecting such earthquakes

  20. Characterizing time series: when Granger causality triggers complex networks

    Science.gov (United States)

    Ge, Tian; Cui, Yindong; Lin, Wei; Kurths, Jürgen; Liu, Chong

    2012-08-01

    In this paper, we propose a new approach to characterize time series with noise perturbations in both the time and frequency domains by combining Granger causality and complex networks. We construct directed and weighted complex networks from time series and use representative network measures to describe their physical and topological properties. Through analyzing the typical dynamical behaviors of some physical models and the MIT-BIHMassachusetts Institute of Technology-Beth Israel Hospital. human electrocardiogram data sets, we show that the proposed approach is able to capture and characterize various dynamics and has much potential for analyzing real-world time series of rather short length.

  1. Characterizing time series: when Granger causality triggers complex networks

    International Nuclear Information System (INIS)

    Ge Tian; Cui Yindong; Lin Wei; Liu Chong; Kurths, Jürgen

    2012-01-01

    In this paper, we propose a new approach to characterize time series with noise perturbations in both the time and frequency domains by combining Granger causality and complex networks. We construct directed and weighted complex networks from time series and use representative network measures to describe their physical and topological properties. Through analyzing the typical dynamical behaviors of some physical models and the MIT-BIH human electrocardiogram data sets, we show that the proposed approach is able to capture and characterize various dynamics and has much potential for analyzing real-world time series of rather short length. (paper)

  2. Multivariate time series analysis with R and financial applications

    CERN Document Server

    Tsay, Ruey S

    2013-01-01

    Since the publication of his first book, Analysis of Financial Time Series, Ruey Tsay has become one of the most influential and prominent experts on the topic of time series. Different from the traditional and oftentimes complex approach to multivariate (MV) time series, this sequel book emphasizes structural specification, which results in simplified parsimonious VARMA modeling and, hence, eases comprehension. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-worl

  3. Adrenalectomy for Cushing's syndrome: do's and don'ts.

    Science.gov (United States)

    Paduraru, D N; Nica, A; Carsote, M; Valea, A

    2016-01-01

    Aim. To present specific aspects of adrenalectomy for Cushing's syndrome (CS) by introducing well established aspects ("do's") and less known aspects ("don'ts"). Material and Method. This is a narrative review. Results. The "do's" for laparoscopic adrenalectomy (LA) are the following: it represents the "gold standard" for secretor and non-secretor adrenal tumors and the first line therapy for CS with an improvement of cardio-metabolic co-morbidities; the success rate depending on the adequate patients' selection and the surgeon's skills. The "don'ts" are large (>6-8 centimeters), locally invasive, malignant tumors requiring open adrenalectomy (OA). Robotic adrenalectomy is a new alternative for LA, with similar safety and conversion rate and lower pain drugs use. The "don'ts" are the following: lack of randomized controlled studies including oncologic outcome, different availability at surgical centers. Related to the sub-types of CS, the "do's" are the following: adrenal adenomas which are cured by LA, while adrenocortical carcinoma (ACC) requires adrenalectomy as first line therapy and adjuvant mitotane therapy; synchronous bilateral adrenalectomy (SBA) is useful for Cushing's disease (only cases refractory to pituitary targeted therapy), for ectopic Cushing's syndrome (cases with unknown or inoperable primary site), and for bilateral cortisol producing adenomas. The less established aspects are the following: criteria of skilled surgeon to approach ACC; the timing of surgery in subclinical CS; the need for adrenal vein catheterization (which is not available in many centers) to avoid unnecessary SBA. Conclusion. Adrenalectomy for CS is a dynamic domain; LA overstepped the former OA area. The future will improve the knowledge related to RA while the cutting edge is represented by a specific frame of intervention in SCS, children and pregnant women. Abbreviations: ACC = adrenocortical carcinoma, ACTH = Adrenocorticotropic Hormone, CD = Cushing's disease, CS

  4. Measurements of spatial population synchrony: influence of time series transformations.

    Science.gov (United States)

    Chevalier, Mathieu; Laffaille, Pascal; Ferdy, Jean-Baptiste; Grenouillet, Gaël

    2015-09-01

    Two mechanisms have been proposed to explain spatial population synchrony: dispersal among populations, and the spatial correlation of density-independent factors (the "Moran effect"). To identify which of these two mechanisms is driving spatial population synchrony, time series transformations (TSTs) of abundance data have been used to remove the signature of one mechanism, and highlight the effect of the other. However, several issues with TSTs remain, and to date no consensus has emerged about how population time series should be handled in synchrony studies. Here, by using 3131 time series involving 34 fish species found in French rivers, we computed several metrics commonly used in synchrony studies to determine whether a large-scale climatic factor (temperature) influenced fish population dynamics at the regional scale, and to test the effect of three commonly used TSTs (detrending, prewhitening and a combination of both) on these metrics. We also tested whether the influence of TSTs on time series and population synchrony levels was related to the features of the time series using both empirical and simulated time series. For several species, and regardless of the TST used, we evidenced a Moran effect on freshwater fish populations. However, these results were globally biased downward by TSTs which reduced our ability to detect significant signals. Depending on the species and the features of the time series, we found that TSTs could lead to contradictory results, regardless of the metric considered. Finally, we suggest guidelines on how population time series should be processed in synchrony studies.

  5. Stochastic time series analysis of hydrology data for water resources

    Science.gov (United States)

    Sathish, S.; Khadar Babu, S. K.

    2017-11-01

    The prediction to current publication of stochastic time series analysis in hydrology and seasonal stage. The different statistical tests for predicting the hydrology time series on Thomas-Fiering model. The hydrology time series of flood flow have accept a great deal of consideration worldwide. The concentration of stochastic process areas of time series analysis method are expanding with develop concerns about seasonal periods and global warming. The recent trend by the researchers for testing seasonal periods in the hydrologic flowseries using stochastic process on Thomas-Fiering model. The present article proposed to predict the seasonal periods in hydrology using Thomas-Fiering model.

  6. Neural network versus classical time series forecasting models

    Science.gov (United States)

    Nor, Maria Elena; Safuan, Hamizah Mohd; Shab, Noorzehan Fazahiyah Md; Asrul, Mohd; Abdullah, Affendi; Mohamad, Nurul Asmaa Izzati; Lee, Muhammad Hisyam

    2017-05-01

    Artificial neural network (ANN) has advantage in time series forecasting as it has potential to solve complex forecasting problems. This is because ANN is data driven approach which able to be trained to map past values of a time series. In this study the forecast performance between neural network and classical time series forecasting method namely seasonal autoregressive integrated moving average models was being compared by utilizing gold price data. Moreover, the effect of different data preprocessing on the forecast performance of neural network being examined. The forecast accuracy was evaluated using mean absolute deviation, root mean square error and mean absolute percentage error. It was found that ANN produced the most accurate forecast when Box-Cox transformation was used as data preprocessing.

  7. Nonlinear time series analysis of the human electrocardiogram

    International Nuclear Information System (INIS)

    Perc, Matjaz

    2005-01-01

    We analyse the human electrocardiogram with simple nonlinear time series analysis methods that are appropriate for graduate as well as undergraduate courses. In particular, attention is devoted to the notions of determinism and stationarity in physiological data. We emphasize that methods of nonlinear time series analysis can be successfully applied only if the studied data set originates from a deterministic stationary system. After positively establishing the presence of determinism and stationarity in the studied electrocardiogram, we calculate the maximal Lyapunov exponent, thus providing interesting insights into the dynamics of the human heart. Moreover, to facilitate interest and enable the integration of nonlinear time series analysis methods into the curriculum at an early stage of the educational process, we also provide user-friendly programs for each implemented method

  8. Els aplecs «ts» i «dz» en valencià. Una anàlisi des de la teoria de l'optimitat

    Directory of Open Access Journals (Sweden)

    Jesús Jiménez

    2015-10-01

    Full Text Available This article intends to demonstrate that the final form of the clusters /ts/ and /dz/ in the Valencian dialect depends upon the behaviour of a series of universal principles referring to the syllabic structure. The author assumes that such restrictions are ranked on a language particular basis, according to the postulates of Optimality Theory (Prince & Smolensky 1993. Then, the outputs for the alveolar clusters will be the result of applying the constraint hierarchy to the set of possible candidates consistent with every input. The different results of the clusters /ts/ and /dz/ in Valencian, and this dialect' s divergences from other Catalan varieties are explained by the differences in the ranking of the principles which each variety presents.

  9. Temperature-sensitive mutants of influenza A virus. XIV. Production and evaluation of influenza A/Georgia/74-ts-1[E] recombinant viruses in human adults.

    Science.gov (United States)

    Richman, D D; Murphy, B R; Belshe, R B; Rusten, H M; Chanock, R M; Blacklow, N R; Parrino, T A; Rose, F B; Levine, M M; Caplan, E

    1977-08-01

    The two temperature-sensitive (ts) lesions present in influenza A/Hong Kong/68-ts-1[E] (H3N2 68) virus were transferred via genetic reassortment to influenza A/Georgia/74 (H3N2 74) wild-type virus. A recombinant clone possessing both ts lesions and the shutoff temperature of 38 C of the Hong Kong/68 ts donor and the two surface antigens of the Georgia/74 wild-type virus was administered to 32 seronegative adult volunteers. Thirty-one volunteers were infected, of whom only five experienced mild afebrile upper respiratory tract illness. The wild-type recipient virus was a cloned population that induced illness in five of six infected volunteers. Therfore, the attenuation exhibited by the Georgia/74-ts-1[E] virus could reasonably be assumed to be due to the acquisition of the two ts-1[E] lesions by the Georgia/74 wild-type virus. The serum and nasal wash antibody responses of the ts-1[E] vaccinees were equivalent to those of the volunteers who received wild-type virus. The two ts lesions present in the Hong Kong/68-ts-1[E] virus have now been transferred three times to a wild-type virus bearing a new hemagglutinin, and in each instance the new ts recombination exhibited a similar, satisfactory level of attenuation and antigenicity for adults. It seems likely that the transfer of the ts-1[E] lesions to any new influenza virus will regularly result in attenuation of a recombinat virus possessing the new surface antigens.

  10. Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis.

    Science.gov (United States)

    Wang, Jin; Sun, Xiangping; Nahavandi, Saeid; Kouzani, Abbas; Wu, Yuchuan; She, Mary

    2014-11-01

    Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  11. Hidden Markov Models for Time Series An Introduction Using R

    CERN Document Server

    Zucchini, Walter

    2009-01-01

    Illustrates the flexibility of HMMs as general-purpose models for time series data. This work presents an overview of HMMs for analyzing time series data, from continuous-valued, circular, and multivariate series to binary data, bounded and unbounded counts and categorical observations.

  12. Constructing ordinal partition transition networks from multivariate time series.

    Science.gov (United States)

    Zhang, Jiayang; Zhou, Jie; Tang, Ming; Guo, Heng; Small, Michael; Zou, Yong

    2017-08-10

    A growing number of algorithms have been proposed to map a scalar time series into ordinal partition transition networks. However, most observable phenomena in the empirical sciences are of a multivariate nature. We construct ordinal partition transition networks for multivariate time series. This approach yields weighted directed networks representing the pattern transition properties of time series in velocity space, which hence provides dynamic insights of the underling system. Furthermore, we propose a measure of entropy to characterize ordinal partition transition dynamics, which is sensitive to capturing the possible local geometric changes of phase space trajectories. We demonstrate the applicability of pattern transition networks to capture phase coherence to non-coherence transitions, and to characterize paths to phase synchronizations. Therefore, we conclude that the ordinal partition transition network approach provides complementary insight to the traditional symbolic analysis of nonlinear multivariate time series.

  13. Permutation entropy of finite-length white-noise time series.

    Science.gov (United States)

    Little, Douglas J; Kane, Deb M

    2016-08-01

    Permutation entropy (PE) is commonly used to discriminate complex structure from white noise in a time series. While the PE of white noise is well understood in the long time-series limit, analysis in the general case is currently lacking. Here the expectation value and variance of white-noise PE are derived as functions of the number of ordinal pattern trials, N, and the embedding dimension, D. It is demonstrated that the probability distribution of the white-noise PE converges to a χ^{2} distribution with D!-1 degrees of freedom as N becomes large. It is further demonstrated that the PE variance for an arbitrary time series can be estimated as the variance of a related metric, the Kullback-Leibler entropy (KLE), allowing the qualitative N≫D! condition to be recast as a quantitative estimate of the N required to achieve a desired PE calculation precision. Application of this theory to statistical inference is demonstrated in the case of an experimentally obtained noise series, where the probability of obtaining the observed PE value was calculated assuming a white-noise time series. Standard statistical inference can be used to draw conclusions whether the white-noise null hypothesis can be accepted or rejected. This methodology can be applied to other null hypotheses, such as discriminating whether two time series are generated from different complex system states.

  14. Multiresolution analysis of Bursa Malaysia KLCI time series

    Science.gov (United States)

    Ismail, Mohd Tahir; Dghais, Amel Abdoullah Ahmed

    2017-05-01

    In general, a time series is simply a sequence of numbers collected at regular intervals over a period. Financial time series data processing is concerned with the theory and practice of processing asset price over time, such as currency, commodity data, and stock market data. The primary aim of this study is to understand the fundamental characteristics of selected financial time series by using the time as well as the frequency domain analysis. After that prediction can be executed for the desired system for in sample forecasting. In this study, multiresolution analysis which the assist of discrete wavelet transforms (DWT) and maximal overlap discrete wavelet transform (MODWT) will be used to pinpoint special characteristics of Bursa Malaysia KLCI (Kuala Lumpur Composite Index) daily closing prices and return values. In addition, further case study discussions include the modeling of Bursa Malaysia KLCI using linear ARIMA with wavelets to address how multiresolution approach improves fitting and forecasting results.

  15. Modelling bursty time series

    International Nuclear Information System (INIS)

    Vajna, Szabolcs; Kertész, János; Tóth, Bálint

    2013-01-01

    Many human-related activities show power-law decaying interevent time distribution with exponents usually varying between 1 and 2. We study a simple task-queuing model, which produces bursty time series due to the non-trivial dynamics of the task list. The model is characterized by a priority distribution as an input parameter, which describes the choice procedure from the list. We give exact results on the asymptotic behaviour of the model and we show that the interevent time distribution is power-law decaying for any kind of input distributions that remain normalizable in the infinite list limit, with exponents tunable between 1 and 2. The model satisfies a scaling law between the exponents of interevent time distribution (β) and autocorrelation function (α): α + β = 2. This law is general for renewal processes with power-law decaying interevent time distribution. We conclude that slowly decaying autocorrelation function indicates long-range dependence only if the scaling law is violated. (paper)

  16. Timing calibration and spectral cleaning of LOFAR time series data

    NARCIS (Netherlands)

    Corstanje, A.; Buitink, S.; Enriquez, J. E.; Falcke, H.; Horandel, J. R.; Krause, M.; Nelles, A.; Rachen, J. P.; Schellart, P.; Scholten, O.; ter Veen, S.; Thoudam, S.; Trinh, T. N. G.

    We describe a method for spectral cleaning and timing calibration of short time series data of the voltage in individual radio interferometer receivers. It makes use of phase differences in fast Fourier transform (FFT) spectra across antenna pairs. For strong, localized terrestrial sources these are

  17. Time series momentum and contrarian effects in the Chinese stock market

    Science.gov (United States)

    Shi, Huai-Long; Zhou, Wei-Xing

    2017-10-01

    This paper concentrates on the time series momentum or contrarian effects in the Chinese stock market. We evaluate the performance of the time series momentum strategy applied to major stock indices in mainland China and explore the relation between the performance of time series momentum strategies and some firm-specific characteristics. Our findings indicate that there is a time series momentum effect in the short run and a contrarian effect in the long run in the Chinese stock market. The performances of the time series momentum and contrarian strategies are highly dependent on the look-back and holding periods and firm-specific characteristics.

  18. Time-Series Analysis: A Cautionary Tale

    Science.gov (United States)

    Damadeo, Robert

    2015-01-01

    Time-series analysis has often been a useful tool in atmospheric science for deriving long-term trends in various atmospherically important parameters (e.g., temperature or the concentration of trace gas species). In particular, time-series analysis has been repeatedly applied to satellite datasets in order to derive the long-term trends in stratospheric ozone, which is a critical atmospheric constituent. However, many of the potential pitfalls relating to the non-uniform sampling of the datasets were often ignored and the results presented by the scientific community have been unknowingly biased. A newly developed and more robust application of this technique is applied to the Stratospheric Aerosol and Gas Experiment (SAGE) II version 7.0 ozone dataset and the previous biases and newly derived trends are presented.

  19. Characterizing interdependencies of multiple time series theory and applications

    CERN Document Server

    Hosoya, Yuzo; Takimoto, Taro; Kinoshita, Ryo

    2017-01-01

    This book introduces academic researchers and professionals to the basic concepts and methods for characterizing interdependencies of multiple time series in the frequency domain. Detecting causal directions between a pair of time series and the extent of their effects, as well as testing the non existence of a feedback relation between them, have constituted major focal points in multiple time series analysis since Granger introduced the celebrated definition of causality in view of prediction improvement. Causality analysis has since been widely applied in many disciplines. Although most analyses are conducted from the perspective of the time domain, a frequency domain method introduced in this book sheds new light on another aspect that disentangles the interdependencies between multiple time series in terms of long-term or short-term effects, quantitatively characterizing them. The frequency domain method includes the Granger noncausality test as a special case. Chapters 2 and 3 of the book introduce an i...

  20. A perturbative approach for enhancing the performance of time series forecasting.

    Science.gov (United States)

    de Mattos Neto, Paulo S G; Ferreira, Tiago A E; Lima, Aranildo R; Vasconcelos, Germano C; Cavalcanti, George D C

    2017-04-01

    This paper proposes a method to perform time series prediction based on perturbation theory. The approach is based on continuously adjusting an initial forecasting model to asymptotically approximate a desired time series model. First, a predictive model generates an initial forecasting for a time series. Second, a residual time series is calculated as the difference between the original time series and the initial forecasting. If that residual series is not white noise, then it can be used to improve the accuracy of the initial model and a new predictive model is adjusted using residual series. The whole process is repeated until convergence or the residual series becomes white noise. The output of the method is then given by summing up the outputs of all trained predictive models in a perturbative sense. To test the method, an experimental investigation was conducted on six real world time series. A comparison was made with six other methods experimented and ten other results found in the literature. Results show that not only the performance of the initial model is significantly improved but also the proposed method outperforms the other results previously published. Copyright © 2017 Elsevier Ltd. All rights reserved.

  1. Drunk driving detection based on classification of multivariate time series.

    Science.gov (United States)

    Li, Zhenlong; Jin, Xue; Zhao, Xiaohua

    2015-09-01

    This paper addresses the problem of detecting drunk driving based on classification of multivariate time series. First, driving performance measures were collected from a test in a driving simulator located in the Traffic Research Center, Beijing University of Technology. Lateral position and steering angle were used to detect drunk driving. Second, multivariate time series analysis was performed to extract the features. A piecewise linear representation was used to represent multivariate time series. A bottom-up algorithm was then employed to separate multivariate time series. The slope and time interval of each segment were extracted as the features for classification. Third, a support vector machine classifier was used to classify driver's state into two classes (normal or drunk) according to the extracted features. The proposed approach achieved an accuracy of 80.0%. Drunk driving detection based on the analysis of multivariate time series is feasible and effective. The approach has implications for drunk driving detection. Copyright © 2015 Elsevier Ltd and National Safety Council. All rights reserved.

  2. Evaluation of scaling invariance embedded in short time series.

    Directory of Open Access Journals (Sweden)

    Xue Pan

    Full Text Available Scaling invariance of time series has been making great contributions in diverse research fields. But how to evaluate scaling exponent from a real-world series is still an open problem. Finite length of time series may induce unacceptable fluctuation and bias to statistical quantities and consequent invalidation of currently used standard methods. In this paper a new concept called correlation-dependent balanced estimation of diffusion entropy is developed to evaluate scale-invariance in very short time series with length ~10(2. Calculations with specified Hurst exponent values of 0.2,0.3,...,0.9 show that by using the standard central moving average de-trending procedure this method can evaluate the scaling exponents for short time series with ignorable bias (≤0.03 and sharp confidential interval (standard deviation ≤0.05. Considering the stride series from ten volunteers along an approximate oval path of a specified length, we observe that though the averages and deviations of scaling exponents are close, their evolutionary behaviors display rich patterns. It has potential use in analyzing physiological signals, detecting early warning signals, and so on. As an emphasis, the our core contribution is that by means of the proposed method one can estimate precisely shannon entropy from limited records.

  3. Evaluation of scaling invariance embedded in short time series.

    Science.gov (United States)

    Pan, Xue; Hou, Lei; Stephen, Mutua; Yang, Huijie; Zhu, Chenping

    2014-01-01

    Scaling invariance of time series has been making great contributions in diverse research fields. But how to evaluate scaling exponent from a real-world series is still an open problem. Finite length of time series may induce unacceptable fluctuation and bias to statistical quantities and consequent invalidation of currently used standard methods. In this paper a new concept called correlation-dependent balanced estimation of diffusion entropy is developed to evaluate scale-invariance in very short time series with length ~10(2). Calculations with specified Hurst exponent values of 0.2,0.3,...,0.9 show that by using the standard central moving average de-trending procedure this method can evaluate the scaling exponents for short time series with ignorable bias (≤0.03) and sharp confidential interval (standard deviation ≤0.05). Considering the stride series from ten volunteers along an approximate oval path of a specified length, we observe that though the averages and deviations of scaling exponents are close, their evolutionary behaviors display rich patterns. It has potential use in analyzing physiological signals, detecting early warning signals, and so on. As an emphasis, the our core contribution is that by means of the proposed method one can estimate precisely shannon entropy from limited records.

  4. Modeling Non-Gaussian Time Series with Nonparametric Bayesian Model.

    Science.gov (United States)

    Xu, Zhiguang; MacEachern, Steven; Xu, Xinyi

    2015-02-01

    We present a class of Bayesian copula models whose major components are the marginal (limiting) distribution of a stationary time series and the internal dynamics of the series. We argue that these are the two features with which an analyst is typically most familiar, and hence that these are natural components with which to work. For the marginal distribution, we use a nonparametric Bayesian prior distribution along with a cdf-inverse cdf transformation to obtain large support. For the internal dynamics, we rely on the traditionally successful techniques of normal-theory time series. Coupling the two components gives us a family of (Gaussian) copula transformed autoregressive models. The models provide coherent adjustments of time scales and are compatible with many extensions, including changes in volatility of the series. We describe basic properties of the models, show their ability to recover non-Gaussian marginal distributions, and use a GARCH modification of the basic model to analyze stock index return series. The models are found to provide better fit and improved short-range and long-range predictions than Gaussian competitors. The models are extensible to a large variety of fields, including continuous time models, spatial models, models for multiple series, models driven by external covariate streams, and non-stationary models.

  5. Geomechanical time series and its singularity spectrum analysis

    Czech Academy of Sciences Publication Activity Database

    Lyubushin, Alexei A.; Kaláb, Zdeněk; Lednická, Markéta

    2012-01-01

    Roč. 47, č. 1 (2012), s. 69-77 ISSN 1217-8977 R&D Projects: GA ČR GA105/09/0089 Institutional research plan: CEZ:AV0Z30860518 Keywords : geomechanical time series * singularity spectrum * time series segmentation * laser distance meter Subject RIV: DC - Siesmology, Volcanology, Earth Structure Impact factor: 0.347, year: 2012 http://www.akademiai.com/content/88v4027758382225/fulltext.pdf

  6. Pseudo-random bit generator based on lag time series

    Science.gov (United States)

    García-Martínez, M.; Campos-Cantón, E.

    2014-12-01

    In this paper, we present a pseudo-random bit generator (PRBG) based on two lag time series of the logistic map using positive and negative values in the bifurcation parameter. In order to hidden the map used to build the pseudo-random series we have used a delay in the generation of time series. These new series when they are mapped xn against xn+1 present a cloud of points unrelated to the logistic map. Finally, the pseudo-random sequences have been tested with the suite of NIST giving satisfactory results for use in stream ciphers.

  7. Non-linear forecasting in high-frequency financial time series

    Science.gov (United States)

    Strozzi, F.; Zaldívar, J. M.

    2005-08-01

    A new methodology based on state space reconstruction techniques has been developed for trading in financial markets. The methodology has been tested using 18 high-frequency foreign exchange time series. The results are in apparent contradiction with the efficient market hypothesis which states that no profitable information about future movements can be obtained by studying the past prices series. In our (off-line) analysis positive gain may be obtained in all those series. The trading methodology is quite general and may be adapted to other financial time series. Finally, the steps for its on-line application are discussed.

  8. ChemTS: an efficient python library for de novo molecular generation

    Science.gov (United States)

    Yang, Xiufeng; Zhang, Jinzhe; Yoshizoe, Kazuki; Terayama, Kei; Tsuda, Koji

    2017-12-01

    Automatic design of organic materials requires black-box optimization in a vast chemical space. In conventional molecular design algorithms, a molecule is built as a combination of predetermined fragments. Recently, deep neural network models such as variational autoencoders and recurrent neural networks (RNNs) are shown to be effective in de novo design of molecules without any predetermined fragments. This paper presents a novel Python library ChemTS that explores the chemical space by combining Monte Carlo tree search and an RNN. In a benchmarking problem of optimizing the octanol-water partition coefficient and synthesizability, our algorithm showed superior efficiency in finding high-scoring molecules. ChemTS is available at https://github.com/tsudalab/ChemTS.

  9. Analysis of JET ELMy time series

    International Nuclear Information System (INIS)

    Zvejnieks, G.; Kuzovkov, V.N.

    2005-01-01

    Full text: Achievement of the planned operational regime in the next generation tokamaks (such as ITER) still faces principal problems. One of the main challenges is obtaining the control of edge localized modes (ELMs), which should lead to both long plasma pulse times and reasonable divertor life time. In order to control ELMs the hypothesis was proposed by Degeling [1] that ELMs exhibit features of chaotic dynamics and thus a standard chaos control methods might be applicable. However, our findings which are based on the nonlinear autoregressive (NAR) model contradict this hypothesis for JET ELMy time-series. In turn, it means that ELM behavior is of a relaxation or random type. These conclusions coincide with our previous results obtained for ASDEX Upgrade time series [2]. [1] A.W. Degeling, Y.R. Martin, P.E. Bak, J. B.Lister, and X. Llobet, Plasma Phys. Control. Fusion 43, 1671 (2001). [2] G. Zvejnieks, V.N. Kuzovkov, O. Dumbrajs, A.W. Degeling, W. Suttrop, H. Urano, and H. Zohm, Physics of Plasmas 11, 5658 (2004)

  10. Kunstisuvi 1999 / Sirje Eelma, Rait Prääts

    Index Scriptorium Estoniae

    Eelma, Sirje, 1950-

    1999-01-01

    12. juunist näitus "Rahvusvaheline väikegraafika ja eesti väikeskulptuur" Kuressaare Raegaleriis. Koostajad ja kujundajad Sirje Eelma, Terje Ojaver. 19. juunist Haapsalu Linnagaleriis ja lisaruumides väljapanek "Motiiv". Koostajad Agur Kruusing, Sirje Eelma, Rait Prääts, kujundaja Agur Kruusing. Pärnu Linnagaleriis näitus "Minimaal". Koostaja ja kujundaja Rait Prääts. Muuseumiaidas eesti noorema kunsti väljapanek "Voolab". Koostaja Karol Kallas. Juuli lõpul Selgase dolomiidipäevad Kuressaares. Pärast põhinäituste lõppemist valiknäitus Paide Vallitornis

  11. The Statistical Analysis of Time Series

    CERN Document Server

    Anderson, T W

    2011-01-01

    The Wiley Classics Library consists of selected books that have become recognized classics in their respective fields. With these new unabridged and inexpensive editions, Wiley hopes to extend the life of these important works by making them available to future generations of mathematicians and scientists. Currently available in the Series: T. W. Anderson Statistical Analysis of Time Series T. S. Arthanari & Yadolah Dodge Mathematical Programming in Statistics Emil Artin Geometric Algebra Norman T. J. Bailey The Elements of Stochastic Processes with Applications to the Natural Sciences George

  12. Analysis of time series and size of equivalent sample

    International Nuclear Information System (INIS)

    Bernal, Nestor; Molina, Alicia; Pabon, Daniel; Martinez, Jorge

    2004-01-01

    In a meteorological context, a first approach to the modeling of time series is to use models of autoregressive type. This allows one to take into account the meteorological persistence or temporal behavior, thereby identifying the memory of the analyzed process. This article seeks to pre-sent the concept of the size of an equivalent sample, which helps to identify in the data series sub periods with a similar structure. Moreover, in this article we examine the alternative of adjusting the variance of the series, keeping in mind its temporal structure, as well as an adjustment to the covariance of two time series. This article presents two examples, the first one corresponding to seven simulated series with autoregressive structure of first order, and the second corresponding to seven meteorological series of anomalies of the air temperature at the surface in two Colombian regions

  13. Geomagnetic activity at Northern Hemisphere's mid-latitude ground stations: How much can be explained using TS05 model

    Science.gov (United States)

    Castillo, Yvelice; Pais, Maria Alexandra; Fernandes, João; Ribeiro, Paulo; Morozova, Anna L.; Pinheiro, Fernando J. G.

    2017-12-01

    For the 2007 to 2014 period, we use a statistical approach to evaluate the performance of Tsyganenko and Sitnov [2005] semi-empirical model (TS05) in estimating the magnetospheric transient signal observed at four Northern Hemisphere mid-latitude ground stations: Coimbra, Portugal; Panagyurishte, Bulgary; Novosibirsk, Russia and Boulder, USA. Using hourly mean data, we find that the TS05 performance is clearly better for the X (North-South) than for the Y (East-West) field components and for more geomagnetically active days as determined by local K-indices. In ∼ 50% (X) and ∼ 30% (Y) of the total number of geomagnetically active days, correlation values yield r ≥ 0.7. During more quiet conditions, only ∼ 30% (X) and ∼ 15% (Y) of the number of analyzed days yield r ≥ 0.7. We compute separate contributions from different magnetospheric currents to data time variability and to signal magnitude. During more active days, all tail, symmetric ring and partial ring currents contribute to the time variability of X while the partial ring and field aligned currents contribute most to the time variability of Y. The tail and symmetric ring currents are main contributors to the magnitude of X. In the best case estimations when r ≥ 0.7, remaining differences between observations and TS05 predictions could be explained by global induction in the Earth's upper layers and crustal magnetization. The closing of field aligned currents through the Earth's center in the TS05 model seems to be mainly affecting the Y magnetospheric field predictions.

  14. An improvement of the filter diagonalization-based post-processing method applied to finite difference time domain calculations of three-dimensional phononic band structures

    International Nuclear Information System (INIS)

    Su Xiaoxing; Zhang Chuanzeng; Ma Tianxue; Wang Yuesheng

    2012-01-01

    When three-dimensional (3D) phononic band structures are calculated by using the finite difference time domain (FDTD) method with a relatively small number of iterations, the results can be effectively improved by post-processing the FDTD time series (FDTD-TS) based on the filter diagonalization method (FDM), instead of the classical fast Fourier transform. In this paper, we propose a way to further improve the performance of the FDM-based post-processing method by introducing a relatively large number of observing points to record the FDTD-TS. To this end, the existing scheme of FDTD-TS preprocessing is modified. With the new preprocessing scheme, the processing efficiency of a single FDTD-TS can be improved significantly, and thus the entire post-processing method can have sufficiently high efficiency even when a relatively large number of observing points are used. The feasibility of the proposed method for improvement is verified by the numerical results.

  15. Scalable Prediction of Energy Consumption using Incremental Time Series Clustering

    Energy Technology Data Exchange (ETDEWEB)

    Simmhan, Yogesh; Noor, Muhammad Usman

    2013-10-09

    Time series datasets are a canonical form of high velocity Big Data, and often generated by pervasive sensors, such as found in smart infrastructure. Performing predictive analytics on time series data can be computationally complex, and requires approximation techniques. In this paper, we motivate this problem using a real application from the smart grid domain. We propose an incremental clustering technique, along with a novel affinity score for determining cluster similarity, which help reduce the prediction error for cumulative time series within a cluster. We evaluate this technique, along with optimizations, using real datasets from smart meters, totaling ~700,000 data points, and show the efficacy of our techniques in improving the prediction error of time series data within polynomial time.

  16. BlendCAC: A BLockchain-ENabled Decentralized Capability-based Access Control for IoTs

    OpenAIRE

    Xu, Ronghua; Chen, Yu; Blasch, Erik; Chen, Genshe

    2018-01-01

    The prevalence of Internet of Things (IoTs) allows heterogeneous embedded smart devices to collaboratively provide smart services with or without human intervention. While leveraging the large scale IoT based applications like Smart Gird or Smart Cities, IoTs also incur more concerns on privacy and security. Among the top security challenges that IoTs face, access authorization is critical in resource sharing and information protection. One of the weaknesses in today's access control (AC) is ...

  17. A Federated Capability-based Access Control Mechanism for Internet of Things (IoTs)

    OpenAIRE

    Xu, Ronghua; Chen, Yu; Blasch, Erik; Chen, Genshe

    2018-01-01

    The prevalence of Internet of Things (IoTs) allows heterogeneous embedded smart devices to collaboratively provide intelligent services with or without human intervention. While leveraging the large-scale IoT-based applications like Smart Gird and Smart Cities, IoT also incurs more concerns on privacy and security. Among the top security challenges that IoTs face is that access authorization is critical in resource and information protection over IoTs. Traditional access control approaches, l...

  18. Forecasting with nonlinear time series models

    DEFF Research Database (Denmark)

    Kock, Anders Bredahl; Teräsvirta, Timo

    In this paper, nonlinear models are restricted to mean nonlinear parametric models. Several such models popular in time series econo- metrics are presented and some of their properties discussed. This in- cludes two models based on universal approximators: the Kolmogorov- Gabor polynomial model...... applied to economic fore- casting problems, is briefly highlighted. A number of large published studies comparing macroeconomic forecasts obtained using different time series models are discussed, and the paper also contains a small simulation study comparing recursive and direct forecasts in a partic...... and two versions of a simple artificial neural network model. Techniques for generating multi-period forecasts from nonlinear models recursively are considered, and the direct (non-recursive) method for this purpose is mentioned as well. Forecasting with com- plex dynamic systems, albeit less frequently...

  19. Nonparametric factor analysis of time series

    OpenAIRE

    Rodríguez-Poo, Juan M.; Linton, Oliver Bruce

    1998-01-01

    We introduce a nonparametric smoothing procedure for nonparametric factor analaysis of multivariate time series. The asymptotic properties of the proposed procedures are derived. We present an application based on the residuals from the Fair macromodel.

  20. Time Series Outlier Detection Based on Sliding Window Prediction

    Directory of Open Access Journals (Sweden)

    Yufeng Yu

    2014-01-01

    Full Text Available In order to detect outliers in hydrological time series data for improving data quality and decision-making quality related to design, operation, and management of water resources, this research develops a time series outlier detection method for hydrologic data that can be used to identify data that deviate from historical patterns. The method first built a forecasting model on the history data and then used it to predict future values. Anomalies are assumed to take place if the observed values fall outside a given prediction confidence interval (PCI, which can be calculated by the predicted value and confidence coefficient. The use of PCI as threshold is mainly on the fact that it considers the uncertainty in the data series parameters in the forecasting model to address the suitable threshold selection problem. The method performs fast, incremental evaluation of data as it becomes available, scales to large quantities of data, and requires no preclassification of anomalies. Experiments with different hydrologic real-world time series showed that the proposed methods are fast and correctly identify abnormal data and can be used for hydrologic time series analysis.

  1. Metagenomics meets time series analysis: unraveling microbial community dynamics

    NARCIS (Netherlands)

    Faust, K.; Lahti, L.M.; Gonze, D.; Vos, de W.M.; Raes, J.

    2015-01-01

    The recent increase in the number of microbial time series studies offers new insights into the stability and dynamics of microbial communities, from the world's oceans to human microbiota. Dedicated time series analysis tools allow taking full advantage of these data. Such tools can reveal periodic

  2. Time series forecasting based on deep extreme learning machine

    NARCIS (Netherlands)

    Guo, Xuqi; Pang, Y.; Yan, Gaowei; Qiao, Tiezhu; Yang, Guang-Hong; Yang, Dan

    2017-01-01

    Multi-layer Artificial Neural Networks (ANN) has caught widespread attention as a new method for time series forecasting due to the ability of approximating any nonlinear function. In this paper, a new local time series prediction model is established with the nearest neighbor domain theory, in

  3. False-nearest-neighbors algorithm and noise-corrupted time series

    International Nuclear Information System (INIS)

    Rhodes, C.; Morari, M.

    1997-01-01

    The false-nearest-neighbors (FNN) algorithm was originally developed to determine the embedding dimension for autonomous time series. For noise-free computer-generated time series, the algorithm does a good job in predicting the embedding dimension. However, the problem of predicting the embedding dimension when the time-series data are corrupted by noise was not fully examined in the original studies of the FNN algorithm. Here it is shown that with large data sets, even small amounts of noise can lead to incorrect prediction of the embedding dimension. Surprisingly, as the length of the time series analyzed by FNN grows larger, the cause of incorrect prediction becomes more pronounced. An analysis of the effect of noise on the FNN algorithm and a solution for dealing with the effects of noise are given here. Some results on the theoretically correct choice of the FNN threshold are also presented. copyright 1997 The American Physical Society

  4. Measurement of the CKM matrix element |V_ts

    CERN Document Server

    Unverdorben, Christopher Gerhard

    This is the first direct measurement of the CKM matrix element |V_ts|, using data collected by the ATLAS detector in 2012 at √s=8 TeV pp-collisions with a total integrated luminosity of 20.3 fb⁻¹. The analysis is based on 112171 reconstructed tt̅ candidate events in the lepton+jets channel, having a purity of 90.0 %. 183 tt̅→WWbs̅ decays are expected (charge conjugation implied), which are available for the extraction of the CKM matrix element |V_ts|². To identify these rare decays, several observables are examined, such as the properties of jets, tracks and of b-quark identification algorithms. Furthermore, the s-quark hadrons K0s are considered, reconstructed by a kinematic fit. The best observables are combined in a multivariate analysis, called "boosted decision trees". The responses from Monte Carlo simulations are used as templates for a fit to data events yielding a significance value of 0.7σ for t→s+W decays. An upper limit of |V_ts|² < 1.74 % at 95 % confidence level is set, includi...

  5. CauseMap: fast inference of causality from complex time series.

    Science.gov (United States)

    Maher, M Cyrus; Hernandez, Ryan D

    2015-01-01

    Background. Establishing health-related causal relationships is a central pursuit in biomedical research. Yet, the interdependent non-linearity of biological systems renders causal dynamics laborious and at times impractical to disentangle. This pursuit is further impeded by the dearth of time series that are sufficiently long to observe and understand recurrent patterns of flux. However, as data generation costs plummet and technologies like wearable devices democratize data collection, we anticipate a coming surge in the availability of biomedically-relevant time series data. Given the life-saving potential of these burgeoning resources, it is critical to invest in the development of open source software tools that are capable of drawing meaningful insight from vast amounts of time series data. Results. Here we present CauseMap, the first open source implementation of convergent cross mapping (CCM), a method for establishing causality from long time series data (≳25 observations). Compared to existing time series methods, CCM has the advantage of being model-free and robust to unmeasured confounding that could otherwise induce spurious associations. CCM builds on Takens' Theorem, a well-established result from dynamical systems theory that requires only mild assumptions. This theorem allows us to reconstruct high dimensional system dynamics using a time series of only a single variable. These reconstructions can be thought of as shadows of the true causal system. If reconstructed shadows can predict points from opposing time series, we can infer that the corresponding variables are providing views of the same causal system, and so are causally related. Unlike traditional metrics, this test can establish the directionality of causation, even in the presence of feedback loops. Furthermore, since CCM can extract causal relationships from times series of, e.g., a single individual, it may be a valuable tool to personalized medicine. We implement CCM in Julia, a

  6. CauseMap: fast inference of causality from complex time series

    Directory of Open Access Journals (Sweden)

    M. Cyrus Maher

    2015-03-01

    Full Text Available Background. Establishing health-related causal relationships is a central pursuit in biomedical research. Yet, the interdependent non-linearity of biological systems renders causal dynamics laborious and at times impractical to disentangle. This pursuit is further impeded by the dearth of time series that are sufficiently long to observe and understand recurrent patterns of flux. However, as data generation costs plummet and technologies like wearable devices democratize data collection, we anticipate a coming surge in the availability of biomedically-relevant time series data. Given the life-saving potential of these burgeoning resources, it is critical to invest in the development of open source software tools that are capable of drawing meaningful insight from vast amounts of time series data.Results. Here we present CauseMap, the first open source implementation of convergent cross mapping (CCM, a method for establishing causality from long time series data (≳25 observations. Compared to existing time series methods, CCM has the advantage of being model-free and robust to unmeasured confounding that could otherwise induce spurious associations. CCM builds on Takens’ Theorem, a well-established result from dynamical systems theory that requires only mild assumptions. This theorem allows us to reconstruct high dimensional system dynamics using a time series of only a single variable. These reconstructions can be thought of as shadows of the true causal system. If reconstructed shadows can predict points from opposing time series, we can infer that the corresponding variables are providing views of the same causal system, and so are causally related. Unlike traditional metrics, this test can establish the directionality of causation, even in the presence of feedback loops. Furthermore, since CCM can extract causal relationships from times series of, e.g., a single individual, it may be a valuable tool to personalized medicine. We implement

  7. A novel method of TVTS in the TS-3 device and the proposal of its application to a large device

    International Nuclear Information System (INIS)

    Tokimatsu, K.; Hayashi, N.; Ueda, Y.; Ono, Y.; Katsurai, M.

    1997-01-01

    A novel method of television Thomson scattering (TVTS) has been developed in the TS-3 device. In this system, a framing camera is located between a spectroscope and a close coupled device camera. This framing camera can take two successive frames from the exit of the spectroscope. The time interval is of 500 ns between those two frames. Because of the shortness of this time interval, the background light is negligible; moreover TVTS is applicable to the TS-3 device whose plasma lifetime is about 150 μs. This method indicates the possibility of not only high spatial resolution but also time repetition in a simple system. (orig.)

  8. Time domain series system definition and gear set reliability modeling

    International Nuclear Information System (INIS)

    Xie, Liyang; Wu, Ningxiang; Qian, Wenxue

    2016-01-01

    Time-dependent multi-configuration is a typical feature for mechanical systems such as gear trains and chain drives. As a series system, a gear train is distinct from a traditional series system, such as a chain, in load transmission path, system-component relationship, system functioning manner, as well as time-dependent system configuration. Firstly, the present paper defines time-domain series system to which the traditional series system reliability model is not adequate. Then, system specific reliability modeling technique is proposed for gear sets, including component (tooth) and subsystem (tooth-pair) load history description, material priori/posterior strength expression, time-dependent and system specific load-strength interference analysis, as well as statistically dependent failure events treatment. Consequently, several system reliability models are developed for gear sets with different tooth numbers in the scenario of tooth root material ultimate tensile strength failure. The application of the models is discussed in the last part, and the differences between the system specific reliability model and the traditional series system reliability model are illustrated by virtue of several numerical examples. - Highlights: • A new type of series system, i.e. time-domain multi-configuration series system is defined, that is of great significance to reliability modeling. • Multi-level statistical analysis based reliability modeling method is presented for gear transmission system. • Several system specific reliability models are established for gear set reliability estimation. • The differences between the traditional series system reliability model and the new model are illustrated.

  9. Track Irregularity Time Series Analysis and Trend Forecasting

    Directory of Open Access Journals (Sweden)

    Jia Chaolong

    2012-01-01

    Full Text Available The combination of linear and nonlinear methods is widely used in the prediction of time series data. This paper analyzes track irregularity time series data by using gray incidence degree models and methods of data transformation, trying to find the connotative relationship between the time series data. In this paper, GM (1,1 is based on first-order, single variable linear differential equations; after an adaptive improvement and error correction, it is used to predict the long-term changing trend of track irregularity at a fixed measuring point; the stochastic linear AR, Kalman filtering model, and artificial neural network model are applied to predict the short-term changing trend of track irregularity at unit section. Both long-term and short-term changes prove that the model is effective and can achieve the expected accuracy.

  10. PRESEE: an MDL/MML algorithm to time-series stream segmenting.

    Science.gov (United States)

    Xu, Kaikuo; Jiang, Yexi; Tang, Mingjie; Yuan, Changan; Tang, Changjie

    2013-01-01

    Time-series stream is one of the most common data types in data mining field. It is prevalent in fields such as stock market, ecology, and medical care. Segmentation is a key step to accelerate the processing speed of time-series stream mining. Previous algorithms for segmenting mainly focused on the issue of ameliorating precision instead of paying much attention to the efficiency. Moreover, the performance of these algorithms depends heavily on parameters, which are hard for the users to set. In this paper, we propose PRESEE (parameter-free, real-time, and scalable time-series stream segmenting algorithm), which greatly improves the efficiency of time-series stream segmenting. PRESEE is based on both MDL (minimum description length) and MML (minimum message length) methods, which could segment the data automatically. To evaluate the performance of PRESEE, we conduct several experiments on time-series streams of different types and compare it with the state-of-art algorithm. The empirical results show that PRESEE is very efficient for real-time stream datasets by improving segmenting speed nearly ten times. The novelty of this algorithm is further demonstrated by the application of PRESEE in segmenting real-time stream datasets from ChinaFLUX sensor networks data stream.

  11. Time-varying surrogate data to assess nonlinearity in nonstationary time series: application to heart rate variability.

    Science.gov (United States)

    Faes, Luca; Zhao, He; Chon, Ki H; Nollo, Giandomenico

    2009-03-01

    We propose a method to extend to time-varying (TV) systems the procedure for generating typical surrogate time series, in order to test the presence of nonlinear dynamics in potentially nonstationary signals. The method is based on fitting a TV autoregressive (AR) model to the original series and then regressing the model coefficients with random replacements of the model residuals to generate TV AR surrogate series. The proposed surrogate series were used in combination with a TV sample entropy (SE) discriminating statistic to assess nonlinearity in both simulated and experimental time series, in comparison with traditional time-invariant (TIV) surrogates combined with the TIV SE discriminating statistic. Analysis of simulated time series showed that using TIV surrogates, linear nonstationary time series may be erroneously regarded as nonlinear and weak TV nonlinearities may remain unrevealed, while the use of TV AR surrogates markedly increases the probability of a correct interpretation. Application to short (500 beats) heart rate variability (HRV) time series recorded at rest (R), after head-up tilt (T), and during paced breathing (PB) showed: 1) modifications of the SE statistic that were well interpretable with the known cardiovascular physiology; 2) significant contribution of nonlinear dynamics to HRV in all conditions, with significant increase during PB at 0.2 Hz respiration rate; and 3) a disagreement between TV AR surrogates and TIV surrogates in about a quarter of the series, suggesting that nonstationarity may affect HRV recordings and bias the outcome of the traditional surrogate-based nonlinearity test.

  12. Local normalization: Uncovering correlations in non-stationary financial time series

    Science.gov (United States)

    Schäfer, Rudi; Guhr, Thomas

    2010-09-01

    The measurement of correlations between financial time series is of vital importance for risk management. In this paper we address an estimation error that stems from the non-stationarity of the time series. We put forward a method to rid the time series of local trends and variable volatility, while preserving cross-correlations. We test this method in a Monte Carlo simulation, and apply it to empirical data for the S&P 500 stocks.

  13. Advanced high temperature superconductor film-based process using RABiTS

    International Nuclear Information System (INIS)

    Goyal, A.; Hawsey, R.A.; Hack, J.; Moon, D.

    2000-01-01

    The purpose of this Cooperative Research and Development Agreement (CRADA) between Lockheed Martin Energy Research Corporation (Contractor), Managing contractor for Oak Ridge National Laboratory (ORNL) and Midwest Superconductivity, Inc. (MSI) and Westinghouse Science and Electric Company (WEC) was to develop the basis for a commercial process for the manufacturing of superconducting tape based on the RABiTS technology developed at ORNL. The chosen method for deposition of YBCO films on RABiTS was Metal Organic chemical Vapor Deposition (MOCVD)

  14. Fuzzy time-series based on Fibonacci sequence for stock price forecasting

    Science.gov (United States)

    Chen, Tai-Liang; Cheng, Ching-Hsue; Jong Teoh, Hia

    2007-07-01

    Time-series models have been utilized to make reasonably accurate predictions in the areas of stock price movements, academic enrollments, weather, etc. For promoting the forecasting performance of fuzzy time-series models, this paper proposes a new model, which incorporates the concept of the Fibonacci sequence, the framework of Song and Chissom's model and the weighted method of Yu's model. This paper employs a 5-year period TSMC (Taiwan Semiconductor Manufacturing Company) stock price data and a 13-year period of TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) stock index data as experimental datasets. By comparing our forecasting performances with Chen's (Forecasting enrollments based on fuzzy time-series. Fuzzy Sets Syst. 81 (1996) 311-319), Yu's (Weighted fuzzy time-series models for TAIEX forecasting. Physica A 349 (2004) 609-624) and Huarng's (The application of neural networks to forecast fuzzy time series. Physica A 336 (2006) 481-491) models, we conclude that the proposed model surpasses in accuracy these conventional fuzzy time-series models.

  15. Parameterizing unconditional skewness in models for financial time series

    DEFF Research Database (Denmark)

    He, Changli; Silvennoinen, Annastiina; Teräsvirta, Timo

    In this paper we consider the third-moment structure of a class of time series models. It is often argued that the marginal distribution of financial time series such as returns is skewed. Therefore it is of importance to know what properties a model should possess if it is to accommodate...

  16. Self-organising mixture autoregressive model for non-stationary time series modelling.

    Science.gov (United States)

    Ni, He; Yin, Hujun

    2008-12-01

    Modelling non-stationary time series has been a difficult task for both parametric and nonparametric methods. One promising solution is to combine the flexibility of nonparametric models with the simplicity of parametric models. In this paper, the self-organising mixture autoregressive (SOMAR) network is adopted as a such mixture model. It breaks time series into underlying segments and at the same time fits local linear regressive models to the clusters of segments. In such a way, a global non-stationary time series is represented by a dynamic set of local linear regressive models. Neural gas is used for a more flexible structure of the mixture model. Furthermore, a new similarity measure has been introduced in the self-organising network to better quantify the similarity of time series segments. The network can be used naturally in modelling and forecasting non-stationary time series. Experiments on artificial, benchmark time series (e.g. Mackey-Glass) and real-world data (e.g. numbers of sunspots and Forex rates) are presented and the results show that the proposed SOMAR network is effective and superior to other similar approaches.

  17. The Prediction of Teacher Turnover Employing Time Series Analysis.

    Science.gov (United States)

    Costa, Crist H.

    The purpose of this study was to combine knowledge of teacher demographic data with time-series forecasting methods to predict teacher turnover. Moving averages and exponential smoothing were used to forecast discrete time series. The study used data collected from the 22 largest school districts in Iowa, designated as FACT schools. Predictions…

  18. Memory H ∞ performance control of a class T-S fuzzy system

    Science.gov (United States)

    Wang, Yanhua; He, Xiqin; Wu, Zhihua; Kang, Xiulan; Xiu, Wei

    2018-03-01

    For much nonlinear system in the control system, both the stability of the system and certain performance indicators are required. The characteristics of T-S model in fuzzy system make it possible to illustrate a great amount of nonlinear system efficiently. First and foremost, the T-S model with uncertainties and external disturbance is utilized to interpret nonlinear system so as to implement H∞ performance control by means of fuzzy control theory. Meantime, owing to the tremendous existence of time delay phenomenon in the controlled, feedback controller with memory fuzzy state is fabricated. On the basis of Lyapunov Stability Theory, the closed-loop system becomes stable by establishing Lyapunov function. Gain matrix of the memory state feedback controller is obtained by applying linear matrix inequality methodology. And simultaneously it makes the system meet the requirement of the H∞ performance indicator. Ultimately, the efficiency of the above-mentioned method is exemplified by the numerical computation.

  19. Stacked Heterogeneous Neural Networks for Time Series Forecasting

    Directory of Open Access Journals (Sweden)

    Florin Leon

    2010-01-01

    Full Text Available A hybrid model for time series forecasting is proposed. It is a stacked neural network, containing one normal multilayer perceptron with bipolar sigmoid activation functions, and the other with an exponential activation function in the output layer. As shown by the case studies, the proposed stacked hybrid neural model performs well on a variety of benchmark time series. The combination of weights of the two stack components that leads to optimal performance is also studied.

  20. Chaotic time series prediction: From one to another

    International Nuclear Information System (INIS)

    Zhao Pengfei; Xing Lei; Yu Jun

    2009-01-01

    In this Letter, a new local linear prediction model is proposed to predict a chaotic time series of a component x(t) by using the chaotic time series of another component y(t) in the same system with x(t). Our approach is based on the phase space reconstruction coming from the Takens embedding theorem. To illustrate our results, we present an example of Lorenz system and compare with the performance of the original local linear prediction model.

  1. Grammar-based feature generation for time-series prediction

    CERN Document Server

    De Silva, Anthony Mihirana

    2015-01-01

    This book proposes a novel approach for time-series prediction using machine learning techniques with automatic feature generation. Application of machine learning techniques to predict time-series continues to attract considerable attention due to the difficulty of the prediction problems compounded by the non-linear and non-stationary nature of the real world time-series. The performance of machine learning techniques, among other things, depends on suitable engineering of features. This book proposes a systematic way for generating suitable features using context-free grammar. A number of feature selection criteria are investigated and a hybrid feature generation and selection algorithm using grammatical evolution is proposed. The book contains graphical illustrations to explain the feature generation process. The proposed approaches are demonstrated by predicting the closing price of major stock market indices, peak electricity load and net hourly foreign exchange client trade volume. The proposed method ...

  2. Arc Security System Based on Harmonics Detection for the TS ICRH Transmitter

    International Nuclear Information System (INIS)

    Berger-by, G.; Beaumont, B.; Lombard, G.; Millon, L.; Mollard, P.; Volpe, D.

    2006-01-01

    Since 1999 and with the help of I.P.P. (Institut fuer Plasmaphysik) Garching, we have tested in the Tore Supra (T.S.) ICRH transmitter, arcs security systems based on harmonics detection in a frequency band lower than the generator frequency. These systems have been designed for ASDEX in order to discriminate between the arcs and the ELMS mismatches during H-mode plasmas; they have been supplied to T.S. by a loan contract by I.P.P. The first tests done in T.S. were very successful and have permitted in 2000 to build a prototype matching T.S. technical requirements. These systems have some advantages in comparison with traditional securities which are based on use of a level on the V r /V f (Reflected to Forward voltage ratio) calculation . They do not use calculation, calibration with frequency or level. They can use RF signals from directional couplers or probes, so they are fully independent of the measurement systems. The detection bandwidth (4 MHz-35 MHz on TS) is independent of the antennae working frequencies. The detection principle is therefore insensitive to the crosstalk between the antennae and to the antennae coupling variations. The RF arc security box is installed in the generator hall which can be located far from the torus hall and the antennae (about 50 m on T.S.) a feature which is very relevant for the ITER transmitters. In 2001 we have built 3 systems based on our prototype in order to improve the protection of the 3 ICRH generators and antennae. These systems use industrial pass-band filters and a very sensitive linear detection (- 60 dBm) in order to facilitate the adjustment. Optical fibbers are used to transfer, with a very high immunity, the trip information to the generator electronic pilot. To guarantee safe operation, a checking test of the entire security chain is performed with a 10 MHz RF oscillator before each plasma start. The RF principle used on T.S. ICRH transmitter and its electronic interactions with the VME command control of

  3. Forecasting autoregressive time series under changing persistence

    DEFF Research Database (Denmark)

    Kruse, Robinson

    Changing persistence in time series models means that a structural change from nonstationarity to stationarity or vice versa occurs over time. Such a change has important implications for forecasting, as negligence may lead to inaccurate model predictions. This paper derives generally applicable...

  4. Recurrent Neural Networks for Multivariate Time Series with Missing Values.

    Science.gov (United States)

    Che, Zhengping; Purushotham, Sanjay; Cho, Kyunghyun; Sontag, David; Liu, Yan

    2018-04-17

    Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values. In time series prediction and other related tasks, it has been noted that missing values and their missing patterns are often correlated with the target labels, a.k.a., informative missingness. There is very limited work on exploiting the missing patterns for effective imputation and improving prediction performance. In this paper, we develop novel deep learning models, namely GRU-D, as one of the early attempts. GRU-D is based on Gated Recurrent Unit (GRU), a state-of-the-art recurrent neural network. It takes two representations of missing patterns, i.e., masking and time interval, and effectively incorporates them into a deep model architecture so that it not only captures the long-term temporal dependencies in time series, but also utilizes the missing patterns to achieve better prediction results. Experiments of time series classification tasks on real-world clinical datasets (MIMIC-III, PhysioNet) and synthetic datasets demonstrate that our models achieve state-of-the-art performance and provide useful insights for better understanding and utilization of missing values in time series analysis.

  5. Conditional time series forecasting with convolutional neural networks

    NARCIS (Netherlands)

    A. Borovykh (Anastasia); S.M. Bohte (Sander); C.W. Oosterlee (Cornelis)

    2017-01-01

    textabstractForecasting financial time series using past observations has been a significant topic of interest. While temporal relationships in the data exist, they are difficult to analyze and predict accurately due to the non-linear trends and noise present in the series. We propose to learn these

  6. Time Series Analysis of Wheat Futures Reward in China

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    Different from the fact that the main researches are focused on single futures contract and lack of the comparison of different periods, this paper described the statistical characteristics of wheat futures reward time series of Zhengzhou Commodity Exchange in recent three years. Besides the basic statistic analysis, the paper used the GARCH and EGARCH model to describe the time series which had the ARCH effect and analyzed the persistence of volatility shocks and the leverage effect. The results showed that compared with that of normal one,wheat futures reward series were abnormality, leptokurtic and thick tail distribution. The study also found that two-part of the reward series had no autocorrelation. Among the six correlative series, three ones presented the ARCH effect. By using of the Auto-regressive Distributed Lag Model, GARCH model and EGARCH model, the paper demonstrates the persistence of volatility shocks and the leverage effect on the wheat futures reward time series. The results reveal that on the one hand, the statistical characteristics of the wheat futures reward are similar to the aboard mature futures market as a whole. But on the other hand, the results reflect some shortages such as the immatureness and the over-control by the government in the Chinese future market.

  7. forecasting with nonlinear time series model: a monte-carlo

    African Journals Online (AJOL)

    PUBLICATIONS1

    erated recursively up to any step greater than one. For nonlinear time series model, point forecast for step one can be done easily like in the linear case but forecast for a step greater than or equal to ..... London. Franses, P. H. (1998). Time series models for business and Economic forecasting, Cam- bridge University press.

  8. Time series analysis of temporal networks

    Science.gov (United States)

    Sikdar, Sandipan; Ganguly, Niloy; Mukherjee, Animesh

    2016-01-01

    A common but an important feature of all real-world networks is that they are temporal in nature, i.e., the network structure changes over time. Due to this dynamic nature, it becomes difficult to propose suitable growth models that can explain the various important characteristic properties of these networks. In fact, in many application oriented studies only knowing these properties is sufficient. For instance, if one wishes to launch a targeted attack on a network, this can be done even without the knowledge of the full network structure; rather an estimate of some of the properties is sufficient enough to launch the attack. We, in this paper show that even if the network structure at a future time point is not available one can still manage to estimate its properties. We propose a novel method to map a temporal network to a set of time series instances, analyze them and using a standard forecast model of time series, try to predict the properties of a temporal network at a later time instance. To our aim, we consider eight properties such as number of active nodes, average degree, clustering coefficient etc. and apply our prediction framework on them. We mainly focus on the temporal network of human face-to-face contacts and observe that it represents a stochastic process with memory that can be modeled as Auto-Regressive-Integrated-Moving-Average (ARIMA). We use cross validation techniques to find the percentage accuracy of our predictions. An important observation is that the frequency domain properties of the time series obtained from spectrogram analysis could be used to refine the prediction framework by identifying beforehand the cases where the error in prediction is likely to be high. This leads to an improvement of 7.96% (for error level ≤20%) in prediction accuracy on an average across all datasets. As an application we show how such prediction scheme can be used to launch targeted attacks on temporal networks. Contribution to the Topical Issue

  9. The Hierarchical Spectral Merger Algorithm: A New Time Series Clustering Procedure

    KAUST Repository

    Euá n, Carolina; Ombao, Hernando; Ortega, Joaquí n

    2018-01-01

    We present a new method for time series clustering which we call the Hierarchical Spectral Merger (HSM) method. This procedure is based on the spectral theory of time series and identifies series that share similar oscillations or waveforms

  10. Notes on economic time series analysis system theoretic perspectives

    CERN Document Server

    Aoki, Masanao

    1983-01-01

    In seminars and graduate level courses I have had several opportunities to discuss modeling and analysis of time series with economists and economic graduate students during the past several years. These experiences made me aware of a gap between what economic graduate students are taught about vector-valued time series and what is available in recent system literature. Wishing to fill or narrow the gap that I suspect is more widely spread than my personal experiences indicate, I have written these notes to augment and reor­ ganize materials I have given in these courses and seminars. I have endeavored to present, in as much a self-contained way as practicable, a body of results and techniques in system theory that I judge to be relevant and useful to economists interested in using time series in their research. I have essentially acted as an intermediary and interpreter of system theoretic results and perspectives in time series by filtering out non-essential details, and presenting coherent accounts of wha...

  11. Turning the Hands of Time Again: A Purely Confirmatory Replication Study and a Bayesian Analysis

    Directory of Open Access Journals (Sweden)

    Eric-Jan eWagenmakers

    2015-04-01

    Full Text Available In a series of four experiments, Topolinski and Sparenberg (2012; TS found support for the conjecture that clockwise movements induce psychological states of temporal progression and an orientation toward the future and novelty. Here we report the results of a preregistered replication attempt of Experiment 2 from TS. Participants turned kitchen rolls either clockwise or counterclockwise while answering items from a questionnaire assessing openness to experience. Data from 102 participants showed that the effect went slightly in the direction opposite to that predicted by TS, and a preregistered Bayes factor hypothesis test revealed that the data were 10.76 times more likely under the null hypothesis than under the alternative hypothesis. Our findings illustrate the theoretical importance and practical advantages of preregistered Bayes factor replication studies, both for psychological science and for empirical work in general.

  12. Dynamical analysis and visualization of tornadoes time series.

    Directory of Open Access Journals (Sweden)

    António M Lopes

    Full Text Available In this paper we analyze the behavior of tornado time-series in the U.S. from the perspective of dynamical systems. A tornado is a violently rotating column of air extending from a cumulonimbus cloud down to the ground. Such phenomena reveal features that are well described by power law functions and unveil characteristics found in systems with long range memory effects. Tornado time series are viewed as the output of a complex system and are interpreted as a manifestation of its dynamics. Tornadoes are modeled as sequences of Dirac impulses with amplitude proportional to the events size. First, a collection of time series involving 64 years is analyzed in the frequency domain by means of the Fourier transform. The amplitude spectra are approximated by power law functions and their parameters are read as an underlying signature of the system dynamics. Second, it is adopted the concept of circular time and the collective behavior of tornadoes analyzed. Clustering techniques are then adopted to identify and visualize the emerging patterns.

  13. Dynamical analysis and visualization of tornadoes time series.

    Science.gov (United States)

    Lopes, António M; Tenreiro Machado, J A

    2015-01-01

    In this paper we analyze the behavior of tornado time-series in the U.S. from the perspective of dynamical systems. A tornado is a violently rotating column of air extending from a cumulonimbus cloud down to the ground. Such phenomena reveal features that are well described by power law functions and unveil characteristics found in systems with long range memory effects. Tornado time series are viewed as the output of a complex system and are interpreted as a manifestation of its dynamics. Tornadoes are modeled as sequences of Dirac impulses with amplitude proportional to the events size. First, a collection of time series involving 64 years is analyzed in the frequency domain by means of the Fourier transform. The amplitude spectra are approximated by power law functions and their parameters are read as an underlying signature of the system dynamics. Second, it is adopted the concept of circular time and the collective behavior of tornadoes analyzed. Clustering techniques are then adopted to identify and visualize the emerging patterns.

  14. "Observation Obscurer" - Time Series Viewer, Editor and Processor

    Science.gov (United States)

    Andronov, I. L.

    The program is described, which contains a set of subroutines suitable for East viewing and interactive filtering and processing of regularly and irregularly spaced time series. Being a 32-bit DOS application, it may be used as a default fast viewer/editor of time series in any compute shell ("commander") or in Windows. It allows to view the data in the "time" or "phase" mode, to remove ("obscure") or filter outstanding bad points; to make scale transformations and smoothing using few methods (e.g. mean with phase binning, determination of the statistically opti- mal number of phase bins; "running parabola" (Andronov, 1997, As. Ap. Suppl, 125, 207) fit and to make time series analysis using some methods, e.g. correlation, autocorrelation and histogram analysis: determination of extrema etc. Some features have been developed specially for variable star observers, e.g. the barycentric correction, the creation and fast analysis of "OC" diagrams etc. The manual for "hot keys" is presented. The computer code was compiled with a 32-bit Free Pascal (www.freepascal.org).

  15. Modelling road accidents: An approach using structural time series

    Science.gov (United States)

    Junus, Noor Wahida Md; Ismail, Mohd Tahir

    2014-09-01

    In this paper, the trend of road accidents in Malaysia for the years 2001 until 2012 was modelled using a structural time series approach. The structural time series model was identified using a stepwise method, and the residuals for each model were tested. The best-fitted model was chosen based on the smallest Akaike Information Criterion (AIC) and prediction error variance. In order to check the quality of the model, a data validation procedure was performed by predicting the monthly number of road accidents for the year 2012. Results indicate that the best specification of the structural time series model to represent road accidents is the local level with a seasonal model.

  16. Simultaneous and multi-criteria optimization of TS requirements and maintenance at NPPs

    International Nuclear Information System (INIS)

    Martorell, S.; Sanchez, A.; Carlos, S.; Serradell, V.

    2002-01-01

    One of the main concerns of the nuclear industry is to improve the availability of safety-related systems at nuclear power plants (NPPs) to achieve high safety levels. The development of efficient testing and maintenance has been traditionally one of the different ways to guarantee high levels of systems availability, which are implemented at NPP through technical specification and maintenance requirements (TS and M). On the other hand, there is a widely recognized interest in using the probabilistic risk analysis (PRA) for risk-informed applications aimed to emphasize both effective risk control and effective resource expenditures at NPPs. TS and M-related parameters in a plant are associated with controlling risk or with satisfying requirements, and are candidate to be evaluated for their resource effectiveness in risk-informed applications. The resource versus risk-control effectiveness principles formally enter in optimization problems where the cost or the burden for the plant staff is to be minimized while the risk or the availability of the safety equipment is constrained to be at a given level, and vice versa. Optimization of TS and M has been found interesting from the very beginning. However, the resolution of such a kind of optimization problem has been limited to focus on only individual TS and M-related parameters (STI, AOT, PM frequency, etc.) and/or adopting an individual optimization criterion (availability, costs, plant risks, etc.). Nevertheless, a number of reasons exist (e.g. interaction, similar scope, etc.) that justify the growing interest in the last years to focus on the simultaneous and multi-criteria optimization of TS and M. In the simultaneous optimization of TS and M-related parameters based on risk (or unavailability) and cost, like in many other engineering optimization problems, one normally faces multi-modal and non-linear objective functions and a variety of both linear and non-linear constraints. Genetic algorithms (GAs) have

  17. Multiscale Poincaré plots for visualizing the structure of heartbeat time series.

    Science.gov (United States)

    Henriques, Teresa S; Mariani, Sara; Burykin, Anton; Rodrigues, Filipa; Silva, Tiago F; Goldberger, Ary L

    2016-02-09

    Poincaré delay maps are widely used in the analysis of cardiac interbeat interval (RR) dynamics. To facilitate visualization of the structure of these time series, we introduce multiscale Poincaré (MSP) plots. Starting with the original RR time series, the method employs a coarse-graining procedure to create a family of time series, each of which represents the system's dynamics in a different time scale. Next, the Poincaré plots are constructed for the original and the coarse-grained time series. Finally, as an optional adjunct, color can be added to each point to represent its normalized frequency. We illustrate the MSP method on simulated Gaussian white and 1/f noise time series. The MSP plots of 1/f noise time series reveal relative conservation of the phase space area over multiple time scales, while those of white noise show a marked reduction in area. We also show how MSP plots can be used to illustrate the loss of complexity when heartbeat time series from healthy subjects are compared with those from patients with chronic (congestive) heart failure syndrome or with atrial fibrillation. This generalized multiscale approach to Poincaré plots may be useful in visualizing other types of time series.

  18. Two-fractal overlap time series: Earthquakes and market crashes

    Indian Academy of Sciences (India)

    velocity over the other and time series of stock prices. An anticipation method for some of the crashes have been proposed here, based on these observations. Keywords. Cantor set; time series; earthquake; market crash. PACS Nos 05.00; 02.50.-r; 64.60; 89.65.Gh; 95.75.Wx. 1. Introduction. Capturing dynamical patterns of ...

  19. T-S Fuzzy Model-Based Approximation and Filter Design for Stochastic Time-Delay Systems with Hankel Norm Criterion

    Directory of Open Access Journals (Sweden)

    Yanhui Li

    2014-01-01

    Full Text Available This paper investigates the Hankel norm filter design problem for stochastic time-delay systems, which are represented by Takagi-Sugeno (T-S fuzzy model. Motivated by the parallel distributed compensation (PDC technique, a novel filtering error system is established. The objective is to design a suitable filter that guarantees the corresponding filtering error system to be mean-square asymptotically stable and to have a specified Hankel norm performance level γ. Based on the Lyapunov stability theory and the Itô differential rule, the Hankel norm criterion is first established by adopting the integral inequality method, which can make some useful efforts in reducing conservativeness. The Hankel norm filtering problem is casted into a convex optimization problem with a convex linearization approach, which expresses all the conditions for the existence of admissible Hankel norm filter as standard linear matrix inequalities (LMIs. The effectiveness of the proposed method is demonstrated via a numerical example.

  20. Nonlinear time series analysis with R

    CERN Document Server

    Huffaker, Ray; Rosa, Rodolfo

    2017-01-01

    In the process of data analysis, the investigator is often facing highly-volatile and random-appearing observed data. A vast body of literature shows that the assumption of underlying stochastic processes was not necessarily representing the nature of the processes under investigation and, when other tools were used, deterministic features emerged. Non Linear Time Series Analysis (NLTS) allows researchers to test whether observed volatility conceals systematic non linear behavior, and to rigorously characterize governing dynamics. Behavioral patterns detected by non linear time series analysis, along with scientific principles and other expert information, guide the specification of mechanistic models that serve to explain real-world behavior rather than merely reproducing it. Often there is a misconception regarding the complexity of the level of mathematics needed to understand and utilize the tools of NLTS (for instance Chaos theory). However, mathematics used in NLTS is much simpler than many other subjec...

  1. InSAR Deformation Time Series Processed On-Demand in the Cloud

    Science.gov (United States)

    Horn, W. B.; Weeden, R.; Dimarchi, H.; Arko, S. A.; Hogenson, K.

    2017-12-01

    During this past year, ASF has developed a cloud-based on-demand processing system known as HyP3 (http://hyp3.asf.alaska.edu/), the Hybrid Pluggable Processing Pipeline, for Synthetic Aperture Radar (SAR) data. The system makes it easy for a user who doesn't have the time or inclination to install and use complex SAR processing software to leverage SAR data in their research or operations. One such processing algorithm is generation of a deformation time series product, which is a series of images representing ground displacements over time, which can be computed using a time series of interferometric SAR (InSAR) products. The set of software tools necessary to generate this useful product are difficult to install, configure, and use. Moreover, for a long time series with many images, the processing of just the interferograms can take days. Principally built by three undergraduate students at the ASF DAAC, the deformation time series processing relies the new Amazon Batch service, which enables processing of jobs with complex interconnected dependencies in a straightforward and efficient manner. In the case of generating a deformation time series product from a stack of single-look complex SAR images, the system uses Batch to serialize the up-front processing, interferogram generation, optional tropospheric correction, and deformation time series generation. The most time consuming portion is the interferogram generation, because even for a fairly small stack of images many interferograms need to be processed. By using AWS Batch, the interferograms are all generated in parallel; the entire process completes in hours rather than days. Additionally, the individual interferograms are saved in Amazon's cloud storage, so that when new data is acquired in the stack, an updated time series product can be generated with minimal addiitonal processing. This presentation will focus on the development techniques and enabling technologies that were used in developing the time

  2. TS WEBSITE AND SOCIAL MEDIA ANALYTICS- DECEMBER 2016

    Data.gov (United States)

    Department of Veterans Affairs — This PowerPoint included accomplishments reached in December 2016, along with how many TS users and Pulse users there are currently. Not only does this PowerPoint...

  3. TS WEBSITE AND SOCIAL MEDIA ANALYTICS NOVEMBER 2016

    Data.gov (United States)

    Department of Veterans Affairs — This PowerPoint included accomplishments reached in November 2016, along with how many TS users and Pulse users there are currently. Not only does this PowerPoint...

  4. Vector bilinear autoregressive time series model and its superiority ...

    African Journals Online (AJOL)

    In this research, a vector bilinear autoregressive time series model was proposed and used to model three revenue series (X1, X2, X3) . The “orders” of the three series were identified on the basis of the distribution of autocorrelation and partial autocorrelation functions and were used to construct the vector bilinear models.

  5. 25 years of time series forecasting

    NARCIS (Netherlands)

    de Gooijer, J.G.; Hyndman, R.J.

    2006-01-01

    We review the past 25 years of research into time series forecasting. In this silver jubilee issue, we naturally highlight results published in journals managed by the International Institute of Forecasters (Journal of Forecasting 1982-1985 and International Journal of Forecasting 1985-2005). During

  6. Markov Trends in Macroeconomic Time Series

    NARCIS (Netherlands)

    R. Paap (Richard)

    1997-01-01

    textabstractMany macroeconomic time series are characterised by long periods of positive growth, expansion periods, and short periods of negative growth, recessions. A popular model to describe this phenomenon is the Markov trend, which is a stochastic segmented trend where the slope depends on the

  7. Modeling seasonality in bimonthly time series

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans)

    1992-01-01

    textabstractA recurring issue in modeling seasonal time series variables is the choice of the most adequate model for the seasonal movements. One selection method for quarterly data is proposed in Hylleberg et al. (1990). Market response models are often constructed for bimonthly variables, and

  8. On clustering fMRI time series

    DEFF Research Database (Denmark)

    Goutte, Cyril; Toft, Peter Aundal; Rostrup, E.

    1999-01-01

    Analysis of fMRI time series is often performed by extracting one or more parameters for the individual voxels. Methods based, e.g., on various statistical tests are then used to yield parameters corresponding to probability of activation or activation strength. However, these methods do...

  9. FALSE DETERMINATIONS OF CHAOS IN SHORT NOISY TIME SERIES. (R828745)

    Science.gov (United States)

    A method (NEMG) proposed in 1992 for diagnosing chaos in noisy time series with 50 or fewer observations entails fitting the time series with an empirical function which predicts an observation in the series from previous observations, and then estimating the rate of divergenc...

  10. Multiscale multifractal multiproperty analysis of financial time series based on Rényi entropy

    Science.gov (United States)

    Yujun, Yang; Jianping, Li; Yimei, Yang

    This paper introduces a multiscale multifractal multiproperty analysis based on Rényi entropy (3MPAR) method to analyze short-range and long-range characteristics of financial time series, and then applies this method to the five time series of five properties in four stock indices. Combining the two analysis techniques of Rényi entropy and multifractal detrended fluctuation analysis (MFDFA), the 3MPAR method focuses on the curves of Rényi entropy and generalized Hurst exponent of five properties of four stock time series, which allows us to study more universal and subtle fluctuation characteristics of financial time series. By analyzing the curves of the Rényi entropy and the profiles of the logarithm distribution of MFDFA of five properties of four stock indices, the 3MPAR method shows some fluctuation characteristics of the financial time series and the stock markets. Then, it also shows a richer information of the financial time series by comparing the profile of five properties of four stock indices. In this paper, we not only focus on the multifractality of time series but also the fluctuation characteristics of the financial time series and subtle differences in the time series of different properties. We find that financial time series is far more complex than reported in some research works using one property of time series.

  11. A Literature Survey of Early Time Series Classification and Deep Learning

    OpenAIRE

    Santos, Tiago; Kern, Roman

    2017-01-01

    This paper provides an overview of current literature on time series classification approaches, in particular of early time series classification. A very common and effective time series classification approach is the 1-Nearest Neighbor classier, with different distance measures such as the Euclidean or dynamic time warping distances. This paper starts by reviewing these baseline methods. More recently, with the gain in popularity in the application of deep neural networks to the eld of...

  12. Signal Processing for Time-Series Functions on a Graph

    Science.gov (United States)

    2018-02-01

    Figures Fig. 1 Time -series function on a fixed graph.............................................2 iv Approved for public release; distribution is...φi〉`2(V)φi (39) 6= f̄ (40) Instead, we simply recover the average of f over time . 13 Approved for public release; distribution is unlimited. This...ARL-TR-8276• FEB 2018 US Army Research Laboratory Signal Processing for Time -Series Functions on a Graph by Humberto Muñoz-Barona, Jean Vettel, and

  13. Non-linear time series extreme events and integer value problems

    CERN Document Server

    Turkman, Kamil Feridun; Zea Bermudez, Patrícia

    2014-01-01

    This book offers a useful combination of probabilistic and statistical tools for analyzing nonlinear time series. Key features of the book include a study of the extremal behavior of nonlinear time series and a comprehensive list of nonlinear models that address different aspects of nonlinearity. Several inferential methods, including quasi likelihood methods, sequential Markov Chain Monte Carlo Methods and particle filters, are also included so as to provide an overall view of the available tools for parameter estimation for nonlinear models. A chapter on integer time series models based on several thinning operations, which brings together all recent advances made in this area, is also included. Readers should have attended a prior course on linear time series, and a good grasp of simulation-based inferential methods is recommended. This book offers a valuable resource for second-year graduate students and researchers in statistics and other scientific areas who need a basic understanding of nonlinear time ...

  14. Learning of time series through neuron-to-neuron instruction

    Energy Technology Data Exchange (ETDEWEB)

    Miyazaki, Y [Department of Physics, Kyoto University, Kyoto 606-8502, (Japan); Kinzel, W [Institut fuer Theoretische Physik, Universitaet Wurzburg, 97074 Wurzburg (Germany); Shinomoto, S [Department of Physics, Kyoto University, Kyoto (Japan)

    2003-02-07

    A model neuron with delayline feedback connections can learn a time series generated by another model neuron. It has been known that some student neurons that have completed such learning under the instruction of a teacher's quasi-periodic sequence mimic the teacher's time series over a long interval, even after instruction has ceased. We found that in addition to such faithful students, there are unfaithful students whose time series eventually diverge exponentially from that of the teacher. In order to understand the circumstances that allow for such a variety of students, the orbit dimension was estimated numerically. The quasi-periodic orbits in question were found to be confined in spaces with dimensions significantly smaller than that of the full phase space.

  15. Learning of time series through neuron-to-neuron instruction

    International Nuclear Information System (INIS)

    Miyazaki, Y; Kinzel, W; Shinomoto, S

    2003-01-01

    A model neuron with delayline feedback connections can learn a time series generated by another model neuron. It has been known that some student neurons that have completed such learning under the instruction of a teacher's quasi-periodic sequence mimic the teacher's time series over a long interval, even after instruction has ceased. We found that in addition to such faithful students, there are unfaithful students whose time series eventually diverge exponentially from that of the teacher. In order to understand the circumstances that allow for such a variety of students, the orbit dimension was estimated numerically. The quasi-periodic orbits in question were found to be confined in spaces with dimensions significantly smaller than that of the full phase space

  16. Quirky patterns in time-series of estimates of recruitment could be artefacts

    DEFF Research Database (Denmark)

    Dickey-Collas, M.; Hinzen, N.T.; Nash, R.D.M.

    2015-01-01

    of recruitment time-series in databases is therefore not consistent across or within species and stocks. Caution is therefore required as perhaps the characteristics of the time-series of stock dynamics may be determined by the model used to generate them, rather than underlying ecological phenomena......The accessibility of databases of global or regional stock assessment outputs is leading to an increase in meta-analysis of the dynamics of fish stocks. In most of these analyses, each of the time-series is generally assumed to be directly comparable. However, the approach to stock assessment...... employed, and the associated modelling assumptions, can have an important influence on the characteristics of each time-series. We explore this idea by investigating recruitment time-series with three different recruitment parameterizations: a stock–recruitment model, a random-walk time-series model...

  17. The Hierarchical Spectral Merger Algorithm: A New Time Series Clustering Procedure

    KAUST Repository

    Euán, Carolina

    2018-04-12

    We present a new method for time series clustering which we call the Hierarchical Spectral Merger (HSM) method. This procedure is based on the spectral theory of time series and identifies series that share similar oscillations or waveforms. The extent of similarity between a pair of time series is measured using the total variation distance between their estimated spectral densities. At each step of the algorithm, every time two clusters merge, a new spectral density is estimated using the whole information present in both clusters, which is representative of all the series in the new cluster. The method is implemented in an R package HSMClust. We present two applications of the HSM method, one to data coming from wave-height measurements in oceanography and the other to electroencefalogram (EEG) data.

  18. Estimation of time-delayed mutual information and bias for irregularly and sparsely sampled time-series

    International Nuclear Information System (INIS)

    Albers, D.J.; Hripcsak, George

    2012-01-01

    Highlights: ► Time-delayed mutual information for irregularly sampled time-series. ► Estimation bias for the time-delayed mutual information calculation. ► Fast, simple, PDF estimator independent, time-delayed mutual information bias estimate. ► Quantification of data-set-size limits of the time-delayed mutual calculation. - Abstract: A method to estimate the time-dependent correlation via an empirical bias estimate of the time-delayed mutual information for a time-series is proposed. In particular, the bias of the time-delayed mutual information is shown to often be equivalent to the mutual information between two distributions of points from the same system separated by infinite time. Thus intuitively, estimation of the bias is reduced to estimation of the mutual information between distributions of data points separated by large time intervals. The proposed bias estimation techniques are shown to work for Lorenz equations data and glucose time series data of three patients from the Columbia University Medical Center database.

  19. Inferring interdependencies from short time series

    Indian Academy of Sciences (India)

    Abstract. Complex networks provide an invaluable framework for the study of interlinked dynamical systems. In many cases, such networks are constructed from observed time series by first estimating the ...... does not quantify causal relations (unlike IOTA, or .... Africa_map_regions.svg, which is under public domain.

  20. Cloning and Characterization of EF-Tu and EF-Ts from Pseudomonas aeruginosa

    Directory of Open Access Journals (Sweden)

    Stephanie O. Palmer

    2013-01-01

    Full Text Available We have cloned genes encoding elongation factors EF-Tu and EF-Ts from Pseudomonas aeruginosa and expressed and purified the proteins to greater than 95% homogeneity. Sequence analysis indicated that P. aeruginosa EF-Tu and EF-Ts are 84% and 55% identical to E. coli counterparts, respectively. P. aeruginosa EF-Tu was active when assayed in GDP exchange assays. Kinetic parameters for the interaction of EF-Tu with GDP in the absence of EF-Ts were observed to be = 33 μM, = 0.003 s−1, and the specificity constant was  s−1 μM−1. In the presence of EF-Ts, these values were shifted to = 2 μM, = 0.005 s−1, and the specificity constant was  s−1 μM−1. The equilibrium dissociation constants governing the binding of EF-Tu to GDP ( were 30–75 nM and to GTP ( were 125–200 nM. EF-Ts stimulated the exchange of GDP by EF-Tu 10-fold. P. aeruginosa EF-Tu was active in forming a ternary complex with GTP and aminoacylated tRNA and was functional in poly(U-dependent binding of Phe-tRNAPhe at the A-site of P. aeruginosa ribosomes. P. aeruginosa EF-Tu was active in poly(U-programmed polyphenylalanine protein synthesis system composed of all P. aeruginosa components.

  1. Stochastic modeling of hourly rainfall times series in Campania (Italy)

    Science.gov (United States)

    Giorgio, M.; Greco, R.

    2009-04-01

    Occurrence of flowslides and floods in small catchments is uneasy to predict, since it is affected by a number of variables, such as mechanical and hydraulic soil properties, slope morphology, vegetation coverage, rainfall spatial and temporal variability. Consequently, landslide risk assessment procedures and early warning systems still rely on simple empirical models based on correlation between recorded rainfall data and observed landslides and/or river discharges. Effectiveness of such systems could be improved by reliable quantitative rainfall prediction, which can allow gaining larger lead-times. Analysis of on-site recorded rainfall height time series represents the most effective approach for a reliable prediction of local temporal evolution of rainfall. Hydrological time series analysis is a widely studied field in hydrology, often carried out by means of autoregressive models, such as AR, ARMA, ARX, ARMAX (e.g. Salas [1992]). Such models gave the best results when applied to the analysis of autocorrelated hydrological time series, like river flow or level time series. Conversely, they are not able to model the behaviour of intermittent time series, like point rainfall height series usually are, especially when recorded with short sampling time intervals. More useful for this issue are the so-called DRIP (Disaggregated Rectangular Intensity Pulse) and NSRP (Neymann-Scott Rectangular Pulse) model [Heneker et al., 2001; Cowpertwait et al., 2002], usually adopted to generate synthetic point rainfall series. In this paper, the DRIP model approach is adopted, in which the sequence of rain storms and dry intervals constituting the structure of rainfall time series is modeled as an alternating renewal process. Final aim of the study is to provide a useful tool to implement an early warning system for hydrogeological risk management. Model calibration has been carried out with hourly rainfall hieght data provided by the rain gauges of Campania Region civil

  2. Evaluation of the Airborne CASI/TASI Ts-VI Space Method for Estimating Near-Surface Soil Moisture

    Directory of Open Access Journals (Sweden)

    Lei Fan

    2015-03-01

    Full Text Available High spatial resolution airborne data with little sub-pixel heterogeneity were used to evaluate the suitability of the temperature/vegetation (Ts/VI space method developed from satellite observations, and were explored to improve the performance of the Ts/VI space method for estimating soil moisture (SM. An evaluation of the airborne ΔTs/Fr space (incorporated with air temperature revealed that normalized difference vegetation index (NDVI saturation and disturbed pixels were hindering the appropriate construction of the space. The non-disturbed ΔTs/Fr space, which was modified by adjusting the NDVI saturation and eliminating the disturbed pixels, was clearly correlated with the measured SM. The SM estimations of the non-disturbed ΔTs/Fr  space using the evaporative fraction (EF and temperature vegetation dryness index (TVDI were validated by using the SM measured at a depth of 4 cm, which was determined according to the land surface types. The validation results show that the EF approach provides superior estimates with a lower RMSE (0.023 m3·m−3 value and a higher correlation coefficient (0.68 than the TVDI. The application of the airborne ΔTs/Fr  space shows that the two modifications proposed in this study strengthen the link between the ΔTs/Fr space and SM, which is important for improving the precision of the remote sensing Ts/VI space method for monitoring SM.

  3. Using forbidden ordinal patterns to detect determinism in irregularly sampled time series.

    Science.gov (United States)

    Kulp, C W; Chobot, J M; Niskala, B J; Needhammer, C J

    2016-02-01

    It is known that when symbolizing a time series into ordinal patterns using the Bandt-Pompe (BP) methodology, there will be ordinal patterns called forbidden patterns that do not occur in a deterministic series. The existence of forbidden patterns can be used to identify deterministic dynamics. In this paper, the ability to use forbidden patterns to detect determinism in irregularly sampled time series is tested on data generated from a continuous model system. The study is done in three parts. First, the effects of sampling time on the number of forbidden patterns are studied on regularly sampled time series. The next two parts focus on two types of irregular-sampling, missing data and timing jitter. It is shown that forbidden patterns can be used to detect determinism in irregularly sampled time series for low degrees of sampling irregularity (as defined in the paper). In addition, comments are made about the appropriateness of using the BP methodology to symbolize irregularly sampled time series.

  4. Forecasting the Reference Evapotranspiration Using Time Series Model

    Directory of Open Access Journals (Sweden)

    H. Zare Abyaneh

    2016-10-01

    Full Text Available Introduction: Reference evapotranspiration is one of the most important factors in irrigation timing and field management. Moreover, reference evapotranspiration forecasting can play a vital role in future developments. Therefore in this study, the seasonal autoregressive integrated moving average (ARIMA model was used to forecast the reference evapotranspiration time series in the Esfahan, Semnan, Shiraz, Kerman, and Yazd synoptic stations. Materials and Methods: In the present study in all stations (characteristics of the synoptic stations are given in Table 1, the meteorological data, including mean, maximum and minimum air temperature, relative humidity, dry-and wet-bulb temperature, dew-point temperature, wind speed, precipitation, air vapor pressure and sunshine hours were collected from the Islamic Republic of Iran Meteorological Organization (IRIMO for the 41 years from 1965 to 2005. The FAO Penman-Monteith equation was used to calculate the monthly reference evapotranspiration in the five synoptic stations and the evapotranspiration time series were formed. The unit root test was used to identify whether the time series was stationary, then using the Box-Jenkins method, seasonal ARIMA models were applied to the sample data. Table 1. The geographical location and climate conditions of the synoptic stations Station\tGeographical location\tAltitude (m\tMean air temperature (°C\tMean precipitation (mm\tClimate, according to the De Martonne index classification Longitude (E\tLatitude (N Annual\tMin. and Max. Esfahan\t51° 40'\t32° 37'\t1550.4\t16.36\t9.4-23.3\t122\tArid Semnan\t53° 33'\t35° 35'\t1130.8\t18.0\t12.4-23.8\t140\tArid Shiraz\t52° 36'\t29° 32'\t1484\t18.0\t10.2-25.9\t324\tSemi-arid Kerman\t56° 58'\t30° 15'\t1753.8\t15.6\t6.7-24.6\t142\tArid Yazd\t54° 17'\t31° 54'\t1237.2\t19.2\t11.8-26.0\t61\tArid Results and Discussion: The monthly meteorological data were used as input for the Ref-ET software and monthly reference

  5. Complexity testing techniques for time series data: A comprehensive literature review

    International Nuclear Information System (INIS)

    Tang, Ling; Lv, Huiling; Yang, Fengmei; Yu, Lean

    2015-01-01

    Highlights: • A literature review of complexity testing techniques for time series data is provided. • Complexity measurements can generally fall into fractality, methods derived from nonlinear dynamics and entropy. • Different types investigate time series data from different perspectives. • Measures, applications and future studies for each type are presented. - Abstract: Complexity may be one of the most important measurements for analysing time series data; it covers or is at least closely related to different data characteristics within nonlinear system theory. This paper provides a comprehensive literature review examining the complexity testing techniques for time series data. According to different features, the complexity measurements for time series data can be divided into three primary groups, i.e., fractality (mono- or multi-fractality) for self-similarity (or system memorability or long-term persistence), methods derived from nonlinear dynamics (via attractor invariants or diagram descriptions) for attractor properties in phase-space, and entropy (structural or dynamical entropy) for the disorder state of a nonlinear system. These estimations analyse time series dynamics from different perspectives but are closely related to or even dependent on each other at the same time. In particular, a weaker self-similarity, a more complex structure of attractor, and a higher-level disorder state of a system consistently indicate that the observed time series data are at a higher level of complexity. Accordingly, this paper presents a historical tour of the important measures and works for each group, as well as ground-breaking and recent applications and future research directions.

  6. Complex dynamic in ecological time series

    Science.gov (United States)

    Peter Turchin; Andrew D. Taylor

    1992-01-01

    Although the possibility of complex dynamical behaviors-limit cycles, quasiperiodic oscillations, and aperiodic chaos-has been recognized theoretically, most ecologists are skeptical of their importance in nature. In this paper we develop a methodology for reconstructing endogenous (or deterministic) dynamics from ecological time series. Our method consists of fitting...

  7. Time Series Modelling using Proc Varmax

    DEFF Research Database (Denmark)

    Milhøj, Anders

    2007-01-01

    In this paper it will be demonstrated how various time series problems could be met using Proc Varmax. The procedure is rather new and hence new features like cointegration, testing for Granger causality are included, but it also means that more traditional ARIMA modelling as outlined by Box...

  8. SensL B-Series and C-Series silicon photomultipliers for time-of-flight positron emission tomography

    Energy Technology Data Exchange (ETDEWEB)

    O' Neill, K., E-mail: koneill@sensl.com; Jackson, C., E-mail: cjackson@sensl.com

    2015-07-01

    Silicon photomultipliers from SensL are designed for high performance, uniformity and low cost. They demonstrate peak photon detection efficiency of 41% at 420 nm, which is matched to the output spectrum of cerium doped lutetium orthosilicate. Coincidence resolving time of less than 220 ps is demonstrated. New process improvements have lead to the development of C-Series SiPM which reduces the dark noise by over an order of magnitude. In this paper we will show characterization test results which include photon detection efficiency, dark count rate, crosstalk probability, afterpulse probability and coincidence resolving time comparing B-Series to the newest pre-production C-Series. Additionally we will discuss the effect of silicon photomultiplier microcell size on coincidence resolving time allowing the optimal microcell size choice to be made for time of flight positron emission tomography systems.

  9. Intra-annual variability of carbon and nitrogen stable isotopes in suspended organic matter in waters of the western continental shelf of India

    Digital Repository Service at National Institute of Oceanography (India)

    Maya, M.V.; Karapurkar, S.G.; Naik, H.; Roy, R.; Shenoy, D.M.; Naqvi, S.W.A.

    receives large runoff from land during the SW monsoon and the associated terrestrial organic matter inputs may possibly play a role in biogeochemical cycling. 2 Methods 2.1 Sampling Time series measurements were carried out at the Candolim Time Series (CaTS...) station (aka Sta. G5), which is located CaTS~10 km Fig. 1. Map of the study area showing the location of Candolim Time Series (CaTS) station (G5) off Goa. at Lat. 15◦31prime N, Long. 73◦39prime E, approximately 10 km off Candolim Beach (Goa), central...

  10. Kriging Methodology and Its Development in Forecasting Econometric Time Series

    Directory of Open Access Journals (Sweden)

    Andrej Gajdoš

    2017-03-01

    Full Text Available One of the approaches for forecasting future values of a time series or unknown spatial data is kriging. The main objective of the paper is to introduce a general scheme of kriging in forecasting econometric time series using a family of linear regression time series models (shortly named as FDSLRM which apply regression not only to a trend but also to a random component of the observed time series. Simultaneously performing a Monte Carlo simulation study with a real electricity consumption dataset in the R computational langure and environment, we investigate the well-known problem of “negative” estimates of variance components when kriging predictions fail. Our following theoretical analysis, including also the modern apparatus of advanced multivariate statistics, gives us the formulation and proof of a general theorem about the explicit form of moments (up to sixth order for a Gaussian time series observation. This result provides a basis for further theoretical and computational research in the kriging methodology development.

  11. Experimental data report for Test TS-1 Reactivity Initiated Accident Test in NSRR with pre-irradiated BWR fuel rod

    International Nuclear Information System (INIS)

    Nakamura, Takehiko; Yoshinaga, Makio; Sobajima, Makoto; Fujishiro, Toshio; Horiki, Ohichiro; Yamahara, Takeshi; Ichihashi, Yoshinori; Kikuchi, Teruo

    1992-01-01

    This report presents experimental data for Test TS-1 which was the first in a series of tests, simulating Reactivity Initiated Accident (RIA) conditions using pre-irradiated BWR fuel rods, performed in the Nuclear Safety Research Reactor (NSRR) in October, 1989. Test fuel rod used in the Test TS-1 was a short-sized BWR (7 x 7) type rod which was fabricated from a commercial rod provided from Tsuruga Unit 1 power reactor. The fuel had an initial enrichment of 2.79 % and burnup of 21.3 GWd/t (bundle average). Pulse irradiation was performed at a condition of stagnant water cooling, atmospheric pressure and ambient temperature using a newly developed double container-type capsule. Energy deposition of the rod in this test was evaluated to be about 61 cal/g·fuel (55 cal/g·fuel in peak fuel enthalpy) and no fuel failure was observed. Descriptions on test conditions, test procedures, fuel burnup measurements, transient behavior of the test rod during pulse irradiation and results of post pulse irradiation examinations are contained in this report. (author)

  12. Use of Time-Series, ARIMA Designs to Assess Program Efficacy.

    Science.gov (United States)

    Braden, Jeffery P.; And Others

    1990-01-01

    Illustrates use of time-series designs for determining efficacy of interventions with fictitious data describing drug-abuse prevention program. Discusses problems and procedures associated with time-series data analysis using Auto Regressive Integrated Moving Averages (ARIMA) models. Example illustrates application of ARIMA analysis for…

  13. Time series sightability modeling of animal populations.

    Science.gov (United States)

    ArchMiller, Althea A; Dorazio, Robert M; St Clair, Katherine; Fieberg, John R

    2018-01-01

    Logistic regression models-or "sightability models"-fit to detection/non-detection data from marked individuals are often used to adjust for visibility bias in later detection-only surveys, with population abundance estimated using a modified Horvitz-Thompson (mHT) estimator. More recently, a model-based alternative for analyzing combined detection/non-detection and detection-only data was developed. This approach seemed promising, since it resulted in similar estimates as the mHT when applied to data from moose (Alces alces) surveys in Minnesota. More importantly, it provided a framework for developing flexible models for analyzing multiyear detection-only survey data in combination with detection/non-detection data. During initial attempts to extend the model-based approach to multiple years of detection-only data, we found that estimates of detection probabilities and population abundance were sensitive to the amount of detection-only data included in the combined (detection/non-detection and detection-only) analysis. Subsequently, we developed a robust hierarchical modeling approach where sightability model parameters are informed only by the detection/non-detection data, and we used this approach to fit a fixed-effects model (FE model) with year-specific parameters and a temporally-smoothed model (TS model) that shares information across years via random effects and a temporal spline. The abundance estimates from the TS model were more precise, with decreased interannual variability relative to the FE model and mHT abundance estimates, illustrating the potential benefits from model-based approaches that allow information to be shared across years.

  14. Time series sightability modeling of animal populations

    Science.gov (United States)

    ArchMiller, Althea A.; Dorazio, Robert; St. Clair, Katherine; Fieberg, John R.

    2018-01-01

    Logistic regression models—or “sightability models”—fit to detection/non-detection data from marked individuals are often used to adjust for visibility bias in later detection-only surveys, with population abundance estimated using a modified Horvitz-Thompson (mHT) estimator. More recently, a model-based alternative for analyzing combined detection/non-detection and detection-only data was developed. This approach seemed promising, since it resulted in similar estimates as the mHT when applied to data from moose (Alces alces) surveys in Minnesota. More importantly, it provided a framework for developing flexible models for analyzing multiyear detection-only survey data in combination with detection/non-detection data. During initial attempts to extend the model-based approach to multiple years of detection-only data, we found that estimates of detection probabilities and population abundance were sensitive to the amount of detection-only data included in the combined (detection/non-detection and detection-only) analysis. Subsequently, we developed a robust hierarchical modeling approach where sightability model parameters are informed only by the detection/non-detection data, and we used this approach to fit a fixed-effects model (FE model) with year-specific parameters and a temporally-smoothed model (TS model) that shares information across years via random effects and a temporal spline. The abundance estimates from the TS model were more precise, with decreased interannual variability relative to the FE model and mHT abundance estimates, illustrating the potential benefits from model-based approaches that allow information to be shared across years.

  15. An algorithm of Saxena-Easo on fuzzy time series forecasting

    Science.gov (United States)

    Ramadhani, L. C.; Anggraeni, D.; Kamsyakawuni, A.; Hadi, A. F.

    2018-04-01

    This paper presents a forecast model of Saxena-Easo fuzzy time series prediction to study the prediction of Indonesia inflation rate in 1970-2016. We use MATLAB software to compute this method. The algorithm of Saxena-Easo fuzzy time series doesn’t need stationarity like conventional forecasting method, capable of dealing with the value of time series which are linguistic and has the advantage of reducing the calculation, time and simplifying the calculation process. Generally it’s focus on percentage change as the universe discourse, interval partition and defuzzification. The result indicate that between the actual data and the forecast data are close enough with Root Mean Square Error (RMSE) = 1.5289.

  16. Evolutionary Algorithms for the Detection of Structural Breaks in Time Series

    DEFF Research Database (Denmark)

    Doerr, Benjamin; Fischer, Paul; Hilbert, Astrid

    2013-01-01

    Detecting structural breaks is an essential task for the statistical analysis of time series, for example, for fitting parametric models to it. In short, structural breaks are points in time at which the behavior of the time series changes. Typically, no solid background knowledge of the time...

  17. On modeling panels of time series

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans)

    2002-01-01

    textabstractThis paper reviews research issues in modeling panels of time series. Examples of this type of data are annually observed macroeconomic indicators for all countries in the world, daily returns on the individual stocks listed in the S&P500, and the sales records of all items in a

  18. Unsupervised Symbolization of Signal Time Series for Extraction of the Embedded Information

    Directory of Open Access Journals (Sweden)

    Yue Li

    2017-03-01

    Full Text Available This paper formulates an unsupervised algorithm for symbolization of signal time series to capture the embedded dynamic behavior. The key idea is to convert time series of the digital signal into a string of (spatially discrete symbols from which the embedded dynamic information can be extracted in an unsupervised manner (i.e., no requirement for labeling of time series. The main challenges here are: (1 definition of the symbol assignment for the time series; (2 identification of the partitioning segment locations in the signal space of time series; and (3 construction of probabilistic finite-state automata (PFSA from the symbol strings that contain temporal patterns. The reported work addresses these challenges by maximizing the mutual information measures between symbol strings and PFSA states. The proposed symbolization method has been validated by numerical simulation as well as by experimentation in a laboratory environment. Performance of the proposed algorithm has been compared to that of two commonly used algorithms of time series partitioning.

  19. Classification of time-series images using deep convolutional neural networks

    Science.gov (United States)

    Hatami, Nima; Gavet, Yann; Debayle, Johan

    2018-04-01

    Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw data. While the majority of Time-Series Classification (TSC) literature is focused on 1D signals, this paper uses Recurrence Plots (RP) to transform time-series into 2D texture images and then take advantage of the deep CNN classifier. Image representation of time-series introduces different feature types that are not available for 1D signals, and therefore TSC can be treated as texture image recognition task. CNN model also allows learning different levels of representations together with a classifier, jointly and automatically. Therefore, using RP and CNN in a unified framework is expected to boost the recognition rate of TSC. Experimental results on the UCR time-series classification archive demonstrate competitive accuracy of the proposed approach, compared not only to the existing deep architectures, but also to the state-of-the art TSC algorithms.

  20. Long Range Dependence Prognostics for Bearing Vibration Intensity Chaotic Time Series

    Directory of Open Access Journals (Sweden)

    Qing Li

    2016-01-01

    Full Text Available According to the chaotic features and typical fractional order characteristics of the bearing vibration intensity time series, a forecasting approach based on long range dependence (LRD is proposed. In order to reveal the internal chaotic properties, vibration intensity time series are reconstructed based on chaos theory in phase-space, the delay time is computed with C-C method and the optimal embedding dimension and saturated correlation dimension are calculated via the Grassberger–Procaccia (G-P method, respectively, so that the chaotic characteristics of vibration intensity time series can be jointly determined by the largest Lyapunov exponent and phase plane trajectory of vibration intensity time series, meanwhile, the largest Lyapunov exponent is calculated by the Wolf method and phase plane trajectory is illustrated using Duffing-Holmes Oscillator (DHO. The Hurst exponent and long range dependence prediction method are proposed to verify the typical fractional order features and improve the prediction accuracy of bearing vibration intensity time series, respectively. Experience shows that the vibration intensity time series have chaotic properties and the LRD prediction method is better than the other prediction methods (largest Lyapunov, auto regressive moving average (ARMA and BP neural network (BPNN model in prediction accuracy and prediction performance, which provides a new approach for running tendency predictions for rotating machinery and provide some guidance value to the engineering practice.

  1. Critical values for unit root tests in seasonal time series

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans); B. Hobijn (Bart)

    1997-01-01

    textabstractIn this paper, we present tables with critical values for a variety of tests for seasonal and non-seasonal unit roots in seasonal time series. We consider (extensions of) the Hylleberg et al. and Osborn et al. test procedures. These extensions concern time series with increasing seasonal

  2. Classification of time series patterns from complex dynamic systems

    Energy Technology Data Exchange (ETDEWEB)

    Schryver, J.C.; Rao, N.

    1998-07-01

    An increasing availability of high-performance computing and data storage media at decreasing cost is making possible the proliferation of large-scale numerical databases and data warehouses. Numeric warehousing enterprises on the order of hundreds of gigabytes to terabytes are a reality in many fields such as finance, retail sales, process systems monitoring, biomedical monitoring, surveillance and transportation. Large-scale databases are becoming more accessible to larger user communities through the internet, web-based applications and database connectivity. Consequently, most researchers now have access to a variety of massive datasets. This trend will probably only continue to grow over the next several years. Unfortunately, the availability of integrated tools to explore, analyze and understand the data warehoused in these archives is lagging far behind the ability to gain access to the same data. In particular, locating and identifying patterns of interest in numerical time series data is an increasingly important problem for which there are few available techniques. Temporal pattern recognition poses many interesting problems in classification, segmentation, prediction, diagnosis and anomaly detection. This research focuses on the problem of classification or characterization of numerical time series data. Highway vehicles and their drivers are examples of complex dynamic systems (CDS) which are being used by transportation agencies for field testing to generate large-scale time series datasets. Tools for effective analysis of numerical time series in databases generated by highway vehicle systems are not yet available, or have not been adapted to the target problem domain. However, analysis tools from similar domains may be adapted to the problem of classification of numerical time series data.

  3. Sensitivity analysis of machine-learning models of hydrologic time series

    Science.gov (United States)

    O'Reilly, A. M.

    2017-12-01

    Sensitivity analysis traditionally has been applied to assessing model response to perturbations in model parameters, where the parameters are those model input variables adjusted during calibration. Unlike physics-based models where parameters represent real phenomena, the equivalent of parameters for machine-learning models are simply mathematical "knobs" that are automatically adjusted during training/testing/verification procedures. Thus the challenge of extracting knowledge of hydrologic system functionality from machine-learning models lies in their very nature, leading to the label "black box." Sensitivity analysis of the forcing-response behavior of machine-learning models, however, can provide understanding of how the physical phenomena represented by model inputs affect the physical phenomena represented by model outputs.As part of a previous study, hybrid spectral-decomposition artificial neural network (ANN) models were developed to simulate the observed behavior of hydrologic response contained in multidecadal datasets of lake water level, groundwater level, and spring flow. Model inputs used moving window averages (MWA) to represent various frequencies and frequency-band components of time series of rainfall and groundwater use. Using these forcing time series, the MWA-ANN models were trained to predict time series of lake water level, groundwater level, and spring flow at 51 sites in central Florida, USA. A time series of sensitivities for each MWA-ANN model was produced by perturbing forcing time-series and computing the change in response time-series per unit change in perturbation. Variations in forcing-response sensitivities are evident between types (lake, groundwater level, or spring), spatially (among sites of the same type), and temporally. Two generally common characteristics among sites are more uniform sensitivities to rainfall over time and notable increases in sensitivities to groundwater usage during significant drought periods.

  4. Fractal analysis and nonlinear forecasting of indoor 222Rn time series

    International Nuclear Information System (INIS)

    Pausch, G.; Bossew, P.; Hofmann, W.; Steger, F.

    1998-01-01

    Fractal analyses of indoor 222 Rn time series were performed using different chaos theory based measurements such as time delay method, Hurst's rescaled range analysis, capacity (fractal) dimension, and Lyapunov exponent. For all time series we calculated only positive Lyapunov exponents which is a hint to chaos, while the Hurst exponents were well below 0.5, indicating antipersistent behaviour (past trends tend to reverse in the future). These time series were also analyzed with a nonlinear prediction method which allowed an estimation of the embedding dimensions with some restrictions, limiting the prediction to about three relative time steps. (orig.)

  5. Koopman Operator Framework for Time Series Modeling and Analysis

    Science.gov (United States)

    Surana, Amit

    2018-01-01

    We propose an interdisciplinary framework for time series classification, forecasting, and anomaly detection by combining concepts from Koopman operator theory, machine learning, and linear systems and control theory. At the core of this framework is nonlinear dynamic generative modeling of time series using the Koopman operator which is an infinite-dimensional but linear operator. Rather than working with the underlying nonlinear model, we propose two simpler linear representations or model forms based on Koopman spectral properties. We show that these model forms are invariants of the generative model and can be readily identified directly from data using techniques for computing Koopman spectral properties without requiring the explicit knowledge of the generative model. We also introduce different notions of distances on the space of such model forms which is essential for model comparison/clustering. We employ the space of Koopman model forms equipped with distance in conjunction with classical machine learning techniques to develop a framework for automatic feature generation for time series classification. The forecasting/anomaly detection framework is based on using Koopman model forms along with classical linear systems and control approaches. We demonstrate the proposed framework for human activity classification, and for time series forecasting/anomaly detection in power grid application.

  6. Konstantin Päts ja NSV Liidu Tallinna saatkond / Magnus Ilmjärv

    Index Scriptorium Estoniae

    Ilmjärv, Magnus, 1961-

    1999-01-01

    Järg Sep/13,14,15,16,17,18,20,21,22,22 lk. 17,21,17,18,18,16,17,21,18,14. Päts kui Nõukogude saatkonna lobitegija. Välisminister Pusta kangutamine. Pätsi info venelastele J. Tõnissoni kohta. Balti riikide vahelise koostöö takistamine. J. Puhk tõmmati venelaste mõjusfääri. Päts nõuab Eesti valitsuselt uut kaubanduslepingut venelastega

  7. Testing for intracycle determinism in pseudoperiodic time series.

    Science.gov (United States)

    Coelho, Mara C S; Mendes, Eduardo M A M; Aguirre, Luis A

    2008-06-01

    A determinism test is proposed based on the well-known method of the surrogate data. Assuming predictability to be a signature of determinism, the proposed method checks for intracycle (e.g., short-term) determinism in the pseudoperiodic time series for which standard methods of surrogate analysis do not apply. The approach presented is composed of two steps. First, the data are preprocessed to reduce the effects of seasonal and trend components. Second, standard tests of surrogate analysis can then be used. The determinism test is applied to simulated and experimental pseudoperiodic time series and the results show the applicability of the proposed test.

  8. Photocatalytic hydrogen generation over Eosin Y-Sensitized TS-1 zeolite

    International Nuclear Information System (INIS)

    Zhang Xiaojie; Jin Zhiliang; Li Yuexiang; Li Shuben; Lu Gongxuan

    2008-01-01

    Eosin Y-sensitized TS-1 zeolite was studied for the photo-reduction of water into hydrogen driven by visible light (λ ≥ 420 nm). The optimal pH and weight ratio between Eosin Y and TS-1 zeolite is 7 and 1/8, respectively. In the presence of triethanolamine (TEA) as an electron donor, the highest rate of hydrogen generation and apparent quantum efficiency are 34 μmol h -1 and 9.4%, respectively, under visible light irradiation (λ ≥ 420 nm). Short-term stability test indicates that the catalyst is rather stable during 50 h photoreaction

  9. Photocatalytic hydrogen generation over Eosin Y-Sensitized TS-1 zeolite

    Energy Technology Data Exchange (ETDEWEB)

    Zhang Xiaojie [State Key Laboratory for Oxo Synthesis and Selective Oxidation, Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences, Tianshui Zhong Road 18, Lanzhou 730000 (China); Graduate University of the Chinese Academy of Sciences, Beijing 100101 (China); Jin Zhiliang [State Key Laboratory for Oxo Synthesis and Selective Oxidation, Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences, Tianshui Zhong Road 18, Lanzhou 730000 (China); Li Yuexiang [Department of Chemistry, Nanchang University, Nanjing Road 245, Nanchang 330047 (China); Li Shuben [State Key Laboratory for Oxo Synthesis and Selective Oxidation, Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences, Tianshui Zhong Road 18, Lanzhou 730000 (China); Lu Gongxuan [State Key Laboratory for Oxo Synthesis and Selective Oxidation, Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences, Tianshui Zhong Road 18, Lanzhou 730000 (China)], E-mail: gxlu@lzb.ac.cn

    2008-05-30

    Eosin Y-sensitized TS-1 zeolite was studied for the photo-reduction of water into hydrogen driven by visible light ({lambda} {>=} 420 nm). The optimal pH and weight ratio between Eosin Y and TS-1 zeolite is 7 and 1/8, respectively. In the presence of triethanolamine (TEA) as an electron donor, the highest rate of hydrogen generation and apparent quantum efficiency are 34 {mu}mol h{sup -1} and 9.4%, respectively, under visible light irradiation ({lambda} {>=} 420 nm). Short-term stability test indicates that the catalyst is rather stable during 50 h photoreaction.

  10. Time series analysis and its applications with R examples

    CERN Document Server

    Shumway, Robert H

    2017-01-01

    The fourth edition of this popular graduate textbook, like its predecessors, presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Numerous examples using nontrivial data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and monitoring a nuclear test ban treaty. The book is designed as a textbook for graduate level students in the physical, biological, and social sciences and as a graduate level text in statistics. Some parts may also serve as an undergraduate introductory course. Theory and methodology are separated to allow presentations on different levels. In addition to coverage of classical methods of time series regression, ARIMA models, spectral analysis and state-space models, the text includes modern developments including categorical time series analysis, multivariate spectral methods, long memory series, nonli...

  11. A KST framework for correlation network construction from time series signals

    Science.gov (United States)

    Qi, Jin-Peng; Gu, Quan; Zhu, Ying; Zhang, Ping

    2018-04-01

    A KST (Kolmogorov-Smirnov test and T statistic) method is used for construction of a correlation network based on the fluctuation of each time series within the multivariate time signals. In this method, each time series is divided equally into multiple segments, and the maximal data fluctuation in each segment is calculated by a KST change detection procedure. Connections between each time series are derived from the data fluctuation matrix, and are used for construction of the fluctuation correlation network (FCN). The method was tested with synthetic simulations and the result was compared with those from using KS or T only for detection of data fluctuation. The novelty of this study is that the correlation analyses was based on the data fluctuation in each segment of each time series rather than on the original time signals, which would be more meaningful for many real world applications and for analysis of large-scale time signals where prior knowledge is uncertain.

  12. ERCC1 and TS Expression as Prognostic and Predictive Biomarkers in Metastatic Colon Cancer.

    Directory of Open Access Journals (Sweden)

    Michel B Choueiri

    Full Text Available In patients with metastatic colon cancer, response to first line chemotherapy is a strong predictor of overall survival (OS. Currently, oncologists lack diagnostic tests to determine which chemotherapy regimen offers the greatest chance for response in an individual patient. Here we present the results of gene expression analysis for two genes, ERCC1 and TS, measured with the commercially available ResponseDX: Colon assay (Response Genetics, Los Angeles, CA in 41 patients with de novo metastatic colon cancer diagnosed between July 2008 and August 2013 at the University of California, San Diego. In addition ERCC1 and TS expression levels as determined by RNAseq and survival data for patients in TCGA were downloaded from the TCGA data portal. We found that patients with low expression of ERCC1 (n = 33 had significantly longer median OS (36.0 vs. 10.1 mo, HR 0.29, 95% CI .095 to .84, log-rank p = 9.0x10-6 and median time to treatment to failure (TTF following first line chemotherapy (14.1 vs. 2.4 mo, HR 0.17, 95% CI 0.048 to 0.58, log-rank p = 5.3x10-4 relative to those with high expression (n = 4. After accounting for the covariates age, sex, tumor grade and ECOG performance status in a Cox proportional hazard model the association of low ERCC1 with longer OS (HR 0.18, 95% CI 0.14 to 0.26, p = 0.0448 and TTF (HR 0.16, 95% CI 0.14 to 0.21, p = 0.0053 remained significant. Patients with low TS expression (n = 29 had significantly longer median OS (36.0 vs. 14.8 mo, HR 0.25, 95% CI 0.074 to 0.82, log-rank p = 0.022 relative to those with high expression (n = 12. The combined low expression of ERCC1/TS was predictive of response in patients treated with FOLFOX (40% vs. 91%, RR 2.3, Fisher's exact test p = 0.03, n = 27, but not with FOLFIRI (71% vs. 71%, RR 1.0, Fisher's exact test p = 1, n = 14. Overall, these findings suggest that measurement of ERCC1 and TS expression has potential clinical utility in managing patients with metastatic colorectal

  13. Multivariate stochastic analysis for Monthly hydrological time series at Cuyahoga River Basin

    Science.gov (United States)

    zhang, L.

    2011-12-01

    Copula has become a very powerful statistic and stochastic methodology in case of the multivariate analysis in Environmental and Water resources Engineering. In recent years, the popular one-parameter Archimedean copulas, e.g. Gumbel-Houggard copula, Cook-Johnson copula, Frank copula, the meta-elliptical copula, e.g. Gaussian Copula, Student-T copula, etc. have been applied in multivariate hydrological analyses, e.g. multivariate rainfall (rainfall intensity, duration and depth), flood (peak discharge, duration and volume), and drought analyses (drought length, mean and minimum SPI values, and drought mean areal extent). Copula has also been applied in the flood frequency analysis at the confluences of river systems by taking into account the dependence among upstream gauge stations rather than by using the hydrological routing technique. In most of the studies above, the annual time series have been considered as stationary signal which the time series have been assumed as independent identically distributed (i.i.d.) random variables. But in reality, hydrological time series, especially the daily and monthly hydrological time series, cannot be considered as i.i.d. random variables due to the periodicity existed in the data structure. Also, the stationary assumption is also under question due to the Climate Change and Land Use and Land Cover (LULC) change in the fast years. To this end, it is necessary to revaluate the classic approach for the study of hydrological time series by relaxing the stationary assumption by the use of nonstationary approach. Also as to the study of the dependence structure for the hydrological time series, the assumption of same type of univariate distribution also needs to be relaxed by adopting the copula theory. In this paper, the univariate monthly hydrological time series will be studied through the nonstationary time series analysis approach. The dependence structure of the multivariate monthly hydrological time series will be

  14. Forecasting daily meteorological time series using ARIMA and regression models

    Science.gov (United States)

    Murat, Małgorzata; Malinowska, Iwona; Gos, Magdalena; Krzyszczak, Jaromir

    2018-04-01

    The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.

  15. Analysis of complex time series using refined composite multiscale entropy

    International Nuclear Information System (INIS)

    Wu, Shuen-De; Wu, Chiu-Wen; Lin, Shiou-Gwo; Lee, Kung-Yen; Peng, Chung-Kang

    2014-01-01

    Multiscale entropy (MSE) is an effective algorithm for measuring the complexity of a time series that has been applied in many fields successfully. However, MSE may yield an inaccurate estimation of entropy or induce undefined entropy because the coarse-graining procedure reduces the length of a time series considerably at large scales. Composite multiscale entropy (CMSE) was recently proposed to improve the accuracy of MSE, but it does not resolve undefined entropy. Here we propose a refined composite multiscale entropy (RCMSE) to improve CMSE. For short time series analyses, we demonstrate that RCMSE increases the accuracy of entropy estimation and reduces the probability of inducing undefined entropy.

  16. Compounding approach for univariate time series with nonstationary variances

    Science.gov (United States)

    Schäfer, Rudi; Barkhofen, Sonja; Guhr, Thomas; Stöckmann, Hans-Jürgen; Kuhl, Ulrich

    2015-12-01

    A defining feature of nonstationary systems is the time dependence of their statistical parameters. Measured time series may exhibit Gaussian statistics on short time horizons, due to the central limit theorem. The sample statistics for long time horizons, however, averages over the time-dependent variances. To model the long-term statistical behavior, we compound the local distribution with the distribution of its parameters. Here, we consider two concrete, but diverse, examples of such nonstationary systems: the turbulent air flow of a fan and a time series of foreign exchange rates. Our main focus is to empirically determine the appropriate parameter distribution for the compounding approach. To this end, we extract the relevant time scales by decomposing the time signals into windows and determine the distribution function of the thus obtained local variances.

  17. Tools for Generating Useful Time-series Data from PhenoCam Images

    Science.gov (United States)

    Milliman, T. E.; Friedl, M. A.; Frolking, S.; Hufkens, K.; Klosterman, S.; Richardson, A. D.; Toomey, M. P.

    2012-12-01

    The PhenoCam project (http://phenocam.unh.edu/) is tasked with acquiring, processing, and archiving digital repeat photography to be used for scientific studies of vegetation phenological processes. Over the past 5 years the PhenoCam project has collected over 2 million time series images for a total over 700 GB of image data. Several papers have been published describing derived "vegetation indices" (such as green-chromatic-coordinate or gcc) which can be compared to standard measures such as NDVI or EVI. Imagery from our archive is available for download but converting series of images for a particular camera into useful scientific data, while simple in principle, is complicated by a variety of factors. Cameras are often exposed to harsh weather conditions (high wind, rain, ice, snow pile up), which result in images where the field of view (FOV) is partially obscured or completely blocked for periods of time. The FOV can also change for other reasons (mount failures, tower maintenance, etc.) Some of the relatively inexpensive cameras that are being used can also temporarily lose color balance or exposure controls resulting in loss of imagery. All these factors negatively influence the automated analysis of the image time series making this a non-trivial task. Here we discuss the challenges of processing PhenoCam image time-series for vegetation monitoring and the associated data management tasks. We describe our current processing framework and a simple standardized output format for the resulting time-series data. The time-series data in this format will be generated for specific "regions of interest" (ROI's) for each of the cameras in the PhenoCam network. This standardized output (which will be updated daily) can be considered 'the pulse' of a particular camera and will provide a default phenological dynamic for said camera. The time-series data can also be viewed as a higher level product which can be used to generate "vegetation indices", like gcc, for

  18. Multiple Time Series Ising Model for Financial Market Simulations

    International Nuclear Information System (INIS)

    Takaishi, Tetsuya

    2015-01-01

    In this paper we propose an Ising model which simulates multiple financial time series. Our model introduces the interaction which couples to spins of other systems. Simulations from our model show that time series exhibit the volatility clustering that is often observed in the real financial markets. Furthermore we also find non-zero cross correlations between the volatilities from our model. Thus our model can simulate stock markets where volatilities of stocks are mutually correlated

  19. Time Series Modelling of Syphilis Incidence in China from 2005 to 2012.

    Science.gov (United States)

    Zhang, Xingyu; Zhang, Tao; Pei, Jiao; Liu, Yuanyuan; Li, Xiaosong; Medrano-Gracia, Pau

    2016-01-01

    The infection rate of syphilis in China has increased dramatically in recent decades, becoming a serious public health concern. Early prediction of syphilis is therefore of great importance for heath planning and management. In this paper, we analyzed surveillance time series data for primary, secondary, tertiary, congenital and latent syphilis in mainland China from 2005 to 2012. Seasonality and long-term trend were explored with decomposition methods. Autoregressive integrated moving average (ARIMA) was used to fit a univariate time series model of syphilis incidence. A separate multi-variable time series for each syphilis type was also tested using an autoregressive integrated moving average model with exogenous variables (ARIMAX). The syphilis incidence rates have increased three-fold from 2005 to 2012. All syphilis time series showed strong seasonality and increasing long-term trend. Both ARIMA and ARIMAX models fitted and estimated syphilis incidence well. All univariate time series showed highest goodness-of-fit results with the ARIMA(0,0,1)×(0,1,1) model. Time series analysis was an effective tool for modelling the historical and future incidence of syphilis in China. The ARIMAX model showed superior performance than the ARIMA model for the modelling of syphilis incidence. Time series correlations existed between the models for primary, secondary, tertiary, congenital and latent syphilis.

  20. FTSPlot: fast time series visualization for large datasets.

    Directory of Open Access Journals (Sweden)

    Michael Riss

    Full Text Available The analysis of electrophysiological recordings often involves visual inspection of time series data to locate specific experiment epochs, mask artifacts, and verify the results of signal processing steps, such as filtering or spike detection. Long-term experiments with continuous data acquisition generate large amounts of data. Rapid browsing through these massive datasets poses a challenge to conventional data plotting software because the plotting time increases proportionately to the increase in the volume of data. This paper presents FTSPlot, which is a visualization concept for large-scale time series datasets using techniques from the field of high performance computer graphics, such as hierarchic level of detail and out-of-core data handling. In a preprocessing step, time series data, event, and interval annotations are converted into an optimized data format, which then permits fast, interactive visualization. The preprocessing step has a computational complexity of O(n x log(N; the visualization itself can be done with a complexity of O(1 and is therefore independent of the amount of data. A demonstration prototype has been implemented and benchmarks show that the technology is capable of displaying large amounts of time series data, event, and interval annotations lag-free with < 20 ms ms. The current 64-bit implementation theoretically supports datasets with up to 2(64 bytes, on the x86_64 architecture currently up to 2(48 bytes are supported, and benchmarks have been conducted with 2(40 bytes/1 TiB or 1.3 x 10(11 double precision samples. The presented software is freely available and can be included as a Qt GUI component in future software projects, providing a standard visualization method for long-term electrophysiological experiments.

  1. Normalization methods in time series of platelet function assays

    Science.gov (United States)

    Van Poucke, Sven; Zhang, Zhongheng; Roest, Mark; Vukicevic, Milan; Beran, Maud; Lauwereins, Bart; Zheng, Ming-Hua; Henskens, Yvonne; Lancé, Marcus; Marcus, Abraham

    2016-01-01

    Abstract Platelet function can be quantitatively assessed by specific assays such as light-transmission aggregometry, multiple-electrode aggregometry measuring the response to adenosine diphosphate (ADP), arachidonic acid, collagen, and thrombin-receptor activating peptide and viscoelastic tests such as rotational thromboelastometry (ROTEM). The task of extracting meaningful statistical and clinical information from high-dimensional data spaces in temporal multivariate clinical data represented in multivariate time series is complex. Building insightful visualizations for multivariate time series demands adequate usage of normalization techniques. In this article, various methods for data normalization (z-transformation, range transformation, proportion transformation, and interquartile range) are presented and visualized discussing the most suited approach for platelet function data series. Normalization was calculated per assay (test) for all time points and per time point for all tests. Interquartile range, range transformation, and z-transformation demonstrated the correlation as calculated by the Spearman correlation test, when normalized per assay (test) for all time points. When normalizing per time point for all tests, no correlation could be abstracted from the charts as was the case when using all data as 1 dataset for normalization. PMID:27428217

  2. Development and application of a modified dynamic time warping algorithm (DTW-S) to analyses of primate brain expression time series.

    Science.gov (United States)

    Yuan, Yuan; Chen, Yi-Ping Phoebe; Ni, Shengyu; Xu, Augix Guohua; Tang, Lin; Vingron, Martin; Somel, Mehmet; Khaitovich, Philipp

    2011-08-18

    Comparing biological time series data across different conditions, or different specimens, is a common but still challenging task. Algorithms aligning two time series represent a valuable tool for such comparisons. While many powerful computation tools for time series alignment have been developed, they do not provide significance estimates for time shift measurements. Here, we present an extended version of the original DTW algorithm that allows us to determine the significance of time shift estimates in time series alignments, the DTW-Significance (DTW-S) algorithm. The DTW-S combines important properties of the original algorithm and other published time series alignment tools: DTW-S calculates the optimal alignment for each time point of each gene, it uses interpolated time points for time shift estimation, and it does not require alignment of the time-series end points. As a new feature, we implement a simulation procedure based on parameters estimated from real time series data, on a series-by-series basis, allowing us to determine the false positive rate (FPR) and the significance of the estimated time shift values. We assess the performance of our method using simulation data and real expression time series from two published primate brain expression datasets. Our results show that this method can provide accurate and robust time shift estimates for each time point on a gene-by-gene basis. Using these estimates, we are able to uncover novel features of the biological processes underlying human brain development and maturation. The DTW-S provides a convenient tool for calculating accurate and robust time shift estimates at each time point for each gene, based on time series data. The estimates can be used to uncover novel biological features of the system being studied. The DTW-S is freely available as an R package TimeShift at http://www.picb.ac.cn/Comparative/data.html.

  3. Automated Bayesian model development for frequency detection in biological time series

    Directory of Open Access Journals (Sweden)

    Oldroyd Giles ED

    2011-06-01

    Full Text Available Abstract Background A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. Results In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Conclusions Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and

  4. Automated Bayesian model development for frequency detection in biological time series.

    Science.gov (United States)

    Granqvist, Emma; Oldroyd, Giles E D; Morris, Richard J

    2011-06-24

    A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and the requirement for uniformly sampled data. Biological time

  5. hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature Extraction.

    Science.gov (United States)

    Fulcher, Ben D; Jones, Nick S

    2017-11-22

    Phenotype measurements frequently take the form of time series, but we currently lack a systematic method for relating these complex data streams to scientifically meaningful outcomes, such as relating the movement dynamics of organisms to their genotype or measurements of brain dynamics of a patient to their disease diagnosis. Previous work addressed this problem by comparing implementations of thousands of diverse scientific time-series analysis methods in an approach termed highly comparative time-series analysis. Here, we introduce hctsa, a software tool for applying this methodological approach to data. hctsa includes an architecture for computing over 7,700 time-series features and a suite of analysis and visualization algorithms to automatically select useful and interpretable time-series features for a given application. Using exemplar applications to high-throughput phenotyping experiments, we show how hctsa allows researchers to leverage decades of time-series research to quantify and understand informative structure in time-series data. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  6. Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models

    Science.gov (United States)

    Price, Larry R.

    2012-01-01

    The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…

  7. Applied time series analysis and innovative computing

    CERN Document Server

    Ao, Sio-Iong

    2010-01-01

    This text is a systematic, state-of-the-art introduction to the use of innovative computing paradigms as an investigative tool for applications in time series analysis. It includes frontier case studies based on recent research.

  8. On Stabilizing the Variance of Dynamic Functional Brain Connectivity Time Series.

    Science.gov (United States)

    Thompson, William Hedley; Fransson, Peter

    2016-12-01

    Assessment of dynamic functional brain connectivity based on functional magnetic resonance imaging (fMRI) data is an increasingly popular strategy to investigate temporal dynamics of the brain's large-scale network architecture. Current practice when deriving connectivity estimates over time is to use the Fisher transformation, which aims to stabilize the variance of correlation values that fluctuate around varying true correlation values. It is, however, unclear how well the stabilization of signal variance performed by the Fisher transformation works for each connectivity time series, when the true correlation is assumed to be fluctuating. This is of importance because many subsequent analyses either assume or perform better when the time series have stable variance or adheres to an approximate Gaussian distribution. In this article, using simulations and analysis of resting-state fMRI data, we analyze the effect of applying different variance stabilization strategies on connectivity time series. We focus our investigation on the Fisher transformation, the Box-Cox (BC) transformation and an approach that combines both transformations. Our results show that, if the intention of stabilizing the variance is to use metrics on the time series, where stable variance or a Gaussian distribution is desired (e.g., clustering), the Fisher transformation is not optimal and may even skew connectivity time series away from being Gaussian. Furthermore, we show that the suboptimal performance of the Fisher transformation can be substantially improved by including an additional BC transformation after the dynamic functional connectivity time series has been Fisher transformed.

  9. Characteristics of the transmission of autoregressive sub-patterns in financial time series

    Science.gov (United States)

    Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong

    2014-09-01

    There are many types of autoregressive patterns in financial time series, and they form a transmission process. Here, we define autoregressive patterns quantitatively through an econometrical regression model. We present a computational algorithm that sets the autoregressive patterns as nodes and transmissions between patterns as edges, and then converts the transmission process of autoregressive patterns in a time series into a network. We utilised daily Shanghai (securities) composite index time series to study the transmission characteristics of autoregressive patterns. We found statistically significant evidence that the financial market is not random and that there are similar characteristics between parts and whole time series. A few types of autoregressive sub-patterns and transmission patterns drive the oscillations of the financial market. A clustering effect on fluctuations appears in the transmission process, and certain non-major autoregressive sub-patterns have high media capabilities in the financial time series. Different stock indexes exhibit similar characteristics in the transmission of fluctuation information. This work not only proposes a distinctive perspective for analysing financial time series but also provides important information for investors.

  10. A Review of Some Aspects of Robust Inference for Time Series.

    Science.gov (United States)

    1984-09-01

    REVIEW OF SOME ASPECTSOF ROBUST INFERNCE FOR TIME SERIES by Ad . Dougla Main TE "iAL REPOW No. 63 Septermber 1984 Department of Statistics University of ...clear. One cannot hope to have a good method for dealing with outliers in time series by using only an instantaneous nonlinear transformation of the data...AI.49 716 A REVIEWd OF SOME ASPECTS OF ROBUST INFERENCE FOR TIME 1/1 SERIES(U) WASHINGTON UNIV SEATTLE DEPT OF STATISTICS R D MARTIN SEP 84 TR-53

  11. Refined composite multiscale weighted-permutation entropy of financial time series

    Science.gov (United States)

    Zhang, Yongping; Shang, Pengjian

    2018-04-01

    For quantifying the complexity of nonlinear systems, multiscale weighted-permutation entropy (MWPE) has recently been proposed. MWPE has incorporated amplitude information and been applied to account for the multiple inherent dynamics of time series. However, MWPE may be unreliable, because its estimated values show large fluctuation for slight variation of the data locations, and a significant distinction only for the different length of time series. Therefore, we propose the refined composite multiscale weighted-permutation entropy (RCMWPE). By comparing the RCMWPE results with other methods' results on both synthetic data and financial time series, RCMWPE method shows not only the advantages inherited from MWPE but also lower sensitivity to the data locations, more stable and much less dependent on the length of time series. Moreover, we present and discuss the results of RCMWPE method on the daily price return series from Asian and European stock markets. There are significant differences between Asian markets and European markets, and the entropy values of Hang Seng Index (HSI) are close to but higher than those of European markets. The reliability of the proposed RCMWPE method has been supported by simulations on generated and real data. It could be applied to a variety of fields to quantify the complexity of the systems over multiple scales more accurately.

  12. Parametric, nonparametric and parametric modelling of a chaotic circuit time series

    Science.gov (United States)

    Timmer, J.; Rust, H.; Horbelt, W.; Voss, H. U.

    2000-09-01

    The determination of a differential equation underlying a measured time series is a frequently arising task in nonlinear time series analysis. In the validation of a proposed model one often faces the dilemma that it is hard to decide whether possible discrepancies between the time series and model output are caused by an inappropriate model or by bad estimates of parameters in a correct type of model, or both. We propose a combination of parametric modelling based on Bock's multiple shooting algorithm and nonparametric modelling based on optimal transformations as a strategy to test proposed models and if rejected suggest and test new ones. We exemplify this strategy on an experimental time series from a chaotic circuit where we obtain an extremely accurate reconstruction of the observed attractor.

  13. Bureau de documentation TS/EL/DO

    CERN Document Server

    Dujardin, M

    2004-01-01

    Le bureau de documentation a comme responsabilité de fournir et manager toute la documentation nécessaire au groupe TS-EL. Ce travail utilise des outils CAO (EUCLID- AUTOCAD) et de gestion de base de donnés ORACLE (GESMAR,EDMS) largement utilisé dans le département TS mais aussi des applications métiers tel que le transfert des donnés CAO vers les bases de données, le cheminement automatique ou la schématique électrique. La section se concentre sur des études de grands ensembles et l'intégration 3D des équipements électriques. Les études de services généraux sont généralement sous-traitées. Le projet LHC occupe actuellement la quasi totalité de nos ressources. Pour mener à bien les études électriques du LHC, caractérisées par une définition tardive des besoins et de nombreuses modifications, une structure souple et réactive a été mis en place : Une cellule CAO 3D EUCLID, en charge des layouts d'équipements et des cheminements, en relation avec l'intégration, et une cellule d'é...

  14. Synthetic river flow time series generator for dispatch and spot price forecast

    International Nuclear Information System (INIS)

    Flores, R.A.

    2007-01-01

    Decision-making in electricity markets is complicated by uncertainties in demand growth, power supplies and fuel prices. In Peru, where the electrical power system is highly dependent on water resources at dams and river flows, hydrological uncertainties play a primary role in planning, price and dispatch forecast. This paper proposed a signal processing method for generating new synthetic river flow time series as a support for planning and spot market price forecasting. River flow time series are natural phenomena representing a continuous-time domain process. As an alternative synthetic representation of the original river flow time series, this proposed signal processing method preserves correlations, basic statistics and seasonality. It takes into account deterministic, periodic and non periodic components such as those due to the El Nino Southern Oscillation phenomenon. The new synthetic time series has many correlations with the original river flow time series, rendering it suitable for possible replacement of the classical method of sorting historical river flow time series. As a dispatch and planning approach to spot pricing, the proposed method offers higher accuracy modeling by decomposing the signal into deterministic, periodic, non periodic and stochastic sub signals. 4 refs., 4 tabs., 13 figs

  15. Cross-sample entropy of foreign exchange time series

    Science.gov (United States)

    Liu, Li-Zhi; Qian, Xi-Yuan; Lu, Heng-Yao

    2010-11-01

    The correlation of foreign exchange rates in currency markets is investigated based on the empirical data of DKK/USD, NOK/USD, CAD/USD, JPY/USD, KRW/USD, SGD/USD, THB/USD and TWD/USD for a period from 1995 to 2002. Cross-SampEn (cross-sample entropy) method is used to compare the returns of every two exchange rate time series to assess their degree of asynchrony. The calculation method of confidence interval of SampEn is extended and applied to cross-SampEn. The cross-SampEn and its confidence interval for every two of the exchange rate time series in periods 1995-1998 (before the Asian currency crisis) and 1999-2002 (after the Asian currency crisis) are calculated. The results show that the cross-SampEn of every two of these exchange rates becomes higher after the Asian currency crisis, indicating a higher asynchrony between the exchange rates. Especially for Singapore, Thailand and Taiwan, the cross-SampEn values after the Asian currency crisis are significantly higher than those before the Asian currency crisis. Comparison with the correlation coefficient shows that cross-SampEn is superior to describe the correlation between time series.

  16. Clustering Multivariate Time Series Using Hidden Markov Models

    Directory of Open Access Journals (Sweden)

    Shima Ghassempour

    2014-03-01

    Full Text Available In this paper we describe an algorithm for clustering multivariate time series with variables taking both categorical and continuous values. Time series of this type are frequent in health care, where they represent the health trajectories of individuals. The problem is challenging because categorical variables make it difficult to define a meaningful distance between trajectories. We propose an approach based on Hidden Markov Models (HMMs, where we first map each trajectory into an HMM, then define a suitable distance between HMMs and finally proceed to cluster the HMMs with a method based on a distance matrix. We test our approach on a simulated, but realistic, data set of 1,255 trajectories of individuals of age 45 and over, on a synthetic validation set with known clustering structure, and on a smaller set of 268 trajectories extracted from the longitudinal Health and Retirement Survey. The proposed method can be implemented quite simply using standard packages in R and Matlab and may be a good candidate for solving the difficult problem of clustering multivariate time series with categorical variables using tools that do not require advanced statistic knowledge, and therefore are accessible to a wide range of researchers.

  17. TimesVector: a vectorized clustering approach to the analysis of time series transcriptome data from multiple phenotypes.

    Science.gov (United States)

    Jung, Inuk; Jo, Kyuri; Kang, Hyejin; Ahn, Hongryul; Yu, Youngjae; Kim, Sun

    2017-12-01

    Identifying biologically meaningful gene expression patterns from time series gene expression data is important to understand the underlying biological mechanisms. To identify significantly perturbed gene sets between different phenotypes, analysis of time series transcriptome data requires consideration of time and sample dimensions. Thus, the analysis of such time series data seeks to search gene sets that exhibit similar or different expression patterns between two or more sample conditions, constituting the three-dimensional data, i.e. gene-time-condition. Computational complexity for analyzing such data is very high, compared to the already difficult NP-hard two dimensional biclustering algorithms. Because of this challenge, traditional time series clustering algorithms are designed to capture co-expressed genes with similar expression pattern in two sample conditions. We present a triclustering algorithm, TimesVector, specifically designed for clustering three-dimensional time series data to capture distinctively similar or different gene expression patterns between two or more sample conditions. TimesVector identifies clusters with distinctive expression patterns in three steps: (i) dimension reduction and clustering of time-condition concatenated vectors, (ii) post-processing clusters for detecting similar and distinct expression patterns and (iii) rescuing genes from unclassified clusters. Using four sets of time series gene expression data, generated by both microarray and high throughput sequencing platforms, we demonstrated that TimesVector successfully detected biologically meaningful clusters of high quality. TimesVector improved the clustering quality compared to existing triclustering tools and only TimesVector detected clusters with differential expression patterns across conditions successfully. The TimesVector software is available at http://biohealth.snu.ac.kr/software/TimesVector/. sunkim.bioinfo@snu.ac.kr. Supplementary data are available at

  18. Stochastic generation of hourly wind speed time series

    International Nuclear Information System (INIS)

    Shamshad, A.; Wan Mohd Ali Wan Hussin; Bawadi, M.A.; Mohd Sanusi, S.A.

    2006-01-01

    In the present study hourly wind speed data of Kuala Terengganu in Peninsular Malaysia are simulated by using transition matrix approach of Markovian process. The wind speed time series is divided into various states based on certain criteria. The next wind speed states are selected based on the previous states. The cumulative probability transition matrix has been formed in which each row ends with 1. Using the uniform random numbers between 0 and 1, a series of future states is generated. These states have been converted to the corresponding wind speed values using another uniform random number generator. The accuracy of the model has been determined by comparing the statistical characteristics such as average, standard deviation, root mean square error, probability density function and autocorrelation function of the generated data to those of the original data. The generated wind speed time series data is capable to preserve the wind speed characteristics of the observed data

  19. Causal strength induction from time series data.

    Science.gov (United States)

    Soo, Kevin W; Rottman, Benjamin M

    2018-04-01

    One challenge when inferring the strength of cause-effect relations from time series data is that the cause and/or effect can exhibit temporal trends. If temporal trends are not accounted for, a learner could infer that a causal relation exists when it does not, or even infer that there is a positive causal relation when the relation is negative, or vice versa. We propose that learners use a simple heuristic to control for temporal trends-that they focus not on the states of the cause and effect at a given instant, but on how the cause and effect change from one observation to the next, which we call transitions. Six experiments were conducted to understand how people infer causal strength from time series data. We found that participants indeed use transitions in addition to states, which helps them to reach more accurate causal judgments (Experiments 1A and 1B). Participants use transitions more when the stimuli are presented in a naturalistic visual format than a numerical format (Experiment 2), and the effect of transitions is not driven by primacy or recency effects (Experiment 3). Finally, we found that participants primarily use the direction in which variables change rather than the magnitude of the change for estimating causal strength (Experiments 4 and 5). Collectively, these studies provide evidence that people often use a simple yet effective heuristic for inferring causal strength from time series data. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  20. Interpretable Categorization of Heterogeneous Time Series Data

    Science.gov (United States)

    Lee, Ritchie; Kochenderfer, Mykel J.; Mengshoel, Ole J.; Silbermann, Joshua

    2017-01-01

    We analyze data from simulated aircraft encounters to validate and inform the development of a prototype aircraft collision avoidance system. The high-dimensional and heterogeneous time series dataset is analyzed to discover properties of near mid-air collisions (NMACs) and categorize the NMAC encounters. Domain experts use these properties to better organize and understand NMAC occurrences. Existing solutions either are not capable of handling high-dimensional and heterogeneous time series datasets or do not provide explanations that are interpretable by a domain expert. The latter is critical to the acceptance and deployment of safety-critical systems. To address this gap, we propose grammar-based decision trees along with a learning algorithm. Our approach extends decision trees with a grammar framework for classifying heterogeneous time series data. A context-free grammar is used to derive decision expressions that are interpretable, application-specific, and support heterogeneous data types. In addition to classification, we show how grammar-based decision trees can also be used for categorization, which is a combination of clustering and generating interpretable explanations for each cluster. We apply grammar-based decision trees to a simulated aircraft encounter dataset and evaluate the performance of four variants of our learning algorithm. The best algorithm is used to analyze and categorize near mid-air collisions in the aircraft encounter dataset. We describe each discovered category in detail and discuss its relevance to aircraft collision avoidance.

  1. Minimum entropy density method for the time series analysis

    Science.gov (United States)

    Lee, Jeong Won; Park, Joongwoo Brian; Jo, Hang-Hyun; Yang, Jae-Suk; Moon, Hie-Tae

    2009-01-01

    The entropy density is an intuitive and powerful concept to study the complicated nonlinear processes derived from physical systems. We develop the minimum entropy density method (MEDM) to detect the structure scale of a given time series, which is defined as the scale in which the uncertainty is minimized, hence the pattern is revealed most. The MEDM is applied to the financial time series of Standard and Poor’s 500 index from February 1983 to April 2006. Then the temporal behavior of structure scale is obtained and analyzed in relation to the information delivery time and efficient market hypothesis.

  2. Time series analysis of the developed financial markets' integration using visibility graphs

    Science.gov (United States)

    Zhuang, Enyu; Small, Michael; Feng, Gang

    2014-09-01

    A time series representing the developed financial markets' segmentation from 1973 to 2012 is studied. The time series reveals an obvious market integration trend. To further uncover the features of this time series, we divide it into seven windows and generate seven visibility graphs. The measuring capabilities of the visibility graphs provide means to quantitatively analyze the original time series. It is found that the important historical incidents that influenced market integration coincide with variations in the measured graphical node degree. Through the measure of neighborhood span, the frequencies of the historical incidents are disclosed. Moreover, it is also found that large "cycles" and significant noise in the time series are linked to large and small communities in the generated visibility graphs. For large cycles, how historical incidents significantly affected market integration is distinguished by density and compactness of the corresponding communities.

  3. A cluster merging method for time series microarray with production values.

    Science.gov (United States)

    Chira, Camelia; Sedano, Javier; Camara, Monica; Prieto, Carlos; Villar, Jose R; Corchado, Emilio

    2014-09-01

    A challenging task in time-course microarray data analysis is to cluster genes meaningfully combining the information provided by multiple replicates covering the same key time points. This paper proposes a novel cluster merging method to accomplish this goal obtaining groups with highly correlated genes. The main idea behind the proposed method is to generate a clustering starting from groups created based on individual temporal series (representing different biological replicates measured in the same time points) and merging them by taking into account the frequency by which two genes are assembled together in each clustering. The gene groups at the level of individual time series are generated using several shape-based clustering methods. This study is focused on a real-world time series microarray task with the aim to find co-expressed genes related to the production and growth of a certain bacteria. The shape-based clustering methods used at the level of individual time series rely on identifying similar gene expression patterns over time which, in some models, are further matched to the pattern of production/growth. The proposed cluster merging method is able to produce meaningful gene groups which can be naturally ranked by the level of agreement on the clustering among individual time series. The list of clusters and genes is further sorted based on the information correlation coefficient and new problem-specific relevant measures. Computational experiments and results of the cluster merging method are analyzed from a biological perspective and further compared with the clustering generated based on the mean value of time series and the same shape-based algorithm.

  4. REAC/TS radiation accident registry. Update of accidents in the United States

    International Nuclear Information System (INIS)

    Ricks, R.C.; Berger, M.E.; Holloway, E.C.; Goans, R.E.

    2000-01-01

    Serious injury due to ionizing radiation is a rare occurrence. From 1944 to the present, 243 US accidents meeting dose criteria for classification as serious are documented in the REAC/TS Registry. Thirty individuals have lost their lives in radiation accidents in the United States. The Registry is part of the overall REAC/TS program providing 24-hour direct or consultative assistance regarding medical and heath physics problems associated with radiation accidents in local, national, and international incidents. The REAC/TS Registry serves as a repository of medically important information documenting the consequences of these accidents. Registry data are gathered from various sources. These include reports from the World Heath Organization (WHO), International Atomic Energy Agency (IAEA), US Nuclear Regulatory Commission (US NRC), state radiological health departments, medical/health physics literature, personal communication, the Internet, and most frequently, from calls for medical assistance to REAC/TS, as part of our 24-hour medical assistance program. The REAC/TS Registry for documentation of radiation accidents serves several useful purposes: 1) weaknesses in design, safety practices, training or control can be identified, and trends noted; 2) information regarding the medical consequences of injuries and the efficacy of treatment protocols is available to the treating physician; and 3) Registry case studies serve as valuable teaching tools. This presentation will review and summarize data on the US radiation accidents including their classification by device, accident circumstances, and frequency by respective states. Data regarding accidents with fatal outcomes will be reviewed. The inclusion of Registry data in the IAEA's International Reporting System of Radiation Events (RADEV) will also be discussed. (author)

  5. Constructing networks from a dynamical system perspective for multivariate nonlinear time series.

    Science.gov (United States)

    Nakamura, Tomomichi; Tanizawa, Toshihiro; Small, Michael

    2016-03-01

    We describe a method for constructing networks for multivariate nonlinear time series. We approach the interaction between the various scalar time series from a deterministic dynamical system perspective and provide a generic and algorithmic test for whether the interaction between two measured time series is statistically significant. The method can be applied even when the data exhibit no obvious qualitative similarity: a situation in which the naive method utilizing the cross correlation function directly cannot correctly identify connectivity. To establish the connectivity between nodes we apply the previously proposed small-shuffle surrogate (SSS) method, which can investigate whether there are correlation structures in short-term variabilities (irregular fluctuations) between two data sets from the viewpoint of deterministic dynamical systems. The procedure to construct networks based on this idea is composed of three steps: (i) each time series is considered as a basic node of a network, (ii) the SSS method is applied to verify the connectivity between each pair of time series taken from the whole multivariate time series, and (iii) the pair of nodes is connected with an undirected edge when the null hypothesis cannot be rejected. The network constructed by the proposed method indicates the intrinsic (essential) connectivity of the elements included in the system or the underlying (assumed) system. The method is demonstrated for numerical data sets generated by known systems and applied to several experimental time series.

  6. Time Series Modelling of Syphilis Incidence in China from 2005 to 2012

    Science.gov (United States)

    Zhang, Xingyu; Zhang, Tao; Pei, Jiao; Liu, Yuanyuan; Li, Xiaosong; Medrano-Gracia, Pau

    2016-01-01

    Background The infection rate of syphilis in China has increased dramatically in recent decades, becoming a serious public health concern. Early prediction of syphilis is therefore of great importance for heath planning and management. Methods In this paper, we analyzed surveillance time series data for primary, secondary, tertiary, congenital and latent syphilis in mainland China from 2005 to 2012. Seasonality and long-term trend were explored with decomposition methods. Autoregressive integrated moving average (ARIMA) was used to fit a univariate time series model of syphilis incidence. A separate multi-variable time series for each syphilis type was also tested using an autoregressive integrated moving average model with exogenous variables (ARIMAX). Results The syphilis incidence rates have increased three-fold from 2005 to 2012. All syphilis time series showed strong seasonality and increasing long-term trend. Both ARIMA and ARIMAX models fitted and estimated syphilis incidence well. All univariate time series showed highest goodness-of-fit results with the ARIMA(0,0,1)×(0,1,1) model. Conclusion Time series analysis was an effective tool for modelling the historical and future incidence of syphilis in China. The ARIMAX model showed superior performance than the ARIMA model for the modelling of syphilis incidence. Time series correlations existed between the models for primary, secondary, tertiary, congenital and latent syphilis. PMID:26901682

  7. Reconstruction of tritium time series in precipitation

    International Nuclear Information System (INIS)

    Celle-Jeanton, H.; Gourcy, L.; Aggarwal, P.K.

    2002-01-01

    Tritium is commonly used in groundwaters studies to calculate the recharge rate and to identify the presence of a modern recharge. The knowledge of 3 H precipitation time series is then very important for the study of groundwater recharge. Rozanski and Araguas provided good information on precipitation tritium content in 180 stations of the GNIP network to the end of 1987, but it shows some lacks of measurements either within one chronicle or within one region (the Southern hemisphere for instance). Therefore, it seems to be essential to find a method to recalculate data for a region where no measurement is available.To solve this problem, we propose another method which is based on triangulation. It needs the knowledge of 3 H time series of 3 stations surrounding geographically the 4-th station for which tritium input curve has to be reconstructed

  8. Time Series, Stochastic Processes and Completeness of Quantum Theory

    International Nuclear Information System (INIS)

    Kupczynski, Marian

    2011-01-01

    Most of physical experiments are usually described as repeated measurements of some random variables. Experimental data registered by on-line computers form time series of outcomes. The frequencies of different outcomes are compared with the probabilities provided by the algorithms of quantum theory (QT). In spite of statistical predictions of QT a claim was made that it provided the most complete description of the data and of the underlying physical phenomena. This claim could be easily rejected if some fine structures, averaged out in the standard descriptive statistical analysis, were found in time series of experimental data. To search for these structures one has to use more subtle statistical tools which were developed to study time series produced by various stochastic processes. In this talk we review some of these tools. As an example we show how the standard descriptive statistical analysis of the data is unable to reveal a fine structure in a simulated sample of AR (2) stochastic process. We emphasize once again that the violation of Bell inequalities gives no information on the completeness or the non locality of QT. The appropriate way to test the completeness of quantum theory is to search for fine structures in time series of the experimental data by means of the purity tests or by studying the autocorrelation and partial autocorrelation functions.

  9. Efficient use of correlation entropy for analysing time series data

    Indian Academy of Sciences (India)

    Abstract. The correlation dimension D2 and correlation entropy K2 are both important quantifiers in nonlinear time series analysis. However, use of D2 has been more common compared to K2 as a discriminating measure. One reason for this is that D2 is a static measure and can be easily evaluated from a time series.

  10. Financial time series analysis based on information categorization method

    Science.gov (United States)

    Tian, Qiang; Shang, Pengjian; Feng, Guochen

    2014-12-01

    The paper mainly applies the information categorization method to analyze the financial time series. The method is used to examine the similarity of different sequences by calculating the distances between them. We apply this method to quantify the similarity of different stock markets. And we report the results of similarity in US and Chinese stock markets in periods 1991-1998 (before the Asian currency crisis), 1999-2006 (after the Asian currency crisis and before the global financial crisis), and 2007-2013 (during and after global financial crisis) by using this method. The results show the difference of similarity between different stock markets in different time periods and the similarity of the two stock markets become larger after these two crises. Also we acquire the results of similarity of 10 stock indices in three areas; it means the method can distinguish different areas' markets from the phylogenetic trees. The results show that we can get satisfactory information from financial markets by this method. The information categorization method can not only be used in physiologic time series, but also in financial time series.

  11. Classification of biosensor time series using dynamic time warping: applications in screening cancer cells with characteristic biomarkers.

    Science.gov (United States)

    Rai, Shesh N; Trainor, Patrick J; Khosravi, Farhad; Kloecker, Goetz; Panchapakesan, Balaji

    2016-01-01

    The development of biosensors that produce time series data will facilitate improvements in biomedical diagnostics and in personalized medicine. The time series produced by these devices often contains characteristic features arising from biochemical interactions between the sample and the sensor. To use such characteristic features for determining sample class, similarity-based classifiers can be utilized. However, the construction of such classifiers is complicated by the variability in the time domains of such series that renders the traditional distance metrics such as Euclidean distance ineffective in distinguishing between biological variance and time domain variance. The dynamic time warping (DTW) algorithm is a sequence alignment algorithm that can be used to align two or more series to facilitate quantifying similarity. In this article, we evaluated the performance of DTW distance-based similarity classifiers for classifying time series that mimics electrical signals produced by nanotube biosensors. Simulation studies demonstrated the positive performance of such classifiers in discriminating between time series containing characteristic features that are obscured by noise in the intensity and time domains. We then applied a DTW distance-based k -nearest neighbors classifier to distinguish the presence/absence of mesenchymal biomarker in cancer cells in buffy coats in a blinded test. Using a train-test approach, we find that the classifier had high sensitivity (90.9%) and specificity (81.8%) in differentiating between EpCAM-positive MCF7 cells spiked in buffy coats and those in plain buffy coats.

  12. A novel water quality data analysis framework based on time-series data mining.

    Science.gov (United States)

    Deng, Weihui; Wang, Guoyin

    2017-07-01

    The rapid development of time-series data mining provides an emerging method for water resource management research. In this paper, based on the time-series data mining methodology, we propose a novel and general analysis framework for water quality time-series data. It consists of two parts: implementation components and common tasks of time-series data mining in water quality data. In the first part, we propose to granulate the time series into several two-dimensional normal clouds and calculate the similarities in the granulated level. On the basis of the similarity matrix, the similarity search, anomaly detection, and pattern discovery tasks in the water quality time-series instance dataset can be easily implemented in the second part. We present a case study of this analysis framework on weekly Dissolve Oxygen time-series data collected from five monitoring stations on the upper reaches of Yangtze River, China. It discovered the relationship of water quality in the mainstream and tributary as well as the main changing patterns of DO. The experimental results show that the proposed analysis framework is a feasible and efficient method to mine the hidden and valuable knowledge from water quality historical time-series data. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Development and application of a modified dynamic time warping algorithm (DTW-S to analyses of primate brain expression time series

    Directory of Open Access Journals (Sweden)

    Vingron Martin

    2011-08-01

    Full Text Available Abstract Background Comparing biological time series data across different conditions, or different specimens, is a common but still challenging task. Algorithms aligning two time series represent a valuable tool for such comparisons. While many powerful computation tools for time series alignment have been developed, they do not provide significance estimates for time shift measurements. Results Here, we present an extended version of the original DTW algorithm that allows us to determine the significance of time shift estimates in time series alignments, the DTW-Significance (DTW-S algorithm. The DTW-S combines important properties of the original algorithm and other published time series alignment tools: DTW-S calculates the optimal alignment for each time point of each gene, it uses interpolated time points for time shift estimation, and it does not require alignment of the time-series end points. As a new feature, we implement a simulation procedure based on parameters estimated from real time series data, on a series-by-series basis, allowing us to determine the false positive rate (FPR and the significance of the estimated time shift values. We assess the performance of our method using simulation data and real expression time series from two published primate brain expression datasets. Our results show that this method can provide accurate and robust time shift estimates for each time point on a gene-by-gene basis. Using these estimates, we are able to uncover novel features of the biological processes underlying human brain development and maturation. Conclusions The DTW-S provides a convenient tool for calculating accurate and robust time shift estimates at each time point for each gene, based on time series data. The estimates can be used to uncover novel biological features of the system being studied. The DTW-S is freely available as an R package TimeShift at http://www.picb.ac.cn/Comparative/data.html.

  14. PhilDB: the time series database with built-in change logging

    Directory of Open Access Journals (Sweden)

    Andrew MacDonald

    2016-03-01

    Full Text Available PhilDB is an open-source time series database that supports storage of time series datasets that are dynamic; that is, it records updates to existing values in a log as they occur. PhilDB eases loading of data for the user by utilising an intelligent data write method. It preserves existing values during updates and abstracts the update complexity required to achieve logging of data value changes. It implements fast reads to make it practical to select data for analysis. Recent open-source systems have been developed to indefinitely store long-period high-resolution time series data without change logging. Unfortunately, such systems generally require a large initial installation investment before use because they are designed to operate over a cluster of servers to achieve high-performance writing of static data in real time. In essence, they have a ‘big data’ approach to storage and access. Other open-source projects for handling time series data that avoid the ‘big data’ approach are also relatively new and are complex or incomplete. None of these systems gracefully handle revision of existing data while tracking values that change. Unlike ‘big data’ solutions, PhilDB has been designed for single machine deployment on commodity hardware, reducing the barrier to deployment. PhilDB takes a unique approach to meta-data tracking; optional attribute attachment. This facilitates scaling the complexities of storing a wide variety of data. That is, it allows time series data to be loaded as time series instances with minimal initial meta-data, yet additional attributes can be created and attached to differentiate the time series instances when a wider variety of data is needed. PhilDB was written in Python, leveraging existing libraries. While some existing systems come close to meeting the needs PhilDB addresses, none cover all the needs at once. PhilDB was written to fill this gap in existing solutions. This paper explores existing time

  15. Model-based Clustering of Categorical Time Series with Multinomial Logit Classification

    Science.gov (United States)

    Frühwirth-Schnatter, Sylvia; Pamminger, Christoph; Winter-Ebmer, Rudolf; Weber, Andrea

    2010-09-01

    A common problem in many areas of applied statistics is to identify groups of similar time series in a panel of time series. However, distance-based clustering methods cannot easily be extended to time series data, where an appropriate distance-measure is rather difficult to define, particularly for discrete-valued time series. Markov chain clustering, proposed by Pamminger and Frühwirth-Schnatter [6], is an approach for clustering discrete-valued time series obtained by observing a categorical variable with several states. This model-based clustering method is based on finite mixtures of first-order time-homogeneous Markov chain models. In order to further explain group membership we present an extension to the approach of Pamminger and Frühwirth-Schnatter [6] by formulating a probabilistic model for the latent group indicators within the Bayesian classification rule by using a multinomial logit model. The parameters are estimated for a fixed number of clusters within a Bayesian framework using an Markov chain Monte Carlo (MCMC) sampling scheme representing a (full) Gibbs-type sampler which involves only draws from standard distributions. Finally, an application to a panel of Austrian wage mobility data is presented which leads to an interesting segmentation of the Austrian labour market.

  16. Time Series Discord Detection in Medical Data using a Parallel Relational Database

    Energy Technology Data Exchange (ETDEWEB)

    Woodbridge, Diane; Rintoul, Mark Daniel; Wilson, Andrew T.; Goldstein, Richard

    2015-10-01

    Recent advances in sensor technology have made continuous real-time health monitoring available in both hospital and non-hospital settings. Since data collected from high frequency medical sensors includes a huge amount of data, storing and processing continuous medical data is an emerging big data area. Especially detecting anomaly in real time is important for patients’ emergency detection and prevention. A time series discord indicates a subsequence that has the maximum difference to the rest of the time series subsequences, meaning that it has abnormal or unusual data trends. In this study, we implemented two versions of time series discord detection algorithms on a high performance parallel database management system (DBMS) and applied them to 240 Hz waveform data collected from 9,723 patients. The initial brute force version of the discord detection algorithm takes each possible subsequence and calculates a distance to the nearest non-self match to find the biggest discords in time series. For the heuristic version of the algorithm, a combination of an array and a trie structure was applied to order time series data for enhancing time efficiency. The study results showed efficient data loading, decoding and discord searches in a large amount of data, benefiting from the time series discord detection algorithm and the architectural characteristics of the parallel DBMS including data compression, data pipe-lining, and task scheduling.

  17. Estimation of system parameters in discrete dynamical systems from time series

    International Nuclear Information System (INIS)

    Palaniyandi, P.; Lakshmanan, M.

    2005-01-01

    We propose a simple method to estimate the parameters involved in discrete dynamical systems from time series. The method is based on the concept of controlling chaos by constant feedback. The major advantages of the method are that it needs a minimal number of time series data (either vector or scalar) and is applicable to dynamical systems of any dimension. The method also works extremely well even in the presence of noise in the time series. The method is specifically illustrated by means of logistic and Henon maps

  18. Evaluation of nonlinearity and validity of nonlinear modeling for complex time series.

    Science.gov (United States)

    Suzuki, Tomoya; Ikeguchi, Tohru; Suzuki, Masuo

    2007-10-01

    Even if an original time series exhibits nonlinearity, it is not always effective to approximate the time series by a nonlinear model because such nonlinear models have high complexity from the viewpoint of information criteria. Therefore, we propose two measures to evaluate both the nonlinearity of a time series and validity of nonlinear modeling applied to it by nonlinear predictability and information criteria. Through numerical simulations, we confirm that the proposed measures effectively detect the nonlinearity of an observed time series and evaluate the validity of the nonlinear model. The measures are also robust against observational noises. We also analyze some real time series: the difference of the number of chickenpox and measles patients, the number of sunspots, five Japanese vowels, and the chaotic laser. We can confirm that the nonlinear model is effective for the Japanese vowel /a/, the difference of the number of measles patients, and the chaotic laser.

  19. A Framework and Algorithms for Multivariate Time Series Analytics (MTSA): Learning, Monitoring, and Recommendation

    Science.gov (United States)

    Ngan, Chun-Kit

    2013-01-01

    Making decisions over multivariate time series is an important topic which has gained significant interest in the past decade. A time series is a sequence of data points which are measured and ordered over uniform time intervals. A multivariate time series is a set of multiple, related time series in a particular domain in which domain experts…

  20. Modeling vector nonlinear time series using POLYMARS

    NARCIS (Netherlands)

    de Gooijer, J.G.; Ray, B.K.

    2003-01-01

    A modified multivariate adaptive regression splines method for modeling vector nonlinear time series is investigated. The method results in models that can capture certain types of vector self-exciting threshold autoregressive behavior, as well as provide good predictions for more general vector

  1. Forecasting with periodic autoregressive time series models

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans); R. Paap (Richard)

    1999-01-01

    textabstractThis paper is concerned with forecasting univariate seasonal time series data using periodic autoregressive models. We show how one should account for unit roots and deterministic terms when generating out-of-sample forecasts. We illustrate the models for various quarterly UK consumption

  2. vector bilinear autoregressive time series model and its superiority

    African Journals Online (AJOL)

    KEYWORDS: Linear time series, Autoregressive process, Autocorrelation function, Partial autocorrelation function,. Vector time .... important result on matrix algebra with respect to the spectral ..... application to covariance analysis of super-.

  3. Correlation measure to detect time series distances, whence economy globalization

    Science.gov (United States)

    Miśkiewicz, Janusz; Ausloos, Marcel

    2008-11-01

    An instantaneous time series distance is defined through the equal time correlation coefficient. The idea is applied to the Gross Domestic Product (GDP) yearly increments of 21 rich countries between 1950 and 2005 in order to test the process of economic globalisation. Some data discussion is first presented to decide what (EKS, GK, or derived) GDP series should be studied. Distances are then calculated from the correlation coefficient values between pairs of series. The role of time averaging of the distances over finite size windows is discussed. Three network structures are next constructed based on the hierarchy of distances. It is shown that the mean distance between the most developed countries on several networks actually decreases in time, -which we consider as a proof of globalization. An empirical law is found for the evolution after 1990, similar to that found in flux creep. The optimal observation time window size is found ≃15 years.

  4. Time Series Analysis Using Geometric Template Matching.

    Science.gov (United States)

    Frank, Jordan; Mannor, Shie; Pineau, Joelle; Precup, Doina

    2013-03-01

    We present a novel framework for analyzing univariate time series data. At the heart of the approach is a versatile algorithm for measuring the similarity of two segments of time series called geometric template matching (GeTeM). First, we use GeTeM to compute a similarity measure for clustering and nearest-neighbor classification. Next, we present a semi-supervised learning algorithm that uses the similarity measure with hierarchical clustering in order to improve classification performance when unlabeled training data are available. Finally, we present a boosting framework called TDEBOOST, which uses an ensemble of GeTeM classifiers. TDEBOOST augments the traditional boosting approach with an additional step in which the features used as inputs to the classifier are adapted at each step to improve the training error. We empirically evaluate the proposed approaches on several datasets, such as accelerometer data collected from wearable sensors and ECG data.

  5. On-line analysis of reactor noise using time-series analysis

    International Nuclear Information System (INIS)

    McGevna, V.G.

    1981-10-01

    A method to allow use of time series analysis for on-line noise analysis has been developed. On-line analysis of noise in nuclear power reactors has been limited primarily to spectral analysis and related frequency domain techniques. Time series analysis has many distinct advantages over spectral analysis in the automated processing of reactor noise. However, fitting an autoregressive-moving average (ARMA) model to time series data involves non-linear least squares estimation. Unless a high speed, general purpose computer is available, the calculations become too time consuming for on-line applications. To eliminate this problem, a special purpose algorithm was developed for fitting ARMA models. While it is based on a combination of steepest descent and Taylor series linearization, properties of the ARMA model are used so that the auto- and cross-correlation functions can be used to eliminate the need for estimating derivatives. The number of calculations, per iteration varies lineegardless of the mee 0.2% yield strength displayed anisotropy, with axial and circumferential values being greater than radial. For CF8-CPF8 and CF8M-CPF8M castings to meet current ASME Code S acid fuel cells

  6. Improving GNSS time series for volcano monitoring: application to Canary Islands (Spain)

    Science.gov (United States)

    García-Cañada, Laura; Sevilla, Miguel J.; Pereda de Pablo, Jorge; Domínguez Cerdeña, Itahiza

    2017-04-01

    The number of permanent GNSS stations has increased significantly in recent years for different geodetic applications such as volcano monitoring, which require a high precision. Recently we have started to have coordinates time series long enough so that we can apply different analysis and filters that allow us to improve the GNSS coordinates results. Following this idea we have processed data from GNSS permanent stations used by the Spanish Instituto Geográfico Nacional (IGN) for volcano monitoring in Canary Islands to obtained time series by double difference processing method with Bernese v5.0 for the period 2007-2014. We have identified the characteristics of these time series and obtained models to estimate velocities with greater accuracy and more realistic uncertainties. In order to improve the results we have used two kinds of filters to improve the time series. The first, a spatial filter, has been computed using the series of residuals of all stations in the Canary Islands without an anomalous behaviour after removing a linear trend. This allows us to apply this filter to all sets of coordinates of the permanent stations reducing their dispersion. The second filter takes account of the temporal correlation in the coordinate time series for each station individually. A research about the evolution of the velocity depending on the series length has been carried out and it has demonstrated the need for using time series of at least four years. Therefore, in those stations with more than four years of data, we calculated the velocity and the characteristic parameters in order to have time series of residuals. This methodology has been applied to the GNSS data network in El Hierro (Canary Islands) during the 2011-2012 eruption and the subsequent magmatic intrusions (2012-2014). The results show that in the new series it is easier to detect anomalous behaviours in the coordinates, so they are most useful to detect crustal deformations in volcano monitoring.

  7. Complexity analysis of the turbulent environmental fluid flow time series

    Science.gov (United States)

    Mihailović, D. T.; Nikolić-Đorić, E.; Drešković, N.; Mimić, G.

    2014-02-01

    We have used the Kolmogorov complexities, sample and permutation entropies to quantify the randomness degree in river flow time series of two mountain rivers in Bosnia and Herzegovina, representing the turbulent environmental fluid, for the period 1926-1990. In particular, we have examined the monthly river flow time series from two rivers (the Miljacka and the Bosnia) in the mountain part of their flow and then calculated the Kolmogorov complexity (KL) based on the Lempel-Ziv Algorithm (LZA) (lower-KLL and upper-KLU), sample entropy (SE) and permutation entropy (PE) values for each time series. The results indicate that the KLL, KLU, SE and PE values in two rivers are close to each other regardless of the amplitude differences in their monthly flow rates. We have illustrated the changes in mountain river flow complexity by experiments using (i) the data set for the Bosnia River and (ii) anticipated human activities and projected climate changes. We have explored the sensitivity of considered measures in dependence on the length of time series. In addition, we have divided the period 1926-1990 into three subintervals: (a) 1926-1945, (b) 1946-1965, (c) 1966-1990, and calculated the KLL, KLU, SE, PE values for the various time series in these subintervals. It is found that during the period 1946-1965, there is a decrease in their complexities, and corresponding changes in the SE and PE, in comparison to the period 1926-1990. This complexity loss may be primarily attributed to (i) human interventions, after the Second World War, on these two rivers because of their use for water consumption and (ii) climate change in recent times.

  8. Mapping Crop Cycles in China Using MODIS-EVI Time Series

    Directory of Open Access Journals (Sweden)

    Le Li

    2014-03-01

    Full Text Available As the Earth’s population continues to grow and demand for food increases, the need for improved and timely information related to the properties and dynamics of global agricultural systems is becoming increasingly important. Global land cover maps derived from satellite data provide indispensable information regarding the geographic distribution and areal extent of global croplands. However, land use information, such as cropping intensity (defined here as the number of cropping cycles per year, is not routinely available over large areas because mapping this information from remote sensing is challenging. In this study, we present a simple but efficient algorithm for automated mapping of cropping intensity based on data from NASA’s (NASA: The National Aeronautics and Space Administration MODerate Resolution Imaging Spectroradiometer (MODIS. The proposed algorithm first applies an adaptive Savitzky-Golay filter to smooth Enhanced Vegetation Index (EVI time series derived from MODIS surface reflectance data. It then uses an iterative moving-window methodology to identify cropping cycles from the smoothed EVI time series. Comparison of results from our algorithm with national survey data at both the provincial and prefectural level in China show that the algorithm provides estimates of gross sown area that agree well with inventory data. Accuracy assessment comparing visually interpreted time series with algorithm results for a random sample of agricultural areas in China indicates an overall accuracy of 91.0% for three classes defined based on the number of cycles observed in EVI time series. The algorithm therefore appears to provide a straightforward and efficient method for mapping cropping intensity from MODIS time series data.

  9. T.S. ELIOT'S MISREADING OF SOME LITERARY SOURCES IN THE WASTE LAND

    Directory of Open Access Journals (Sweden)

    Liem Satya Limanta

    2002-01-01

    Full Text Available A poet cannot be a poet at all if he is not connected with all the poetic tradition before him. This is T.S. Eliot's dictum which he stated in Tradition and the Individual Talent and which he practiced in his work The Waste Land. T.S. Eliot showed his relationship with the past through all the quotations and allusions to mythical, literary, and religious works. His reading of those sources according to Paul de Man cannot avoid misreading or misinterpretation. Harold Bloom has tried to build up a theory of misreading, which he prefers to call misprision, especially applied to and done by poets in The Anxiety of Influence. Throughout this article I shall show how T.S. Eliot has applied some of the ways Bloom describes in reading some of the literary sources, and by so doing Eliot has given new meaning to them.

  10. Spectral Estimation of UV-Vis Absorbance Time Series for Water Quality Monitoring

    Directory of Open Access Journals (Sweden)

    Leonardo Plazas-Nossa

    2017-05-01

    Full Text Available Context: Signals recorded as multivariate time series by UV-Vis absorbance captors installed in urban sewer systems, can be non-stationary, yielding complications in the analysis of water quality monitoring. This work proposes to perform spectral estimation using the Box-Cox transformation and differentiation in order to obtain stationary multivariate time series in a wide sense. Additionally, Principal Component Analysis (PCA is applied to reduce their dimensionality. Method: Three different UV-Vis absorbance time series for different Colombian locations were studied: (i El-Salitre Wastewater Treatment Plant (WWTP in Bogotá; (ii Gibraltar Pumping Station (GPS in Bogotá; and (iii San-Fernando WWTP in Itagüí. Each UV-Vis absorbance time series had equal sample number (5705. The esti-mation of the spectral power density is obtained using the average of modified periodograms with rectangular window and an overlap of 50%, with the 20 most important harmonics from the Discrete Fourier Transform (DFT and Inverse Fast Fourier Transform (IFFT. Results: Absorbance time series dimensionality reduction using PCA, resulted in 6, 8 and 7 principal components for each study site respectively, altogether explaining more than 97% of their variability. Values of differences below 30% for the UV range were obtained for the three study sites, while for the visible range the maximum differences obtained were: (i 35% for El-Salitre WWTP; (ii 61% for GPS; and (iii 75% for San-Fernando WWTP. Conclusions: The Box-Cox transformation and the differentiation process applied to the UV-Vis absorbance time series for the study sites (El-Salitre, GPS and San-Fernando, allowed to reduce variance and to eliminate ten-dency of the time series. A pre-processing of UV-Vis absorbance time series is recommended to detect and remove outliers and then apply the proposed process for spectral estimation. Language: Spanish.

  11. Toward automatic time-series forecasting using neural networks.

    Science.gov (United States)

    Yan, Weizhong

    2012-07-01

    Over the past few decades, application of artificial neural networks (ANN) to time-series forecasting (TSF) has been growing rapidly due to several unique features of ANN models. However, to date, a consistent ANN performance over different studies has not been achieved. Many factors contribute to the inconsistency in the performance of neural network models. One such factor is that ANN modeling involves determining a large number of design parameters, and the current design practice is essentially heuristic and ad hoc, this does not exploit the full potential of neural networks. Systematic ANN modeling processes and strategies for TSF are, therefore, greatly needed. Motivated by this need, this paper attempts to develop an automatic ANN modeling scheme. It is based on the generalized regression neural network (GRNN), a special type of neural network. By taking advantage of several GRNN properties (i.e., a single design parameter and fast learning) and by incorporating several design strategies (e.g., fusing multiple GRNNs), we have been able to make the proposed modeling scheme to be effective for modeling large-scale business time series. The initial model was entered into the NN3 time-series competition. It was awarded the best prediction on the reduced dataset among approximately 60 different models submitted by scholars worldwide.

  12. Experimental data report for Test TS-2 reactivity initiated accident test in NSRR with pre-irradiated BWR fuel rod

    International Nuclear Information System (INIS)

    Nakamura, Takehiko; Yoshinaga, Makio; Sobajima, Makoto; Fujishiro, Toshio; Kobayashi, Shinsho; Yamahara, Takeshi; Sukegawa, Tomohide; Kikuchi, Teruo

    1993-02-01

    This report presents experimental data for Test TS-2 which was the second test in a series of Reactivity Initiated Accident (RIA) condition test using pre-irradiated BWR fuel rods, performed at the Nuclear Safety Research Reactor (NSRR) in February, 1990. Test fuel rod used in the Test TS-2 was a short sized BWR (7x7) type rod which was fabricated from a commercial rod irradiated at Tsuruga Unit 1 power reactor. The fuel had an initial enrichment of 2.79% and a burnup of 21.3Gwd/tU (bundle average). A pulse irradiation of the test fuel rod was performed under a cooling condition of stagnant water at atmospheric pressure and at ambient temperature which simulated a BWR's cold start-up RIA event. The energy deposition of the fuel rod in this test was evaluated to be 72±5cal/g·fuel (66±5cal/g·fuel in peak fuel enthalpy) and no fuel failure was observed. Descriptions on test conditions, test procedures, transient behavior of the test rod during the pulse irradiation, and, results of pre and post pulse irradiation examinations are described in this report. (author)

  13. Multi-granular trend detection for time-series analysis

    NARCIS (Netherlands)

    van Goethem, A.I.; Staals, F.; Löffler, M.; Dykes, J.; Speckmann, B.

    2017-01-01

    Time series (such as stock prices) and ensembles (such as model runs for weather forecasts) are two important types of one-dimensional time-varying data. Such data is readily available in large quantities but visual analysis of the raw data quickly becomes infeasible, even for moderately sized data

  14. The Timeseries Toolbox - A Web Application to Enable Accessible, Reproducible Time Series Analysis

    Science.gov (United States)

    Veatch, W.; Friedman, D.; Baker, B.; Mueller, C.

    2017-12-01

    The vast majority of data analyzed by climate researchers are repeated observations of physical process or time series data. This data lends itself of a common set of statistical techniques and models designed to determine trends and variability (e.g., seasonality) of these repeated observations. Often, these same techniques and models can be applied to a wide variety of different time series data. The Timeseries Toolbox is a web application designed to standardize and streamline these common approaches to time series analysis and modeling with particular attention to hydrologic time series used in climate preparedness and resilience planning and design by the U. S. Army Corps of Engineers. The application performs much of the pre-processing of time series data necessary for more complex techniques (e.g. interpolation, aggregation). With this tool, users can upload any dataset that conforms to a standard template and immediately begin applying these techniques to analyze their time series data.

  15. Optimal transformations for categorical autoregressive time series

    NARCIS (Netherlands)

    Buuren, S. van

    1996-01-01

    This paper describes a method for finding optimal transformations for analyzing time series by autoregressive models. 'Optimal' implies that the agreement between the autoregressive model and the transformed data is maximal. Such transformations help 1) to increase the model fit, and 2) to analyze

  16. Satellite Image Time Series Decomposition Based on EEMD

    Directory of Open Access Journals (Sweden)

    Yun-long Kong

    2015-11-01

    Full Text Available Satellite Image Time Series (SITS have recently been of great interest due to the emerging remote sensing capabilities for Earth observation. Trend and seasonal components are two crucial elements of SITS. In this paper, a novel framework of SITS decomposition based on Ensemble Empirical Mode Decomposition (EEMD is proposed. EEMD is achieved by sifting an ensemble of adaptive orthogonal components called Intrinsic Mode Functions (IMFs. EEMD is noise-assisted and overcomes the drawback of mode mixing in conventional Empirical Mode Decomposition (EMD. Inspired by these advantages, the aim of this work is to employ EEMD to decompose SITS into IMFs and to choose relevant IMFs for the separation of seasonal and trend components. In a series of simulations, IMFs extracted by EEMD achieved a clear representation with physical meaning. The experimental results of 16-day compositions of Moderate Resolution Imaging Spectroradiometer (MODIS, Normalized Difference Vegetation Index (NDVI, and Global Environment Monitoring Index (GEMI time series with disturbance illustrated the effectiveness and stability of the proposed approach to monitoring tasks, such as applications for the detection of abrupt changes.

  17. An accuracy assessment of realtime GNSS time series toward semi- real time seafloor geodetic observation

    Science.gov (United States)

    Osada, Y.; Ohta, Y.; Demachi, T.; Kido, M.; Fujimoto, H.; Azuma, R.; Hino, R.

    2013-12-01

    Large interplate earthquake repeatedly occurred in Japan Trench. Recently, the detail crustal deformation revealed by the nation-wide inland GPS network called as GEONET by GSI. However, the maximum displacement region for interplate earthquake is mainly located offshore region. GPS/Acoustic seafloor geodetic observation (hereafter GPS/A) is quite important and useful for understanding of shallower part of the interplate coupling between subducting and overriding plates. We typically conduct GPS/A in specific ocean area based on repeated campaign style using research vessel or buoy. Therefore, we cannot monitor the temporal variation of seafloor crustal deformation in real time. The one of technical issue on real time observation is kinematic GPS analysis because kinematic GPS analysis based on reference and rover data. If the precise kinematic GPS analysis will be possible in the offshore region, it should be promising method for real time GPS/A with USV (Unmanned Surface Vehicle) and a moored buoy. We assessed stability, precision and accuracy of StarFireTM global satellites based augmentation system. We primarily tested for StarFire in the static condition. In order to assess coordinate precision and accuracy, we compared 1Hz StarFire time series and post-processed precise point positioning (PPP) 1Hz time series by GIPSY-OASIS II processing software Ver. 6.1.2 with three difference product types (ultra-rapid, rapid, and final orbits). We also used difference interval clock information (30 and 300 seconds) for the post-processed PPP processing. The standard deviation of real time StarFire time series is less than 30 mm (horizontal components) and 60 mm (vertical component) based on 1 month continuous processing. We also assessed noise spectrum of the estimated time series by StarFire and post-processed GIPSY PPP results. We found that the noise spectrum of StarFire time series is similar pattern with GIPSY-OASIS II processing result based on JPL rapid orbit

  18. The Exponential Model for the Spectrum of a Time Series: Extensions and Applications

    DEFF Research Database (Denmark)

    Proietti, Tommaso; Luati, Alessandra

    The exponential model for the spectrum of a time series and its fractional extensions are based on the Fourier series expansion of the logarithm of the spectral density. The coefficients of the expansion form the cepstrum of the time series. After deriving the cepstrum of important classes of time...

  19. Digastric Muscle Phenotypes of the Ts65Dn Mouse Model of Down Syndrome.

    Directory of Open Access Journals (Sweden)

    Tiffany J Glass

    Full Text Available Down syndrome is frequently associated with complex difficulties in oromotor development, feeding, and swallowing. However, the muscle phenotypes underlying these deficits are unclear. We tested the hypotheses that the Ts65Dn mouse model of DS has significantly altered myosin heavy chain (MyHC isoform profiles of the muscles involved in feeding and swallowing, as well as reductions in the speed of these movements during behavioral assays. SDS-PAGE, immunofluorescence, and qRT-PCR were used to assess MyHC isoform expression in pertinent muscles, and functional feeding and swallowing performance were quantified through videofluoroscopy and mastication assays. We found that both the anterior digastric (ADG and posterior digastric (PDG muscles in 11-day old and 5-6 week old Ts65Dn groups showed significantly lower MyHC 2b protein levels than in age-matched euploid control groups. In videofluoroscopic and videotape assays used to quantify swallowing and mastication performance, 5-6 week old Ts65Dn and euploid controls showed similar swallow rates, inter-swallow intervals, and mastication rates. In analysis of adults, 10-11 week old Ts65Dn mice revealed significantly less MyHC 2b mRNA expression in the posterior digastric, but not the anterior digastric muscle as compared with euploid controls. Analysis of MyHC 2b protein levels across an adult age range (10-53 weeks of age revealed lower levels of MyHC 2b protein in the PDG of Ts65Dn than in euploids, but similar levels of MyHC 2b in the ADG. Cumulatively, these results indicate biochemical differences in some, but not all, muscles involved in swallowing and jaw movement in Ts65Dn mice that manifest early in post-natal development, and persist into adulthood. These findings suggest potential utility of this model for future investigations of the mechanisms of oromotor difficulties associated with Down syndrome.

  20. Enteroclysis and small bowel series: Comparison of radiation dose and examination time

    International Nuclear Information System (INIS)

    Thoeni, R.F.; Gould, R.G.

    1991-01-01

    Respective radiation doses and total examination and fluoroscopy times were compared for 50 patients; 25 underwent enteroclysis and 25 underwent small bowel series with (n = 17) and without (n = 8) an examination of the upper gastrointestinal (GI) tract. For enteroclysis, the mean skin entry radiation dose (12.3 rad [123 mGy]) and mean fluoroscopy time (18.4 minutes) were almost 1 1/2 times greater than those for the small bowel series with examination of the upper GI tract (8.4 rad [84 mGy]; 11.4 minutes) and almost three times greater than those for the small bowel series without upper GI examination (4.6 rad [46 mGy]; 6.3 minutes). However, the mean total examination completion time for enteroclysis (31.2 minutes) was almost half that of the small bowel series without upper GI examination (57.5 minutes) and almost four times shorter than that of the small bowel series with upper GI examination (114 minutes). The higher radiation dose of enteroclysis should be considered along with the short examination time, the age and clinical condition of the patient, and the reported higher accuracy when deciding on the appropriate radiographic examination of the small bowel

  1. Rotation in the dynamic factor modeling of multivariate stationary time series.

    NARCIS (Netherlands)

    Molenaar, P.C.M.; Nesselroade, J.R.

    2001-01-01

    A special rotation procedure is proposed for the exploratory dynamic factor model for stationary multivariate time series. The rotation procedure applies separately to each univariate component series of a q-variate latent factor series and transforms such a component, initially represented as white

  2. A simple and fast representation space for classifying complex time series

    International Nuclear Information System (INIS)

    Zunino, Luciano; Olivares, Felipe; Bariviera, Aurelio F.; Rosso, Osvaldo A.

    2017-01-01

    In the context of time series analysis considerable effort has been directed towards the implementation of efficient discriminating statistical quantifiers. Very recently, a simple and fast representation space has been introduced, namely the number of turning points versus the Abbe value. It is able to separate time series from stationary and non-stationary processes with long-range dependences. In this work we show that this bidimensional approach is useful for distinguishing complex time series: different sets of financial and physiological data are efficiently discriminated. Additionally, a multiscale generalization that takes into account the multiple time scales often involved in complex systems has been also proposed. This multiscale analysis is essential to reach a higher discriminative power between physiological time series in health and disease. - Highlights: • A bidimensional scheme has been tested for classification purposes. • A multiscale generalization is introduced. • Several practical applications confirm its usefulness. • Different sets of financial and physiological data are efficiently distinguished. • This multiscale bidimensional approach has high potential as discriminative tool.

  3. A simple and fast representation space for classifying complex time series

    Energy Technology Data Exchange (ETDEWEB)

    Zunino, Luciano, E-mail: lucianoz@ciop.unlp.edu.ar [Centro de Investigaciones Ópticas (CONICET La Plata – CIC), C.C. 3, 1897 Gonnet (Argentina); Departamento de Ciencias Básicas, Facultad de Ingeniería, Universidad Nacional de La Plata (UNLP), 1900 La Plata (Argentina); Olivares, Felipe, E-mail: olivaresfe@gmail.com [Instituto de Física, Pontificia Universidad Católica de Valparaíso (PUCV), 23-40025 Valparaíso (Chile); Bariviera, Aurelio F., E-mail: aurelio.fernandez@urv.cat [Department of Business, Universitat Rovira i Virgili, Av. Universitat 1, 43204 Reus (Spain); Rosso, Osvaldo A., E-mail: oarosso@gmail.com [Instituto de Física, Universidade Federal de Alagoas (UFAL), BR 104 Norte km 97, 57072-970, Maceió, Alagoas (Brazil); Instituto Tecnológico de Buenos Aires (ITBA) and CONICET, C1106ACD, Av. Eduardo Madero 399, Ciudad Autónoma de Buenos Aires (Argentina); Complex Systems Group, Facultad de Ingeniería y Ciencias Aplicadas, Universidad de los Andes, Av. Mons. Álvaro del Portillo 12.455, Las Condes, Santiago (Chile)

    2017-03-18

    In the context of time series analysis considerable effort has been directed towards the implementation of efficient discriminating statistical quantifiers. Very recently, a simple and fast representation space has been introduced, namely the number of turning points versus the Abbe value. It is able to separate time series from stationary and non-stationary processes with long-range dependences. In this work we show that this bidimensional approach is useful for distinguishing complex time series: different sets of financial and physiological data are efficiently discriminated. Additionally, a multiscale generalization that takes into account the multiple time scales often involved in complex systems has been also proposed. This multiscale analysis is essential to reach a higher discriminative power between physiological time series in health and disease. - Highlights: • A bidimensional scheme has been tested for classification purposes. • A multiscale generalization is introduced. • Several practical applications confirm its usefulness. • Different sets of financial and physiological data are efficiently distinguished. • This multiscale bidimensional approach has high potential as discriminative tool.

  4. A Combination of TsHARP and Thin Plate Spline Interpolation for Spatial Sharpening of Thermal Imagery

    Directory of Open Access Journals (Sweden)

    Xuehong Chen

    2014-03-01

    Full Text Available There have been many studies and much attention paid to spatial sharpening for thermal imagery. Among them, TsHARP, based on the good correlation between vegetation index and land surface temperature (LST, is regarded as a standard technique because of its operational simplicity and effectiveness. However, as LST is affected by other factors (e.g., soil moisture in the areas with low vegetation cover, these areas cannot be well sharpened by TsHARP. Thin plate spline (TPS is another popular downscaling technique for surface data. It has been shown to be accurate and robust for different datasets; however, it has not yet been attempted in thermal sharpening. This paper proposes to combine the TsHARP and TPS methods to enhance the advantages of each. The spatially explicit errors of these two methods were firstly estimated in theory, and then the results of TPS and TsHARP were combined with the estimation of their errors. The experiments performed across various landscapes and data showed that the proposed combined method performs more robustly and accurately than TsHARP.

  5. Visibility graphlet approach to chaotic time series

    Energy Technology Data Exchange (ETDEWEB)

    Mutua, Stephen [Business School, University of Shanghai for Science and Technology, Shanghai 200093 (China); Computer Science Department, Masinde Muliro University of Science and Technology, P.O. Box 190-50100, Kakamega (Kenya); Gu, Changgui, E-mail: gu-changgui@163.com, E-mail: hjyang@ustc.edu.cn; Yang, Huijie, E-mail: gu-changgui@163.com, E-mail: hjyang@ustc.edu.cn [Business School, University of Shanghai for Science and Technology, Shanghai 200093 (China)

    2016-05-15

    Many novel methods have been proposed for mapping time series into complex networks. Although some dynamical behaviors can be effectively captured by existing approaches, the preservation and tracking of the temporal behaviors of a chaotic system remains an open problem. In this work, we extended the visibility graphlet approach to investigate both discrete and continuous chaotic time series. We applied visibility graphlets to capture the reconstructed local states, so that each is treated as a node and tracked downstream to create a temporal chain link. Our empirical findings show that the approach accurately captures the dynamical properties of chaotic systems. Networks constructed from periodic dynamic phases all converge to regular networks and to unique network structures for each model in the chaotic zones. Furthermore, our results show that the characterization of chaotic and non-chaotic zones in the Lorenz system corresponds to the maximal Lyapunov exponent, thus providing a simple and straightforward way to analyze chaotic systems.

  6. Fast and Flexible Multivariate Time Series Subsequence Search

    Data.gov (United States)

    National Aeronautics and Space Administration — Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical...

  7. Automated Feature Design for Time Series Classification by Genetic Programming

    OpenAIRE

    Harvey, Dustin Yewell

    2014-01-01

    Time series classification (TSC) methods discover and exploit patterns in time series and other one-dimensional signals. Although many accurate, robust classifiers exist for multivariate feature sets, general approaches are needed to extend machine learning techniques to make use of signal inputs. Numerous applications of TSC can be found in structural engineering, especially in the areas of structural health monitoring and non-destructive evaluation. Additionally, the fields of process contr...

  8. The use of synthetic input sequences in time series modeling

    International Nuclear Information System (INIS)

    Oliveira, Dair Jose de; Letellier, Christophe; Gomes, Murilo E.D.; Aguirre, Luis A.

    2008-01-01

    In many situations time series models obtained from noise-like data settle to trivial solutions under iteration. This Letter proposes a way of producing a synthetic (dummy) input, that is included to prevent the model from settling down to a trivial solution, while maintaining features of the original signal. Simulated benchmark models and a real time series of RR intervals from an ECG are used to illustrate the procedure

  9. Near-Real-Time Monitoring of Insect Defoliation Using Landsat Time Series

    Directory of Open Access Journals (Sweden)

    Valerie J. Pasquarella

    2017-07-01

    Full Text Available Introduced insects and pathogens impact millions of acres of forested land in the United States each year, and large-scale monitoring efforts are essential for tracking the spread of outbreaks and quantifying the extent of damage. However, monitoring the impacts of defoliating insects presents a significant challenge due to the ephemeral nature of defoliation events. Using the 2016 gypsy moth (Lymantria dispar outbreak in Southern New England as a case study, we present a new approach for near-real-time defoliation monitoring using synthetic images produced from Landsat time series. By comparing predicted and observed images, we assessed changes in vegetation condition multiple times over the course of an outbreak. Initial measures can be made as imagery becomes available, and season-integrated products provide a wall-to-wall assessment of potential defoliation at 30 m resolution. Qualitative and quantitative comparisons suggest our Landsat Time Series (LTS products improve identification of defoliation events relative to existing products and provide a repeatable metric of change in condition. Our synthetic-image approach is an important step toward using the full temporal potential of the Landsat archive for operational monitoring of forest health over large extents, and provides an important new tool for understanding spatial and temporal dynamics of insect defoliators.

  10. Analysis of three amphibian populations with quarter-century long time-series.

    OpenAIRE

    Meyer, A H; Schimidt, B R; Grossenbacher, K

    1998-01-01

    Amphibians are in decline in many parts of the world. Long tme-series of amphibian populations are necessary to distinguish declines from the often strong fluctuations observed in natural populations. Time-series may also help to understand the causes of these declines. We analysed 23-28-year long time-series of the frog Rana temporaria. Only one of the three studied populations showed a negative trend which was probably caused by the introduction of fish. Two populations appeared to be densi...

  11. Robust Control Charts for Time Series Data

    NARCIS (Netherlands)

    Croux, C.; Gelper, S.; Mahieu, K.

    2010-01-01

    This article presents a control chart for time series data, based on the one-step- ahead forecast errors of the Holt-Winters forecasting method. We use robust techniques to prevent that outliers affect the estimation of the control limits of the chart. Moreover, robustness is important to maintain

  12. Lecture notes for Advanced Time Series Analysis

    DEFF Research Database (Denmark)

    Madsen, Henrik; Holst, Jan

    1997-01-01

    A first version of this notes was used at the lectures in Grenoble, and they are now extended and improved (together with Jan Holst), and used in Ph.D. courses on Advanced Time Series Analysis at IMM and at the Department of Mathematical Statistics, University of Lund, 1994, 1997, ...

  13. Predicting long-term catchment nutrient export: the use of nonlinear time series models

    Science.gov (United States)

    Valent, Peter; Howden, Nicholas J. K.; Szolgay, Jan; Komornikova, Magda

    2010-05-01

    After the Second World War the nitrate concentrations in European water bodies changed significantly as the result of increased nitrogen fertilizer use and changes in land use. However, in the last decades, as a consequence of the implementation of nitrate-reducing measures in Europe, the nitrate concentrations in water bodies slowly decrease. This causes that the mean and variance of the observed time series also changes with time (nonstationarity and heteroscedascity). In order to detect changes and properly describe the behaviour of such time series by time series analysis, linear models (such as autoregressive (AR), moving average (MA) and autoregressive moving average models (ARMA)), are no more suitable. Time series with sudden changes in statistical characteristics can cause various problems in the calibration of traditional water quality models and thus give biased predictions. Proper statistical analysis of these non-stationary and heteroscedastic time series with the aim of detecting and subsequently explaining the variations in their statistical characteristics requires the use of nonlinear time series models. This information can be then used to improve the model building and calibration of conceptual water quality model or to select right calibration periods in order to produce reliable predictions. The objective of this contribution is to analyze two long time series of nitrate concentrations of the rivers Ouse and Stour with advanced nonlinear statistical modelling techniques and compare their performance with traditional linear models of the ARMA class in order to identify changes in the time series characteristics. The time series were analysed with nonlinear models with multiple regimes represented by self-exciting threshold autoregressive (SETAR) and Markov-switching models (MSW). The analysis showed that, based on the value of residual sum of squares (RSS) in both datasets, SETAR and MSW models described the time-series better than models of the

  14. Extracting biologically significant patterns from short time series gene expression data

    Directory of Open Access Journals (Sweden)

    McGinnis Thomas

    2009-08-01

    Full Text Available Abstract Background Time series gene expression data analysis is used widely to study the dynamics of various cell processes. Most of the time series data available today consist of few time points only, thus making the application of standard clustering techniques difficult. Results We developed two new algorithms that are capable of extracting biological patterns from short time point series gene expression data. The two algorithms, ASTRO and MiMeSR, are inspired by the rank order preserving framework and the minimum mean squared residue approach, respectively. However, ASTRO and MiMeSR differ from previous approaches in that they take advantage of the relatively few number of time points in order to reduce the problem from NP-hard to linear. Tested on well-defined short time expression data, we found that our approaches are robust to noise, as well as to random patterns, and that they can correctly detect the temporal expression profile of relevant functional categories. Evaluation of our methods was performed using Gene Ontology (GO annotations and chromatin immunoprecipitation (ChIP-chip data. Conclusion Our approaches generally outperform both standard clustering algorithms and algorithms designed specifically for clustering of short time series gene expression data. Both algorithms are available at http://www.benoslab.pitt.edu/astro/.

  15. The application of complex network time series analysis in turbulent heated jets

    International Nuclear Information System (INIS)

    Charakopoulos, A. K.; Karakasidis, T. E.; Liakopoulos, A.; Papanicolaou, P. N.

    2014-01-01

    In the present study, we applied the methodology of the complex network-based time series analysis to experimental temperature time series from a vertical turbulent heated jet. More specifically, we approach the hydrodynamic problem of discriminating time series corresponding to various regions relative to the jet axis, i.e., time series corresponding to regions that are close to the jet axis from time series originating at regions with a different dynamical regime based on the constructed network properties. Applying the transformation phase space method (k nearest neighbors) and also the visibility algorithm, we transformed time series into networks and evaluated the topological properties of the networks such as degree distribution, average path length, diameter, modularity, and clustering coefficient. The results show that the complex network approach allows distinguishing, identifying, and exploring in detail various dynamical regions of the jet flow, and associate it to the corresponding physical behavior. In addition, in order to reject the hypothesis that the studied networks originate from a stochastic process, we generated random network and we compared their statistical properties with that originating from the experimental data. As far as the efficiency of the two methods for network construction is concerned, we conclude that both methodologies lead to network properties that present almost the same qualitative behavior and allow us to reveal the underlying system dynamics

  16. Trend analysis using non-stationary time series clustering based on the finite element method

    Science.gov (United States)

    Gorji Sefidmazgi, M.; Sayemuzzaman, M.; Homaifar, A.; Jha, M. K.; Liess, S.

    2014-05-01

    In order to analyze low-frequency variability of climate, it is useful to model the climatic time series with multiple linear trends and locate the times of significant changes. In this paper, we have used non-stationary time series clustering to find change points in the trends. Clustering in a multi-dimensional non-stationary time series is challenging, since the problem is mathematically ill-posed. Clustering based on the finite element method (FEM) is one of the methods that can analyze multidimensional time series. One important attribute of this method is that it is not dependent on any statistical assumption and does not need local stationarity in the time series. In this paper, it is shown how the FEM-clustering method can be used to locate change points in the trend of temperature time series from in situ observations. This method is applied to the temperature time series of North Carolina (NC) and the results represent region-specific climate variability despite higher frequency harmonics in climatic time series. Next, we investigated the relationship between the climatic indices with the clusters/trends detected based on this clustering method. It appears that the natural variability of climate change in NC during 1950-2009 can be explained mostly by AMO and solar activity.

  17. Frontiers in Time Series and Financial Econometrics : An overview

    NARCIS (Netherlands)

    S. Ling (Shiqing); M.J. McAleer (Michael); H. Tong (Howell)

    2015-01-01

    markdownabstract__Abstract__ Two of the fastest growing frontiers in econometrics and quantitative finance are time series and financial econometrics. Significant theoretical contributions to financial econometrics have been made by experts in statistics, econometrics, mathematics, and time

  18. Frontiers in Time Series and Financial Econometrics: An Overview

    NARCIS (Netherlands)

    S. Ling (Shiqing); M.J. McAleer (Michael); H. Tong (Howell)

    2015-01-01

    markdownabstract__Abstract__ Two of the fastest growing frontiers in econometrics and quantitative finance are time series and financial econometrics. Significant theoretical contributions to financial econometrics have been made by experts in statistics, econometrics, mathematics, and time

  19. A four-stage hybrid model for hydrological time series forecasting.

    Science.gov (United States)

    Di, Chongli; Yang, Xiaohua; Wang, Xiaochao

    2014-01-01

    Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of 'denoising, decomposition and ensemble'. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models.

  20. A Four-Stage Hybrid Model for Hydrological Time Series Forecasting

    Science.gov (United States)

    Di, Chongli; Yang, Xiaohua; Wang, Xiaochao

    2014-01-01

    Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of ‘denoising, decomposition and ensemble’. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models. PMID:25111782

  1. A Gaussian Process Based Online Change Detection Algorithm for Monitoring Periodic Time Series

    Energy Technology Data Exchange (ETDEWEB)

    Chandola, Varun [ORNL; Vatsavai, Raju [ORNL

    2011-01-01

    Online time series change detection is a critical component of many monitoring systems, such as space and air-borne remote sensing instruments, cardiac monitors, and network traffic profilers, which continuously analyze observations recorded by sensors. Data collected by such sensors typically has a periodic (seasonal) component. Most existing time series change detection methods are not directly applicable to handle such data, either because they are not designed to handle periodic time series or because they cannot operate in an online mode. We propose an online change detection algorithm which can handle periodic time series. The algorithm uses a Gaussian process based non-parametric time series prediction model and monitors the difference between the predictions and actual observations within a statistically principled control chart framework to identify changes. A key challenge in using Gaussian process in an online mode is the need to solve a large system of equations involving the associated covariance matrix which grows with every time step. The proposed algorithm exploits the special structure of the covariance matrix and can analyze a time series of length T in O(T^2) time while maintaining a O(T) memory footprint, compared to O(T^4) time and O(T^2) memory requirement of standard matrix manipulation methods. We experimentally demonstrate the superiority of the proposed algorithm over several existing time series change detection algorithms on a set of synthetic and real time series. Finally, we illustrate the effectiveness of the proposed algorithm for identifying land use land cover changes using Normalized Difference Vegetation Index (NDVI) data collected for an agricultural region in Iowa state, USA. Our algorithm is able to detect different types of changes in a NDVI validation data set (with ~80% accuracy) which occur due to crop type changes as well as disruptive changes (e.g., natural disasters).

  2. Time-variant power spectral analysis of heart-rate time series by ...

    Indian Academy of Sciences (India)

    Frequency domain representation of a short-term heart-rate time series (HRTS) signal is a popular method for evaluating the cardiovascular control system. The spectral parameters, viz. percentage power in low frequency band (%PLF), percentage power in high frequency band (%PHF), power ratio of low frequency to high ...

  3. Rotation in the Dynamic Factor Modeling of Multivariate Stationary Time Series.

    Science.gov (United States)

    Molenaar, Peter C. M.; Nesselroade, John R.

    2001-01-01

    Proposes a special rotation procedure for the exploratory dynamic factor model for stationary multivariate time series. The rotation procedure applies separately to each univariate component series of a q-variate latent factor series and transforms such a component, initially represented as white noise, into a univariate moving-average.…

  4. Estimating the level of dynamical noise in time series by using fractal dimensions

    Energy Technology Data Exchange (ETDEWEB)

    Sase, Takumi, E-mail: sase@sat.t.u-tokyo.ac.jp [Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 153-8505 (Japan); Ramírez, Jonatán Peña [CONACYT Research Fellow, Center for Scientific Research and Higher Education at Ensenada (CICESE), Carretera Ensenada-Tijuana No. 3918, Zona Playitas, C.P. 22860, Ensenada, Baja California (Mexico); Kitajo, Keiichi [BSI-Toyota Collaboration Center, RIKEN Brain Science Institute, Wako, Saitama 351-0198 (Japan); Aihara, Kazuyuki; Hirata, Yoshito [Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 153-8505 (Japan); Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505 (Japan)

    2016-03-11

    We present a method for estimating the dynamical noise level of a ‘short’ time series even if the dynamical system is unknown. The proposed method estimates the level of dynamical noise by calculating the fractal dimensions of the time series. Additionally, the method is applied to EEG data to demonstrate its possible effectiveness as an indicator of temporal changes in the level of dynamical noise. - Highlights: • A dynamical noise level estimator for time series is proposed. • The estimator does not need any information about the dynamics generating the time series. • The estimator is based on a novel definition of time series dimension (TSD). • It is demonstrated that there exists a monotonic relationship between the • TSD and the level of dynamical noise. • We apply the proposed method to human electroencephalographic data.

  5. Estimating the level of dynamical noise in time series by using fractal dimensions

    International Nuclear Information System (INIS)

    Sase, Takumi; Ramírez, Jonatán Peña; Kitajo, Keiichi; Aihara, Kazuyuki; Hirata, Yoshito

    2016-01-01

    We present a method for estimating the dynamical noise level of a ‘short’ time series even if the dynamical system is unknown. The proposed method estimates the level of dynamical noise by calculating the fractal dimensions of the time series. Additionally, the method is applied to EEG data to demonstrate its possible effectiveness as an indicator of temporal changes in the level of dynamical noise. - Highlights: • A dynamical noise level estimator for time series is proposed. • The estimator does not need any information about the dynamics generating the time series. • The estimator is based on a novel definition of time series dimension (TSD). • It is demonstrated that there exists a monotonic relationship between the • TSD and the level of dynamical noise. • We apply the proposed method to human electroencephalographic data.

  6. Updating Landsat time series of surface-reflectance composites and forest change products with new observations

    Science.gov (United States)

    Hermosilla, Txomin; Wulder, Michael A.; White, Joanne C.; Coops, Nicholas C.; Hobart, Geordie W.

    2017-12-01

    The use of time series satellite data allows for the temporally dense, systematic, transparent, and synoptic capture of land dynamics over time. Subsequent to the opening of the Landsat archive, several time series approaches for characterizing landscape change have been developed, often representing a particular analytical time window. The information richness and widespread utility of these time series data have created a need to maintain the currency of time series information via the addition of new data, as it becomes available. When an existing time series is temporally extended, it is critical that previously generated change information remains consistent, thereby not altering reported change statistics or science outcomes based on that change information. In this research, we investigate the impacts and implications of adding additional years to an existing 29-year annual Landsat time series for forest change. To do so, we undertook a spatially explicit comparison of the 29 overlapping years of a time series representing 1984-2012, with a time series representing 1984-2016. Surface reflectance values, and presence, year, and type of change were compared. We found that the addition of years to extend the time series had minimal effect on the annual surface reflectance composites, with slight band-specific differences (r ≥ 0.1) in the final years of the original time series being updated. The area of stand replacing disturbances and determination of change year are virtually unchanged for the overlapping period between the two time-series products. Over the overlapping temporal period (1984-2012), the total area of change differs by 0.53%, equating to an annual difference in change area of 0.019%. Overall, the spatial and temporal agreement of the changes detected by both time series was 96%. Further, our findings suggest that the entire pre-existing historic time series does not need to be re-processed during the update process. Critically, given the time

  7. A New Modified Histogram Matching Normalization for Time Series Microarray Analysis.

    Science.gov (United States)

    Astola, Laura; Molenaar, Jaap

    2014-07-01

    Microarray data is often utilized in inferring regulatory networks. Quantile normalization (QN) is a popular method to reduce array-to-array variation. We show that in the context of time series measurements QN may not be the best choice for this task, especially not if the inference is based on continuous time ODE model. We propose an alternative normalization method that is better suited for network inference from time series data.

  8. Quantifying evolutionary dynamics from variant-frequency time series

    Science.gov (United States)

    Khatri, Bhavin S.

    2016-09-01

    From Kimura’s neutral theory of protein evolution to Hubbell’s neutral theory of biodiversity, quantifying the relative importance of neutrality versus selection has long been a basic question in evolutionary biology and ecology. With deep sequencing technologies, this question is taking on a new form: given a time-series of the frequency of different variants in a population, what is the likelihood that the observation has arisen due to selection or neutrality? To tackle the 2-variant case, we exploit Fisher’s angular transformation, which despite being discovered by Ronald Fisher a century ago, has remained an intellectual curiosity. We show together with a heuristic approach it provides a simple solution for the transition probability density at short times, including drift, selection and mutation. Our results show under that under strong selection and sufficiently frequent sampling these evolutionary parameters can be accurately determined from simulation data and so they provide a theoretical basis for techniques to detect selection from variant or polymorphism frequency time-series.

  9. RankExplorer: Visualization of Ranking Changes in Large Time Series Data.

    Science.gov (United States)

    Shi, Conglei; Cui, Weiwei; Liu, Shixia; Xu, Panpan; Chen, Wei; Qu, Huamin

    2012-12-01

    For many applications involving time series data, people are often interested in the changes of item values over time as well as their ranking changes. For example, people search many words via search engines like Google and Bing every day. Analysts are interested in both the absolute searching number for each word as well as their relative rankings. Both sets of statistics may change over time. For very large time series data with thousands of items, how to visually present ranking changes is an interesting challenge. In this paper, we propose RankExplorer, a novel visualization method based on ThemeRiver to reveal the ranking changes. Our method consists of four major components: 1) a segmentation method which partitions a large set of time series curves into a manageable number of ranking categories; 2) an extended ThemeRiver view with embedded color bars and changing glyphs to show the evolution of aggregation values related to each ranking category over time as well as the content changes in each ranking category; 3) a trend curve to show the degree of ranking changes over time; 4) rich user interactions to support interactive exploration of ranking changes. We have applied our method to some real time series data and the case studies demonstrate that our method can reveal the underlying patterns related to ranking changes which might otherwise be obscured in traditional visualizations.

  10. ESTIMATING RELIABILITY OF DISTURBANCES IN SATELLITE TIME SERIES DATA BASED ON STATISTICAL ANALYSIS

    Directory of Open Access Journals (Sweden)

    Z.-G. Zhou

    2016-06-01

    Full Text Available Normally, the status of land cover is inherently dynamic and changing continuously on temporal scale. However, disturbances or abnormal changes of land cover — caused by such as forest fire, flood, deforestation, and plant diseases — occur worldwide at unknown times and locations. Timely detection and characterization of these disturbances is of importance for land cover monitoring. Recently, many time-series-analysis methods have been developed for near real-time or online disturbance detection, using satellite image time series. However, the detection results were only labelled with “Change/ No change” by most of the present methods, while few methods focus on estimating reliability (or confidence level of the detected disturbances in image time series. To this end, this paper propose a statistical analysis method for estimating reliability of disturbances in new available remote sensing image time series, through analysis of full temporal information laid in time series data. The method consists of three main steps. (1 Segmenting and modelling of historical time series data based on Breaks for Additive Seasonal and Trend (BFAST. (2 Forecasting and detecting disturbances in new time series data. (3 Estimating reliability of each detected disturbance using statistical analysis based on Confidence Interval (CI and Confidence Levels (CL. The method was validated by estimating reliability of disturbance regions caused by a recent severe flooding occurred around the border of Russia and China. Results demonstrated that the method can estimate reliability of disturbances detected in satellite image with estimation error less than 5% and overall accuracy up to 90%.

  11. Wavelet entropy of BOLD time series: An application to Rolandic epilepsy.

    Science.gov (United States)

    Gupta, Lalit; Jansen, Jacobus F A; Hofman, Paul A M; Besseling, René M H; de Louw, Anton J A; Aldenkamp, Albert P; Backes, Walter H

    2017-12-01

    To assess the wavelet entropy for the characterization of intrinsic aberrant temporal irregularities in the time series of resting-state blood-oxygen-level-dependent (BOLD) signal fluctuations. Further, to evaluate the temporal irregularities (disorder/order) on a voxel-by-voxel basis in the brains of children with Rolandic epilepsy. The BOLD time series was decomposed using the discrete wavelet transform and the wavelet entropy was calculated. Using a model time series consisting of multiple harmonics and nonstationary components, the wavelet entropy was compared with Shannon and spectral (Fourier-based) entropy. As an application, the wavelet entropy in 22 children with Rolandic epilepsy was compared to 22 age-matched healthy controls. The images were obtained by performing resting-state functional magnetic resonance imaging (fMRI) using a 3T system, an 8-element receive-only head coil, and an echo planar imaging pulse sequence ( T2*-weighted). The wavelet entropy was also compared to spectral entropy, regional homogeneity, and Shannon entropy. Wavelet entropy was found to identify the nonstationary components of the model time series. In Rolandic epilepsy patients, a significantly elevated wavelet entropy was observed relative to controls for the whole cerebrum (P = 0.03). Spectral entropy (P = 0.41), regional homogeneity (P = 0.52), and Shannon entropy (P = 0.32) did not reveal significant differences. The wavelet entropy measure appeared more sensitive to detect abnormalities in cerebral fluctuations represented by nonstationary effects in the BOLD time series than more conventional measures. This effect was observed in the model time series as well as in Rolandic epilepsy. These observations suggest that the brains of children with Rolandic epilepsy exhibit stronger nonstationary temporal signal fluctuations than controls. 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017;46:1728-1737. © 2017 International Society for Magnetic

  12. Time Series Forecasting with Missing Values

    OpenAIRE

    Shin-Fu Wu; Chia-Yung Chang; Shie-Jue Lee

    2015-01-01

    Time series prediction has become more popular in various kinds of applications such as weather prediction, control engineering, financial analysis, industrial monitoring, etc. To deal with real-world problems, we are often faced with missing values in the data due to sensor malfunctions or human errors. Traditionally, the missing values are simply omitted or replaced by means of imputation methods. However, omitting those missing values may cause temporal discontinuity. Imputation methods, o...

  13. Detection of chaotic determinism in time series from randomly forced maps

    Science.gov (United States)

    Chon, K. H.; Kanters, J. K.; Cohen, R. J.; Holstein-Rathlou, N. H.

    1997-01-01

    Time series from biological system often display fluctuations in the measured variables. Much effort has been directed at determining whether this variability reflects deterministic chaos, or whether it is merely "noise". Despite this effort, it has been difficult to establish the presence of chaos in time series from biological sytems. The output from a biological system is probably the result of both its internal dynamics, and the input to the system from the surroundings. This implies that the system should be viewed as a mixed system with both stochastic and deterministic components. We present a method that appears to be useful in deciding whether determinism is present in a time series, and if this determinism has chaotic attributes, i.e., a positive characteristic exponent that leads to sensitivity to initial conditions. The method relies on fitting a nonlinear autoregressive model to the time series followed by an estimation of the characteristic exponents of the model over the observed probability distribution of states for the system. The method is tested by computer simulations, and applied to heart rate variability data.

  14. Time-series modeling: applications to long-term finfish monitoring data

    International Nuclear Information System (INIS)

    Bireley, L.E.

    1985-01-01

    The growing concern and awareness that developed during the 1970's over the effects that industry had on the environment caused the electric utility industry in particular to develop monitoring programs. These programs generate long-term series of data that are not very amenable to classical normal-theory statistical analysis. The monitoring data collected from three finfish programs (impingement, trawl and seine) at the Millstone Nuclear Power Station were typical of such series and thus were used to develop methodology that used the full extent of the information in the series. The basis of the methodology was classic Box-Jenkins time-series modeling; however, the models also included deterministic components that involved flow, season and time as predictor variables. Time entered into the models as harmonic regression terms. Of the 32 models fitted to finfish catch data, 19 were found to account for more than 70% of the historical variation. The models were than used to forecast finfish catches a year in advance and comparisons were made to actual data. Usually the confidence intervals associated with the forecasts encompassed most of the observed data. The technique can provide the basis for intervention analysis in future impact assessments

  15. LIDAR TS for ITER core plasma. Part I: layout & hardware

    Science.gov (United States)

    Salzmann, H.; Gowers, C.; Nielsen, P.

    2017-12-01

    The original time-of-flight design of the Thomson scattering diagnostic for the ITER core plasma has been shown up by ITER. This decision was justified by insufficiencies of some of the components. In this paper we show that with available, present day technology a LIDAR TS system is feasible which meets all the ITER specifications. As opposed to the conventional TS system the LIDAR TS also measures the high field side of the plasma. The optical layout of the front end has been changed only little in comparison with the latest one considered by ITER. The main change is that it offers an optical collection without any vignetting over the low field side. The throughput of the system is defined only by the size and the angle of acceptance of the detectors. This, in combination with the fact that the LIDAR system uses only one set of spectral channels for the whole line of sight, means that no absolute calibration using Raman or Rayleigh scattering from a non-hydrogen isotope gas fill of the vessel is needed. Alignment of the system is easy since the collection optics view the footprint of the laser on the inner wall. In the described design we use, simultaneously, two different wavelength pulses from a Nd:YAG laser system. Its fundamental wavelength ensures measurements of 2 keV up to more than 40 keV, whereas the injection of the second harmonic enables measurements of low temperatures. As it is the purpose of this paper to show the technological feasibility of the LIDAR system, the hardware is considered in Part I of the paper. In Part II we demonstrate by numerical simulations that the accuracy of the measurements as required by ITER is maintained throughout the given plasma parameter range. The effect of enhanced background radiation in the wavelength range 400 nm-500 nm is considered. In Part III the recovery of calibration in case of changing spectral transmission of the front end is treated. We also investigate how to improve the spatial resolution at the

  16. Enhanced catalytic oxidation by hierarchically structured TS-1 zeolite

    NARCIS (Netherlands)

    Xin, H.; Zhao, Jiao; Zhao, J.; Xu, S.; Li, Junping; Zhang, Weiping; Guo, X.; Hensen, E.J.M.; Yang, Q.; Li, Can

    2010-01-01

    A TS-1 zeolite with a disordered network of mesopores penetrating the microporous crystalline zeolite framework was successfully synthesized by a one-pot carbon hard-templating synthesis approach. Besides conventional methods to characterize the mesoporosity, the use of variable-temperature 129Xe

  17. Time Series Imputation via L1 Norm-Based Singular Spectrum Analysis

    Science.gov (United States)

    Kalantari, Mahdi; Yarmohammadi, Masoud; Hassani, Hossein; Silva, Emmanuel Sirimal

    Missing values in time series data is a well-known and important problem which many researchers have studied extensively in various fields. In this paper, a new nonparametric approach for missing value imputation in time series is proposed. The main novelty of this research is applying the L1 norm-based version of Singular Spectrum Analysis (SSA), namely L1-SSA which is robust against outliers. The performance of the new imputation method has been compared with many other established methods. The comparison is done by applying them to various real and simulated time series. The obtained results confirm that the SSA-based methods, especially L1-SSA can provide better imputation in comparison to other methods.

  18. Multiple Time Series Forecasting Using Quasi-Randomized Functional Link Neural Networks

    Directory of Open Access Journals (Sweden)

    Thierry Moudiki

    2018-03-01

    Full Text Available We are interested in obtaining forecasts for multiple time series, by taking into account the potential nonlinear relationships between their observations. For this purpose, we use a specific type of regression model on an augmented dataset of lagged time series. Our model is inspired by dynamic regression models (Pankratz 2012, with the response variable’s lags included as predictors, and is known as Random Vector Functional Link (RVFL neural networks. The RVFL neural networks have been successfully applied in the past, to solving regression and classification problems. The novelty of our approach is to apply an RVFL model to multivariate time series, under two separate regularization constraints on the regression parameters.

  19. A novel time series link prediction method: Learning automata approach

    Science.gov (United States)

    Moradabadi, Behnaz; Meybodi, Mohammad Reza

    2017-09-01

    Link prediction is a main social network challenge that uses the network structure to predict future links. The common link prediction approaches to predict hidden links use a static graph representation where a snapshot of the network is analyzed to find hidden or future links. For example, similarity metric based link predictions are a common traditional approach that calculates the similarity metric for each non-connected link and sort the links based on their similarity metrics and label the links with higher similarity scores as the future links. Because people activities in social networks are dynamic and uncertainty, and the structure of the networks changes over time, using deterministic graphs for modeling and analysis of the social network may not be appropriate. In the time-series link prediction problem, the time series link occurrences are used to predict the future links In this paper, we propose a new time series link prediction based on learning automata. In the proposed algorithm for each link that must be predicted there is one learning automaton and each learning automaton tries to predict the existence or non-existence of the corresponding link. To predict the link occurrence in time T, there is a chain consists of stages 1 through T - 1 and the learning automaton passes from these stages to learn the existence or non-existence of the corresponding link. Our preliminary link prediction experiments with co-authorship and email networks have provided satisfactory results when time series link occurrences are considered.

  20. Topological data analysis of financial time series: Landscapes of crashes

    Science.gov (United States)

    Gidea, Marian; Katz, Yuri

    2018-02-01

    We explore the evolution of daily returns of four major US stock market indices during the technology crash of 2000, and the financial crisis of 2007-2009. Our methodology is based on topological data analysis (TDA). We use persistence homology to detect and quantify topological patterns that appear in multidimensional time series. Using a sliding window, we extract time-dependent point cloud data sets, to which we associate a topological space. We detect transient loops that appear in this space, and we measure their persistence. This is encoded in real-valued functions referred to as a 'persistence landscapes'. We quantify the temporal changes in persistence landscapes via their Lp-norms. We test this procedure on multidimensional time series generated by various non-linear and non-equilibrium models. We find that, in the vicinity of financial meltdowns, the Lp-norms exhibit strong growth prior to the primary peak, which ascends during a crash. Remarkably, the average spectral density at low frequencies of the time series of Lp-norms of the persistence landscapes demonstrates a strong rising trend for 250 trading days prior to either dotcom crash on 03/10/2000, or to the Lehman bankruptcy on 09/15/2008. Our study suggests that TDA provides a new type of econometric analysis, which complements the standard statistical measures. The method can be used to detect early warning signals of imminent market crashes. We believe that this approach can be used beyond the analysis of financial time series presented here.

  1. A Solution-Based Temperature Sensor Using the Organic Compound CuTsPc

    Directory of Open Access Journals (Sweden)

    Shahino Mah Abdullah

    2014-06-01

    Full Text Available An electrochemical cell using an organic compound, copper (II phthalocyanine-tetrasulfonic acid tetrasodium salt (CuTsPc, has been fabricated and investigated as a solution-based temperature sensor. The capacitance and resistance of the ITO/CuTsPc solution/ITO chemical cell has been characterized as a function of temperature in the temperature range of 25–80 °C. A linear response with minimal hysteresis is observed. The fabricated temperature sensor has shown high consistency and sensitive response towards a specific range of temperature values.

  2. ENVIRONMENTAL TECHNOLOGY VERIFICATION: JOINT (NSF-EPA) VERIFICATION STATEMENT AND REPORT: TRITON SYSTEMS, LLC SOLID BOWL CENTRIFUGE, MODEL TS-5000

    Science.gov (United States)

    Verification testing of the Triton Systems, LLC Solid Bowl Centrifuge Model TS-5000 (TS-5000) was conducted at the Lake Wheeler Road Field Laboratory Swine Educational Unit in Raleigh, North Carolina. The TS-5000 was 48" in diameter and 30" deep, with a bowl capacity of 16 ft3. ...

  3. Properties of Asymmetric Detrended Fluctuation Analysis in the time series of RR intervals

    Science.gov (United States)

    Piskorski, J.; Kosmider, M.; Mieszkowski, D.; Krauze, T.; Wykretowicz, A.; Guzik, P.

    2018-02-01

    Heart rate asymmetry is a phenomenon by which the accelerations and decelerations of heart rate behave differently, and this difference is consistent and unidirectional, i.e. in most of the analyzed recordings the inequalities have the same directions. So far, it has been established for variance and runs based types of descriptors of RR intervals time series. In this paper we apply the newly developed method of Asymmetric Detrended Fluctuation Analysis, which so far has mainly been used with economic time series, to the set of 420 stationary 30 min time series of RR intervals from young, healthy individuals aged between 20 and 40. This asymmetric approach introduces separate scaling exponents for rising and falling trends. We systematically study the presence of asymmetry in both global and local versions of this method. In this study global means "applying to the whole time series" and local means "applying to windows jumping along the recording". It is found that the correlation structure of the fluctuations left over after detrending in physiological time series shows strong asymmetric features in both magnitude, with α+ physiological data after shuffling or with a group of symmetric synthetic time series.

  4. A New Modified Histogram Matching Normalization for Time Series Microarray Analysis

    Directory of Open Access Journals (Sweden)

    Laura Astola

    2014-07-01

    Full Text Available Microarray data is often utilized in inferring regulatory networks. Quantile normalization (QN is a popular method to reduce array-to-array variation. We show that in the context of time series measurements QN may not be the best choice for this task, especially not if the inference is based on continuous time ODE model. We propose an alternative normalization method that is better suited for network inference from time series data.

  5. AFSC/ABL: Ugashik sockeye salmon scale time series

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — A time series of scale samples (1956 b?? 2002) collected from adult sockeye salmon returning to Ugashik River were retrieved from the Alaska Department of Fish and...

  6. AFSC/ABL: Naknek sockeye salmon scale time series

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — A time series of scale samples (1956 2002) collected from adult sockeye salmon returning to Naknek River were retrieved from the Alaska Department of Fish and Game....

  7. Time series regression model for infectious disease and weather.

    Science.gov (United States)

    Imai, Chisato; Armstrong, Ben; Chalabi, Zaid; Mangtani, Punam; Hashizume, Masahiro

    2015-10-01

    Time series regression has been developed and long used to evaluate the short-term associations of air pollution and weather with mortality or morbidity of non-infectious diseases. The application of the regression approaches from this tradition to infectious diseases, however, is less well explored and raises some new issues. We discuss and present potential solutions for five issues often arising in such analyses: changes in immune population, strong autocorrelations, a wide range of plausible lag structures and association patterns, seasonality adjustments, and large overdispersion. The potential approaches are illustrated with datasets of cholera cases and rainfall from Bangladesh and influenza and temperature in Tokyo. Though this article focuses on the application of the traditional time series regression to infectious diseases and weather factors, we also briefly introduce alternative approaches, including mathematical modeling, wavelet analysis, and autoregressive integrated moving average (ARIMA) models. Modifications proposed to standard time series regression practice include using sums of past cases as proxies for the immune population, and using the logarithm of lagged disease counts to control autocorrelation due to true contagion, both of which are motivated from "susceptible-infectious-recovered" (SIR) models. The complexity of lag structures and association patterns can often be informed by biological mechanisms and explored by using distributed lag non-linear models. For overdispersed models, alternative distribution models such as quasi-Poisson and negative binomial should be considered. Time series regression can be used to investigate dependence of infectious diseases on weather, but may need modifying to allow for features specific to this context. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

  8. Assessing Coupling Dynamics from an Ensemble of Time Series

    Directory of Open Access Journals (Sweden)

    Germán Gómez-Herrero

    2015-04-01

    Full Text Available Finding interdependency relations between time series provides valuable knowledge about the processes that generated the signals. Information theory sets a natural framework for important classes of statistical dependencies. However, a reliable estimation from information-theoretic functionals is hampered when the dependency to be assessed is brief or evolves in time. Here, we show that these limitations can be partly alleviated when we have access to an ensemble of independent repetitions of the time series. In particular, we gear a data-efficient estimator of probability densities to make use of the full structure of trial-based measures. By doing so, we can obtain time-resolved estimates for a family of entropy combinations (including mutual information, transfer entropy and their conditional counterparts, which are more accurate than the simple average of individual estimates over trials. We show with simulated and real data generated by coupled electronic circuits that the proposed approach allows one to recover the time-resolved dynamics of the coupling between different subsystems.

  9. Wavelet transform approach for fitting financial time series data

    Science.gov (United States)

    Ahmed, Amel Abdoullah; Ismail, Mohd Tahir

    2015-10-01

    This study investigates a newly developed technique; a combined wavelet filtering and VEC model, to study the dynamic relationship among financial time series. Wavelet filter has been used to annihilate noise data in daily data set of NASDAQ stock market of US, and three stock markets of Middle East and North Africa (MENA) region, namely, Egypt, Jordan, and Istanbul. The data covered is from 6/29/2001 to 5/5/2009. After that, the returns of generated series by wavelet filter and original series are analyzed by cointegration test and VEC model. The results show that the cointegration test affirms the existence of cointegration between the studied series, and there is a long-term relationship between the US, stock markets and MENA stock markets. A comparison between the proposed model and traditional model demonstrates that, the proposed model (DWT with VEC model) outperforms traditional model (VEC model) to fit the financial stock markets series well, and shows real information about these relationships among the stock markets.

  10. Evolution of the Sunspot Number and Solar Wind B Time Series

    Science.gov (United States)

    Cliver, Edward W.; Herbst, Konstantin

    2018-03-01

    The past two decades have witnessed significant changes in our knowledge of long-term solar and solar wind activity. The sunspot number time series (1700-present) developed by Rudolf Wolf during the second half of the 19th century was revised and extended by the group sunspot number series (1610-1995) of Hoyt and Schatten during the 1990s. The group sunspot number is significantly lower than the Wolf series before ˜1885. An effort from 2011-2015 to understand and remove differences between these two series via a series of workshops had the unintended consequence of prompting several alternative constructions of the sunspot number. Thus it has been necessary to expand and extend the sunspot number reconciliation process. On the solar wind side, after a decade of controversy, an ISSI International Team used geomagnetic and sunspot data to obtain a high-confidence time series of the solar wind magnetic field strength (B) from 1750-present that can be compared with two independent long-term (> ˜600 year) series of annual B-values based on cosmogenic nuclides. In this paper, we trace the twists and turns leading to our current understanding of long-term solar and solar wind activity.

  11. Time irreversibility and intrinsics revealing of series with complex network approach

    Science.gov (United States)

    Xiong, Hui; Shang, Pengjian; Xia, Jianan; Wang, Jing

    2018-06-01

    In this work, we analyze time series on the basis of the visibility graph algorithm that maps the original series into a graph. By taking into account the all-round information carried by the signals, the time irreversibility and fractal behavior of series are evaluated from a complex network perspective, and considered signals are further classified from different aspects. The reliability of the proposed analysis is supported by numerical simulations on synthesized uncorrelated random noise, short-term correlated chaotic systems and long-term correlated fractal processes, and by the empirical analysis on daily closing prices of eleven worldwide stock indices. Obtained results suggest that finite size has a significant effect on the evaluation, and that there might be no direct relation between the time irreversibility and long-range correlation of series. Similarity and dissimilarity between stock indices are also indicated from respective regional and global perspectives, showing the existence of multiple features of underlying systems.

  12. Recurrence and symmetry of time series: Application to transition detection

    International Nuclear Information System (INIS)

    Girault, Jean-Marc

    2015-01-01

    Highlights: •A new theoretical framework based on the symmetry concept is proposed. •Four types of symmetry present in any time series were analyzed. •New descriptors make possible the analysis of regime changes in logistic systems. •Chaos–chaos, chaos–periodic, symmetry-breaking, symmetry-increasing bifurcations can be detected. -- Abstract: The study of transitions in low dimensional, nonlinear dynamical systems is a complex problem for which there is not yet a simple, global numerical method able to detect chaos–chaos, chaos–periodic bifurcations and symmetry-breaking, symmetry-increasing bifurcations. We present here for the first time a general framework focusing on the symmetry concept of time series that at the same time reveals new kinds of recurrence. We propose several numerical tools based on the symmetry concept allowing both the qualification and quantification of different kinds of possible symmetry. By using several examples based on periodic symmetrical time series and on logistic and cubic maps, we show that it is possible with simple numerical tools to detect a large number of bifurcations of chaos–chaos, chaos–periodic, broken symmetry and increased symmetry types

  13. Bootstrap Power of Time Series Goodness of fit tests

    Directory of Open Access Journals (Sweden)

    Sohail Chand

    2013-10-01

    Full Text Available In this article, we looked at power of various versions of Box and Pierce statistic and Cramer von Mises test. An extensive simulation study has been conducted to compare the power of these tests. Algorithms have been provided for the power calculations and comparison has also been made between the semi parametric bootstrap methods used for time series. Results show that Box-Pierce statistic and its various versions have good power against linear time series models but poor power against non linear models while situation reverses for Cramer von Mises test. Moreover, we found that dynamic bootstrap method is better than xed design bootstrap method.

  14. Identification of non-indigenous phytoplankton species dominated bloom off Goa using inverted microscopy and pigment (HPLC) analysis

    Digital Repository Service at National Institute of Oceanography (India)

    Bhaskar, P.V.; Roy, R.; Gauns, M.; Shenoy, D.M.; Rao, V.D.; Mochemadkar, S.

    site and sampling The mixed phytoplankton bloom was observed during one of the monthly sampling at the Can- dolim time series (CaTS) transect (figure 1) in the near-shore waters off Goa, west coast of India. Sea- water was sampled on two days (27 and 29... January, Figure 1. Map showing the CaTS (Candolim time-series) stations G1 to G5 and one station off Morjim north of CaTS. The approximate spread of the bloom is indicated by the shaded portion. Identification of non-indigenous phytoplankton off Goa 1147...

  15. Advances in Antithetic Time Series Analysis : Separating Fact from Artifact

    Directory of Open Access Journals (Sweden)

    Dennis Ridley

    2016-01-01

    Full Text Available The problem of biased time series mathematical model parameter estimates is well known to be insurmountable. When used to predict future values by extrapolation, even a de minimis bias will eventually grow into a large bias, with misleading results. This paper elucidates how combining antithetic time series' solves this baffling problem of bias in the fitted and forecast values by dynamic bias cancellation. Instead of growing to infinity, the average error can converge to a constant. (original abstract

  16. Recursive Bayesian recurrent neural networks for time-series modeling.

    Science.gov (United States)

    Mirikitani, Derrick T; Nikolaev, Nikolay

    2010-02-01

    This paper develops a probabilistic approach to recursive second-order training of recurrent neural networks (RNNs) for improved time-series modeling. A general recursive Bayesian Levenberg-Marquardt algorithm is derived to sequentially update the weights and the covariance (Hessian) matrix. The main strengths of the approach are a principled handling of the regularization hyperparameters that leads to better generalization, and stable numerical performance. The framework involves the adaptation of a noise hyperparameter and local weight prior hyperparameters, which represent the noise in the data and the uncertainties in the model parameters. Experimental investigations using artificial and real-world data sets show that RNNs equipped with the proposed approach outperform standard real-time recurrent learning and extended Kalman training algorithms for recurrent networks, as well as other contemporary nonlinear neural models, on time-series modeling.

  17. A neuro-fuzzy computing technique for modeling hydrological time series

    Science.gov (United States)

    Nayak, P. C.; Sudheer, K. P.; Rangan, D. M.; Ramasastri, K. S.

    2004-05-01

    Intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are proven to be efficient when applied individually to a variety of problems. Recently there has been a growing interest in combining both these approaches, and as a result, neuro-fuzzy computing techniques have evolved. This approach has been tested and evaluated in the field of signal processing and related areas, but researchers have only begun evaluating the potential of this neuro-fuzzy hybrid approach in hydrologic modeling studies. This paper presents the application of an adaptive neuro fuzzy inference system (ANFIS) to hydrologic time series modeling, and is illustrated by an application to model the river flow of Baitarani River in Orissa state, India. An introduction to the ANFIS modeling approach is also presented. The advantage of the method is that it does not require the model structure to be known a priori, in contrast to most of the time series modeling techniques. The results showed that the ANFIS forecasted flow series preserves the statistical properties of the original flow series. The model showed good performance in terms of various statistical indices. The results are highly promising, and a comparative analysis suggests that the proposed modeling approach outperforms ANNs and other traditional time series models in terms of computational speed, forecast errors, efficiency, peak flow estimation etc. It was observed that the ANFIS model preserves the potential of the ANN approach fully, and eases the model building process.

  18. Academic Workload and Working Time: Retrospective Perceptions versus Time-Series Data

    Science.gov (United States)

    Kyvik, Svein

    2013-01-01

    The purpose of this article is to examine the validity of perceptions by academic staff about their past and present workload and working hours. Retrospective assessments are compared with time-series data. The data are drawn from four mail surveys among academic staff in Norwegian universities undertaken in the period 1982-2008. The findings show…

  19. Interrupted Time Series Versus Statistical Process Control in Quality Improvement Projects.

    Science.gov (United States)

    Andersson Hagiwara, Magnus; Andersson Gäre, Boel; Elg, Mattias

    2016-01-01

    To measure the effect of quality improvement interventions, it is appropriate to use analysis methods that measure data over time. Examples of such methods include statistical process control analysis and interrupted time series with segmented regression analysis. This article compares the use of statistical process control analysis and interrupted time series with segmented regression analysis for evaluating the longitudinal effects of quality improvement interventions, using an example study on an evaluation of a computerized decision support system.

  20. Comparison of different Methods for Univariate Time Series Imputation in R

    OpenAIRE

    Moritz, Steffen; Sardá, Alexis; Bartz-Beielstein, Thomas; Zaefferer, Martin; Stork, Jörg

    2015-01-01

    Missing values in datasets are a well-known problem and there are quite a lot of R packages offering imputation functions. But while imputation in general is well covered within R, it is hard to find functions for imputation of univariate time series. The problem is, most standard imputation techniques can not be applied directly. Most algorithms rely on inter-attribute correlations, while univariate time series imputation needs to employ time dependencies. This paper provides an overview of ...

  1. PSO-MISMO modeling strategy for multistep-ahead time series prediction.

    Science.gov (United States)

    Bao, Yukun; Xiong, Tao; Hu, Zhongyi

    2014-05-01

    Multistep-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research. Recently, the multiple-input several multiple-outputs (MISMO) modeling strategy has been proposed as a promising alternative for multistep-ahead time series prediction, exhibiting advantages compared with the two currently dominating strategies, the iterated and the direct strategies. Built on the established MISMO strategy, this paper proposes a particle swarm optimization (PSO)-based MISMO modeling strategy, which is capable of determining the number of sub-models in a self-adaptive mode, with varying prediction horizons. Rather than deriving crisp divides with equal-size s prediction horizons from the established MISMO, the proposed PSO-MISMO strategy, implemented with neural networks, employs a heuristic to create flexible divides with varying sizes of prediction horizons and to generate corresponding sub-models, providing considerable flexibility in model construction, which has been validated with simulated and real datasets.

  2. New significance test methods for Fourier analysis of geophysical time series

    Directory of Open Access Journals (Sweden)

    Z. Zhang

    2011-09-01

    Full Text Available When one applies the discrete Fourier transform to analyze finite-length time series, discontinuities at the data boundaries will distort its Fourier power spectrum. In this paper, based on a rigid statistics framework, we present a new significance test method which can extract the intrinsic feature of a geophysical time series very well. We show the difference in significance level compared with traditional Fourier tests by analyzing the Arctic Oscillation (AO and the Nino3.4 time series. In the AO, we find significant peaks at about 2.8, 4.3, and 5.7 yr periods and in Nino3.4 at about 12 yr period in tests against red noise. These peaks are not significant in traditional tests.

  3. A Non-standard Empirical Likelihood for Time Series

    DEFF Research Database (Denmark)

    Nordman, Daniel J.; Bunzel, Helle; Lahiri, Soumendra N.

    Standard blockwise empirical likelihood (BEL) for stationary, weakly dependent time series requires specifying a fixed block length as a tuning parameter for setting confidence regions. This aspect can be difficult and impacts coverage accuracy. As an alternative, this paper proposes a new version...... of BEL based on a simple, though non-standard, data-blocking rule which uses a data block of every possible length. Consequently, the method involves no block selection and is also anticipated to exhibit better coverage performance. Its non-standard blocking scheme, however, induces non......-standard asymptotics and requires a significantly different development compared to standard BEL. We establish the large-sample distribution of log-ratio statistics from the new BEL method for calibrating confidence regions for mean or smooth function parameters of time series. This limit law is not the usual chi...

  4. Time series analysis of ozone data in Isfahan

    Science.gov (United States)

    Omidvari, M.; Hassanzadeh, S.; Hosseinibalam, F.

    2008-07-01

    Time series analysis used to investigate the stratospheric ozone formation and decomposition processes. Different time series methods are applied to detect the reason for extreme high ozone concentrations for each season. Data was convert into seasonal component and frequency domain, the latter has been evaluated by using the Fast Fourier Transform (FFT), spectral analysis. The power density spectrum estimated from the ozone data showed peaks at cycle duration of 22, 20, 36, 186, 365 and 40 days. According to seasonal component analysis most fluctuation was in 1999 and 2000, but the least fluctuation was in 2003. The best correlation between ozone and sun radiation was found in 2000. Other variables which are not available cause to this fluctuation in the 1999 and 2001. The trend of ozone is increasing in 1999 and is decreasing in other years.

  5. Long-memory time series theory and methods

    CERN Document Server

    Palma, Wilfredo

    2007-01-01

    Wilfredo Palma, PhD, is Chairman and Professor of Statistics in the Department of Statistics at Pontificia Universidad Católica de Chile. Dr. Palma has published several refereed articles and has received over a dozen academic honors and awards. His research interests include time series analysis, prediction theory, state space systems, linear models, and econometrics.

  6. Nonlinear Time Series Analysis via Neural Networks

    Science.gov (United States)

    Volná, Eva; Janošek, Michal; Kocian, Václav; Kotyrba, Martin

    This article deals with a time series analysis based on neural networks in order to make an effective forex market [Moore and Roche, J. Int. Econ. 58, 387-411 (2002)] pattern recognition. Our goal is to find and recognize important patterns which repeatedly appear in the market history to adapt our trading system behaviour based on them.

  7. On statistical inference in time series analysis of the evolution of road safety.

    Science.gov (United States)

    Commandeur, Jacques J F; Bijleveld, Frits D; Bergel-Hayat, Ruth; Antoniou, Constantinos; Yannis, George; Papadimitriou, Eleonora

    2013-11-01

    Data collected for building a road safety observatory usually include observations made sequentially through time. Examples of such data, called time series data, include annual (or monthly) number of road traffic accidents, traffic fatalities or vehicle kilometers driven in a country, as well as the corresponding values of safety performance indicators (e.g., data on speeding, seat belt use, alcohol use, etc.). Some commonly used statistical techniques imply assumptions that are often violated by the special properties of time series data, namely serial dependency among disturbances associated with the observations. The first objective of this paper is to demonstrate the impact of such violations to the applicability of standard methods of statistical inference, which leads to an under or overestimation of the standard error and consequently may produce erroneous inferences. Moreover, having established the adverse consequences of ignoring serial dependency issues, the paper aims to describe rigorous statistical techniques used to overcome them. In particular, appropriate time series analysis techniques of varying complexity are employed to describe the development over time, relating the accident-occurrences to explanatory factors such as exposure measures or safety performance indicators, and forecasting the development into the near future. Traditional regression models (whether they are linear, generalized linear or nonlinear) are shown not to naturally capture the inherent dependencies in time series data. Dedicated time series analysis techniques, such as the ARMA-type and DRAG approaches are discussed next, followed by structural time series models, which are a subclass of state space methods. The paper concludes with general recommendations and practice guidelines for the use of time series models in road safety research. Copyright © 2012 Elsevier Ltd. All rights reserved.

  8. Multiband Prediction Model for Financial Time Series with Multivariate Empirical Mode Decomposition

    Directory of Open Access Journals (Sweden)

    Md. Rabiul Islam

    2012-01-01

    Full Text Available This paper presents a subband approach to financial time series prediction. Multivariate empirical mode decomposition (MEMD is employed here for multiband representation of multichannel financial time series together. Autoregressive moving average (ARMA model is used in prediction of individual subband of any time series data. Then all the predicted subband signals are summed up to obtain the overall prediction. The ARMA model works better for stationary signal. With multiband representation, each subband becomes a band-limited (narrow band signal and hence better prediction is achieved. The performance of the proposed MEMD-ARMA model is compared with classical EMD, discrete wavelet transform (DWT, and with full band ARMA model in terms of signal-to-noise ratio (SNR and mean square error (MSE between the original and predicted time series. The simulation results show that the MEMD-ARMA-based method performs better than the other methods.

  9. AHP 40: NAMUYI TIBETAN pha⁵⁴ tsə⁵⁴ RITUALS AND ORAL CHANTS

    Directory of Open Access Journals (Sweden)

    Libu Lakhi (Li Jianfu

    2016-02-01

    Full Text Available sa54 da54 (b. 1939 is a Namuyi pha54 tsə54 - a traditional Namuyi religious practitioner/ specialist - who chants and conducts religious rituals. He lives and practices in Liangshan Yi Autonomous Prefecture, Sichuan Province, China. This article introduces pha54 tsə54 sa54 da54 and the many hours of his oral materials recorded by Libu Lakhi in 2009 that are archived at http://tinyurl.com/lgf3row. In addition, aspects of Namuyi religion are described, including terms of mountain deities, ill omens and taboos, the pha54 tsə54, sa54 da54, chants, and a personal account of a ritual done in Dashui Village, Minsheng Township, Xichang City, Sichuan Province in March 2013. An appendix, seven photographs, and a diagram provide additional detail.

  10. Chaotic characteristic of electromagnetic radiation time series of coal or rock under different scales

    Energy Technology Data Exchange (ETDEWEB)

    Zhen-Tang Liu; En-Lai Zhao; En-Yuan Wang; Jing Wang [China University of Mining and Technology, Xuzhou (China). School of Safety Engineering

    2009-02-15

    Based on chaos theory, the chaotic characteristics of electromagnetic radiation time series of coal or rock under different loads was studied. The results show that the correlation of electromagnetic radiation time series of small-scale coal or rock and coal mine converges to a stable saturation value, which shows that these electromagnetic radiation time series have chaos characteristics. When there is danger of coal seam burst, the value of the saturation correlation dimension D{sub 2} of the electromagnetic radiation time series is bigger and it changes greatly; when there is no danger, its value is smaller and changes smoothly. The change of saturation correlation of electromagnetic radiation time series can be used to forecast coal or rock dynamic disasters. 11 refs., 4 figs.

  11. Ridge Polynomial Neural Network with Error Feedback for Time Series Forecasting.

    Science.gov (United States)

    Waheeb, Waddah; Ghazali, Rozaida; Herawan, Tutut

    2016-01-01

    Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback. In this study, we propose a novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF) that incorporates higher order terms, recurrence and error feedback. To evaluate the performance of RPNN-EF, we used four univariate time series with different forecasting horizons, namely star brightness, monthly smoothed sunspot numbers, daily Euro/Dollar exchange rate, and Mackey-Glass time-delay differential equation. We compared the forecasting performance of RPNN-EF with the ordinary Ridge Polynomial Neural Network (RPNN) and the Dynamic Ridge Polynomial Neural Network (DRPNN). Simulation results showed an average 23.34% improvement in Root Mean Square Error (RMSE) with respect to RPNN and an average 10.74% improvement with respect to DRPNN. That means that using network errors during training helps enhance the overall forecasting performance for the network.

  12. Ridge Polynomial Neural Network with Error Feedback for Time Series Forecasting.

    Directory of Open Access Journals (Sweden)

    Waddah Waheeb

    Full Text Available Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback. In this study, we propose a novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF that incorporates higher order terms, recurrence and error feedback. To evaluate the performance of RPNN-EF, we used four univariate time series with different forecasting horizons, namely star brightness, monthly smoothed sunspot numbers, daily Euro/Dollar exchange rate, and Mackey-Glass time-delay differential equation. We compared the forecasting performance of RPNN-EF with the ordinary Ridge Polynomial Neural Network (RPNN and the Dynamic Ridge Polynomial Neural Network (DRPNN. Simulation results showed an average 23.34% improvement in Root Mean Square Error (RMSE with respect to RPNN and an average 10.74% improvement with respect to DRPNN. That means that using network errors during training helps enhance the overall forecasting performance for the network.

  13. Hypocellularity in the Murine Model for Down Syndrome Ts65Dn Is Not Affected by Adult Neurogenesis

    Science.gov (United States)

    López-Hidalgo, Rosa; Ballestín, Raul; Vega, Jessica; Blasco-Ibáñez, José M.; Crespo, Carlos; Gilabert-Juan, Javier; Nácher, Juan; Varea, Emilio

    2016-01-01

    Down syndrome (DS) is caused by the presence of an extra copy of the chromosome 21 and it is the most common aneuploidy producing intellectual disability. Neural mechanisms underlying this alteration may include defects in the formation of neuronal networks, information processing and brain plasticity. The murine model for DS, Ts65Dn, presents reduced adult neurogenesis. This reduction has been suggested to underlie the hypocellularity of the hippocampus as well as the deficit in olfactory learning in the Ts65Dn mice. Similar alterations have also been observed in individuals with DS. To determine whether the impairment in adult neurogenesis is, in fact, responsible for the hypocellularity in the hippocampus and physiology of the olfactory bulb, we have analyzed cell proliferation and neuronal maturation in the two major adult neurogenic niches in the Ts656Dn mice: the subgranular zone (SGZ) of the hippocampus and the subventricular zone (SVZ). Additionally, we carried out a study to determine the survival rate and phenotypic fate of newly generated cells in both regions, injecting 5′BrdU and sacrificing the mice 21 days later, and analyzing the number and phenotype of the remaining 5′BrdU-positive cells. We observed a reduction in the number of proliferating (Ki67 positive) cells and immature (doublecortin positive) neurons in the subgranular and SVZ of Ts65Dn mice, but we did not observe changes in the number of surviving cells or in their phenotype. These data correlated with a lower number of apoptotic cells (cleaved caspase 3 positive) in Ts65Dn. We conclude that although adult Ts65Dn mice have a lower number of proliferating cells, it is compensated by a lower level of cell death. This higher survival rate in Ts65Dn produces a final number of mature cells similar to controls. Therefore, the reduction of adult neurogenesis cannot be held responsible for the neuronal hypocellularity in the hippocampus or for the olfactory learning deficit of Ts65Dn mice

  14. Hierarchical Hidden Markov Models for Multivariate Integer-Valued Time-Series

    DEFF Research Database (Denmark)

    Catania, Leopoldo; Di Mari, Roberto

    2018-01-01

    We propose a new flexible dynamic model for multivariate nonnegative integer-valued time-series. Observations are assumed to depend on the realization of two additional unobserved integer-valued stochastic variables which control for the time-and cross-dependence of the data. An Expectation......-Maximization algorithm for maximum likelihood estimation of the model's parameters is derived. We provide conditional and unconditional (cross)-moments implied by the model, as well as the limiting distribution of the series. A Monte Carlo experiment investigates the finite sample properties of our estimation...

  15. Pre- and post-natal melatonin administration partially regulates brain oxidative stress but does not improve cognitive or histological alterations in the Ts65Dn mouse model of Down syndrome.

    Science.gov (United States)

    Corrales, Andrea; Parisotto, Eduardo B; Vidal, Verónica; García-Cerro, Susana; Lantigua, Sara; Diego, Marian; Wilhem Filho, Danilo; Sanchez-Barceló, Emilio J; Martínez-Cué, Carmen; Rueda, Noemí

    2017-09-15

    Melatonin administered during adulthood induces beneficial effects on cognition and neuroprotection in the Ts65Dn (TS) mouse model of Down syndrome. Here, we investigated the effects of pre- and post-natal melatonin treatment on behavioral and cognitive abnormalities and on several neuromorphological alterations (hypocellularity, neurogenesis impairment and increased oxidative stress) that appear during the early developmental stages in TS mice. Pregnant TS females were orally treated with melatonin or vehicle from the time of conception until the weaning of the offspring, and the pups continued to receive the treatment from weaning until the age of 5 months. Melatonin administered during the pre- and post-natal periods did not improve the cognitive impairment of TS mice as measured by the Morris Water maze or fear conditioning tests. Histological alterations, such as decreased proliferation (Ki67+ cells) and hippocampal hypocellularity (DAPI+ cells), which are typical in TS mice, were not prevented by melatonin. However, melatonin partially regulated brain oxidative stress by modulating the activity of the primary antioxidant enzymes (superoxide dismutase in the cortex and catalase in the cortex and hippocampus) and slightly decreasing the levels of lipid peroxidation in the hippocampus of TS mice. These results show the inability of melatonin to prevent cognitive impairment in TS mice when it is administered at pre- and post-natal stages. Additionally, our findings suggest that to induce pro-cognitive effects in TS mice during the early stages of development, in addition to attenuating oxidative stress, therapies should aim to improve other altered processes, such as hippocampal neurogenesis and/or hypocellularity. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. Single-Index Additive Vector Autoregressive Time Series Models

    KAUST Repository

    LI, YEHUA; GENTON, MARC G.

    2009-01-01

    We study a new class of nonlinear autoregressive models for vector time series, where the current vector depends on single-indexes defined on the past lags and the effects of different lags have an additive form. A sufficient condition is provided

  17. Unstable Periodic Orbit Analysis of Histograms of Chaotic Time Series

    International Nuclear Information System (INIS)

    Zoldi, S.M.

    1998-01-01

    Using the Lorenz equations, we have investigated whether unstable periodic orbits (UPOs) associated with a strange attractor may predict the occurrence of the robust sharp peaks in histograms of some experimental chaotic time series. Histograms with sharp peaks occur for the Lorenz parameter value r=60.0 but not for r=28.0 , and the sharp peaks for r=60.0 do not correspond to a histogram derived from any single UPO. However, we show that histograms derived from the time series of a non-Axiom-A chaotic system can be accurately predicted by an escape-time weighting of UPO histograms. copyright 1998 The American Physical Society

  18. Aleksandr Gol’ts, Armiia Rossii: 11 poteriannykh let, Moscow: Zakharov, 2004, 224 pp.

    Directory of Open Access Journals (Sweden)

    Richard F. Staar

    2004-07-01

    Full Text Available Specialist of military questions, Aleksandr Gol’ts worked for Red Star (Krasnaia Zvezda, the newspaper of the Russian Ministry of Defense, before joining various prestigious magazines such as Itogi. He is considered one of the most prominent journalist on military matters in Russia today. His book, “The Russian Army : 11 lost years”, is dedicated to military reforms in post-Soviet Russia. With his extensive knowledge of institutional realities of the military establishment, Gol’ts provides a...

  19. Arbitrage, market definition and monitoring a time series approach

    OpenAIRE

    Burke, S; Hunter, J

    2012-01-01

    This article considers the application to regional price data of time series methods to test stationarity, multivariate cointegration and exogeneity. The discovery of stationary price differentials in a bivariate setting implies that the series are rendered stationary by capturing a common trend and we observe through this mechanism long-run arbitrage. This is indicative of a broader market definition and efficiency. The problem is considered in relation to more than 700 weekly data points on...

  20. Inverse statistical approach in heartbeat time series

    International Nuclear Information System (INIS)

    Ebadi, H; Shirazi, A H; Mani, Ali R; Jafari, G R

    2011-01-01

    We present an investigation on heart cycle time series, using inverse statistical analysis, a concept borrowed from studying turbulence. Using this approach, we studied the distribution of the exit times needed to achieve a predefined level of heart rate alteration. Such analysis uncovers the most likely waiting time needed to reach a certain change in the rate of heart beat. This analysis showed a significant difference between the raw data and shuffled data, when the heart rate accelerates or decelerates to a rare event. We also report that inverse statistical analysis can distinguish between the electrocardiograms taken from healthy volunteers and patients with heart failure

  1. Artificial neural networks applied to forecasting time series.

    Science.gov (United States)

    Montaño Moreno, Juan J; Palmer Pol, Alfonso; Muñoz Gracia, Pilar

    2011-04-01

    This study offers a description and comparison of the main models of Artificial Neural Networks (ANN) which have proved to be useful in time series forecasting, and also a standard procedure for the practical application of ANN in this type of task. The Multilayer Perceptron (MLP), Radial Base Function (RBF), Generalized Regression Neural Network (GRNN), and Recurrent Neural Network (RNN) models are analyzed. With this aim in mind, we use a time series made up of 244 time points. A comparative study establishes that the error made by the four neural network models analyzed is less than 10%. In accordance with the interpretation criteria of this performance, it can be concluded that the neural network models show a close fit regarding their forecasting capacity. The model with the best performance is the RBF, followed by the RNN and MLP. The GRNN model is the one with the worst performance. Finally, we analyze the advantages and limitations of ANN, the possible solutions to these limitations, and provide an orientation towards future research.

  2. A general statistical test for correlations in a finite-length time series.

    Science.gov (United States)

    Hanson, Jeffery A; Yang, Haw

    2008-06-07

    The statistical properties of the autocorrelation function from a time series composed of independently and identically distributed stochastic variables has been studied. Analytical expressions for the autocorrelation function's variance have been derived. It has been found that two common ways of calculating the autocorrelation, moving-average and Fourier transform, exhibit different uncertainty characteristics. For periodic time series, the Fourier transform method is preferred because it gives smaller uncertainties that are uniform through all time lags. Based on these analytical results, a statistically robust method has been proposed to test the existence of correlations in a time series. The statistical test is verified by computer simulations and an application to single-molecule fluorescence spectroscopy is discussed.

  3. ECONOMETRIC APPROACH OF HETEROSKEDASTICITY ON FINANCIAL TIME SERIES IN A GENERAL FRAMEWORK

    Directory of Open Access Journals (Sweden)

    FELICIA RAMONA BIRĂU

    2012-12-01

    Full Text Available The aim of this paper is to provide an overview of the diagnostic tests for detecting heteroskedasticity on financial time series. In financial econometrics, heteroskedasticity is generally associated with cross sectional data but can also be identified modeling time series data. The presence of heteroscedasticity in financial time series can be caused by certain specific factors, like a model misspecification, inadequate data transformation or as a result of certain outliers. Heteroskedasticity arise when the homoskedasticity assumption is violated. Testing for the presence of heteroskedasticity in financial time is performed by applying diagnostic test, such as : Breusch-Pagan LM test, White’s test, Glesjer LM test, Harvey-Godfrey LM test, Park LM test and Goldfeld-Quand test.

  4. Deriving crop calendar using NDVI time-series

    Science.gov (United States)

    Patel, J. H.; Oza, M. P.

    2014-11-01

    Agricultural intensification is defined in terms as cropping intensity, which is the numbers of crops (single, double and triple) per year in a unit cropland area. Information about crop calendar (i.e. number of crops in a parcel of land and their planting & harvesting dates and date of peak vegetative stage) is essential for proper management of agriculture. Remote sensing sensors provide a regular, consistent and reliable measurement of vegetation response at various growth stages of crop. Therefore it is ideally suited for monitoring purpose. The spectral response of vegetation, as measured by the Normalized Difference Vegetation Index (NDVI) and its profiles, can provide a new dimension for describing vegetation growth cycle. The analysis based on values of NDVI at regular time interval provides useful information about various crop growth stages and performance of crop in a season. However, the NDVI data series has considerable amount of local fluctuation in time domain and needs to be smoothed so that dominant seasonal behavior is enhanced. Based on temporal analysis of smoothed NDVI series, it is possible to extract number of crop cycles per year and their crop calendar. In the present study, a methodology is developed to extract key elements of crop growth cycle (i.e. number of crops per year and their planting - peak - harvesting dates). This is illustrated by analysing MODIS-NDVI data series of one agricultural year (from June 2012 to May 2013) over Gujarat. Such an analysis is very useful for analysing dynamics of kharif and rabi crops.

  5. The Santander Atlantic Time-Series Station (SATS): A Time Series combination of a monthly hydrographic Station and The Biscay AGL Oceanic Observatory.

    Science.gov (United States)

    Lavin, Alicia; Somavilla, Raquel; Cano, Daniel; Rodriguez, Carmen; Gonzalez-Pola, Cesar; Viloria, Amaia; Tel, Elena; Ruiz-Villareal, Manuel

    2017-04-01

    Long-Term Time Series Stations have been developed in order to document seasonal to decadal scale variations in key physical and biogeochemical parameters. Long-term time series measurements are crucial for determining the physical and biological mechanisms controlling the system. The Science and Technology Ministers of the G7 in their Tsukuba Communiqué have stated that 'many parts of the ocean interior are not sufficiently observed' and that 'it is crucial to develop far stronger scientific knowledge necessary to assess the ongoing changes in the ocean and their impact on economies.' Time series has been classically obtained by oceanographic ships that regularly cover standard sections and stations. From 1991, shelf and slope waters of the Southern Bay of Biscay are regularly sampled in a monthly hydrographic line north of Santander to a depth of 1000 m in early stages and for the whole water column down to 2580 m in recent times. Nearby, in June 2007, the IEO deployed an oceanic-meteorological buoy (AGL Buoy, 43° 50.67'N; 3° 46.20'W, and 40 km offshore, www.boya-agl.st.ieo.es). The Santander Atlantic Time Series Station is integrated in the Spanish Institute of Oceanography Observing Sistem (IEOOS). The long-term hydrographic monitoring has allowed to define the seasonality of the main oceanographic facts as the upwelling, the Iberian Poleward Current, low salinity incursions, trends and interannual variability at mixing layer, and at the main water masses North Atlantic Central Water and Mediterranean Water. The relation of these changes with the high frequency surface conditions recorded by the Biscay AGL has been examined using also satellite and reanalysis data. During the FIXO3 Project (Fixed-point Open Ocean Observatories), and using this combined sources, some products and quality controled series of high interest and utility for scientific purposes has been developed. Hourly products as Sea Surface Temperature and Salinity anomalies, wave significant

  6. Multivariate time series clustering on geophysical data recorded at Mt. Etna from 1996 to 2003

    Science.gov (United States)

    Di Salvo, Roberto; Montalto, Placido; Nunnari, Giuseppe; Neri, Marco; Puglisi, Giuseppe

    2013-02-01

    Time series clustering is an important task in data analysis issues in order to extract implicit, previously unknown, and potentially useful information from a large collection of data. Finding useful similar trends in multivariate time series represents a challenge in several areas including geophysics environment research. While traditional time series analysis methods deal only with univariate time series, multivariate time series analysis is a more suitable approach in the field of research where different kinds of data are available. Moreover, the conventional time series clustering techniques do not provide desired results for geophysical datasets due to the huge amount of data whose sampling rate is different according to the nature of signal. In this paper, a novel approach concerning geophysical multivariate time series clustering is proposed using dynamic time series segmentation and Self Organizing Maps techniques. This method allows finding coupling among trends of different geophysical data recorded from monitoring networks at Mt. Etna spanning from 1996 to 2003, when the transition from summit eruptions to flank eruptions occurred. This information can be used to carry out a more careful evaluation of the state of volcano and to define potential hazard assessment at Mt. Etna.

  7. Time series analysis methods and applications for flight data

    CERN Document Server

    Zhang, Jianye

    2017-01-01

    This book focuses on different facets of flight data analysis, including the basic goals, methods, and implementation techniques. As mass flight data possesses the typical characteristics of time series, the time series analysis methods and their application for flight data have been illustrated from several aspects, such as data filtering, data extension, feature optimization, similarity search, trend monitoring, fault diagnosis, and parameter prediction, etc. An intelligent information-processing platform for flight data has been established to assist in aircraft condition monitoring, training evaluation and scientific maintenance. The book will serve as a reference resource for people working in aviation management and maintenance, as well as researchers and engineers in the fields of data analysis and data mining.

  8. The Impact of the Hotel Room Tax: An Interrupted Time Series Approach

    OpenAIRE

    Bonham, Carl; Fujii, Edwin; Im, Eric; Mak, James

    1992-01-01

    Employs interrupted time series analysis to estimate ex post the impact of a hotel room tax on real net hotel revenues by analyzing that time series before and after the imposition of the tax. Finds that the tax had a negligible effect on real hotel revenues.

  9. Mapping air temperature using time series analysis of LST : The SINTESI approach

    NARCIS (Netherlands)

    Alfieri, S.M.; De Lorenzi, F.; Menenti, M.

    2013-01-01

    This paper presents a new procedure to map time series of air temperature (Ta) at fine spatial resolution using time series analysis of satellite-derived land surface temperature (LST) observations. The method assumes that air temperature is known at a single (reference) location such as in gridded

  10. Optimization of recurrent neural networks for time series modeling

    DEFF Research Database (Denmark)

    Pedersen, Morten With

    1997-01-01

    The present thesis is about optimization of recurrent neural networks applied to time series modeling. In particular is considered fully recurrent networks working from only a single external input, one layer of nonlinear hidden units and a li near output unit applied to prediction of discrete time...... series. The overall objective s are to improve training by application of second-order methods and to improve generalization ability by architecture optimization accomplished by pruning. The major topics covered in the thesis are: 1. The problem of training recurrent networks is analyzed from a numerical...... of solution obtained as well as computation time required. 3. A theoretical definition of the generalization error for recurrent networks is provided. This definition justifies a commonly adopted approach for estimating generalization ability. 4. The viability of pruning recurrent networks by the Optimal...

  11. Move of Purchasing Offices TS – AB* – AT*

    CERN Multimedia

    FI Department

    2008-01-01

    The TS – AB* - AT* Purchasing Offices and the Purchasing Pool have moved to Building 5 – 2nd and *3rd floors. The phone and fax numbers are unchanged. We apologize for any inconvenience caused by the move. Thank you for your understanding. Finance Department – Purchasing Service.

  12. Time-series analysis of Nigeria rice supply and demand: Error ...

    African Journals Online (AJOL)

    The study examined a time-series analysis of Nigeria rice supply and demand with a view to determining any long-run equilibrium between them using the Error Correction Model approach (ECM). The data used for the study represents the annual series of 1960-2007 (47 years) for rice supply and demand in Nigeria, ...

  13. Energy-Based Wavelet De-Noising of Hydrologic Time Series

    Science.gov (United States)

    Sang, Yan-Fang; Liu, Changming; Wang, Zhonggen; Wen, Jun; Shang, Lunyu

    2014-01-01

    De-noising is a substantial issue in hydrologic time series analysis, but it is a difficult task due to the defect of methods. In this paper an energy-based wavelet de-noising method was proposed. It is to remove noise by comparing energy distribution of series with the background energy distribution, which is established from Monte-Carlo test. Differing from wavelet threshold de-noising (WTD) method with the basis of wavelet coefficient thresholding, the proposed method is based on energy distribution of series. It can distinguish noise from deterministic components in series, and uncertainty of de-noising result can be quantitatively estimated using proper confidence interval, but WTD method cannot do this. Analysis of both synthetic and observed series verified the comparable power of the proposed method and WTD, but de-noising process by the former is more easily operable. The results also indicate the influences of three key factors (wavelet choice, decomposition level choice and noise content) on wavelet de-noising. Wavelet should be carefully chosen when using the proposed method. The suitable decomposition level for wavelet de-noising should correspond to series' deterministic sub-signal which has the smallest temporal scale. If too much noise is included in a series, accurate de-noising result cannot be obtained by the proposed method or WTD, but the series would show pure random but not autocorrelation characters, so de-noising is no longer needed. PMID:25360533

  14. Fractal dimension algorithms and their application to time series associated with natural phenomena

    International Nuclear Information System (INIS)

    La Torre, F Cervantes-De; González-Trejo, J I; Real-Ramírez, C A; Hoyos-Reyes, L F

    2013-01-01

    Chaotic invariants like the fractal dimensions are used to characterize non-linear time series. The fractal dimension is an important characteristic of systems, because it contains information about their geometrical structure at multiple scales. In this work, three algorithms are applied to non-linear time series: spectral analysis, rescaled range analysis and Higuchi's algorithm. The analyzed time series are associated with natural phenomena. The disturbance storm time (Dst) is a global indicator of the state of the Earth's geomagnetic activity. The time series used in this work show a self-similar behavior, which depends on the time scale of measurements. It is also observed that fractal dimensions, D, calculated with Higuchi's method may not be constant over-all time scales. This work shows that during 2001, D reaches its lowest values in March and November. The possibility that D recovers a change pattern arising from self-organized critical phenomena is also discussed

  15. Detection of "noisy" chaos in a time series

    DEFF Research Database (Denmark)

    Chon, K H; Kanters, J K; Cohen, R J

    1997-01-01

    Time series from biological system often displays fluctuations in the measured variables. Much effort has been directed at determining whether this variability reflects deterministic chaos, or whether it is merely "noise". The output from most biological systems is probably the result of both...

  16. Handbook of Time Series Analysis Recent Theoretical Developments and Applications

    CERN Document Server

    Schelter, Björn; Timmer, Jens

    2006-01-01

    This handbook provides an up-to-date survey of current research topics and applications of time series analysis methods written by leading experts in their fields. It covers recent developments in univariate as well as bivariate and multivariate time series analysis techniques ranging from physics' to life sciences' applications. Each chapter comprises both methodological aspects and applications to real world complex systems, such as the human brain or Earth's climate. Covering an exceptionally broad spectrum of topics, beginners, experts and practitioners who seek to understand the latest de

  17. feets: feATURE eXTRACTOR for tIME sERIES

    Science.gov (United States)

    Cabral, Juan; Sanchez, Bruno; Ramos, Felipe; Gurovich, Sebastián; Granitto, Pablo; VanderPlas, Jake

    2018-06-01

    feets characterizes and analyzes light-curves from astronomical photometric databases for modelling, classification, data cleaning, outlier detection and data analysis. It uses machine learning algorithms to determine the numerical descriptors that characterize and distinguish the different variability classes of light-curves; these range from basic statistical measures such as the mean or standard deviation to complex time-series characteristics such as the autocorrelation function. The library is not restricted to the astronomical field and could also be applied to any kind of time series. This project is a derivative work of FATS (ascl:1711.017).

  18. The impact of ISO/TS 16949 on automotive industries and created organizational capabilities from its implementation

    Directory of Open Access Journals (Sweden)

    Bakhtiar Ostadi

    2010-12-01

    Full Text Available ISO/TS 16949 is an ISO Technical Specification. ISO/TS 16949 achieves the objectives which are continually to improve the production of automobile parts and related services, and to strengthen the international competition for the automotive industry and its suppliers. By applying this quality system standard, automotive manufacturers could offer superior products and good services to customers. The more the companies know about the benefits if quality management systems such as ISO/TS 16949, better they can seek interest and determine indices of these systems. So, this standard has been implemented in companies considering a number of benefits. In this paper, we carry out an empirical study in order to verify the importance these benefits and ranking them based on the value of importance. Finally, the study tends to provide a reference guide considering benefits assessment and created organizational capabilities from this standard for the automotive industry in pursuing ISO/TS 16949 and procuring maximum benefit from the results.

  19. Reconstruction of network topology using status-time-series data

    Science.gov (United States)

    Pandey, Pradumn Kumar; Badarla, Venkataramana

    2018-01-01

    Uncovering the heterogeneous connection pattern of a networked system from the available status-time-series (STS) data of a dynamical process on the network is of great interest in network science and known as a reverse engineering problem. Dynamical processes on a network are affected by the structure of the network. The dependency between the diffusion dynamics and structure of the network can be utilized to retrieve the connection pattern from the diffusion data. Information of the network structure can help to devise the control of dynamics on the network. In this paper, we consider the problem of network reconstruction from the available status-time-series (STS) data using matrix analysis. The proposed method of network reconstruction from the STS data is tested successfully under susceptible-infected-susceptible (SIS) diffusion dynamics on real-world and computer-generated benchmark networks. High accuracy and efficiency of the proposed reconstruction procedure from the status-time-series data define the novelty of the method. Our proposed method outperforms compressed sensing theory (CST) based method of network reconstruction using STS data. Further, the same procedure of network reconstruction is applied to the weighted networks. The ordering of the edges in the weighted networks is identified with high accuracy.

  20. Spectral Unmixing Analysis of Time Series Landsat 8 Images

    Science.gov (United States)

    Zhuo, R.; Xu, L.; Peng, J.; Chen, Y.

    2018-05-01

    Temporal analysis of Landsat 8 images opens up new opportunities in the unmixing procedure. Although spectral analysis of time series Landsat imagery has its own advantage, it has rarely been studied. Nevertheless, using the temporal information can provide improved unmixing performance when compared to independent image analyses. Moreover, different land cover types may demonstrate different temporal patterns, which can aid the discrimination of different natures. Therefore, this letter presents time series K-P-Means, a new solution to the problem of unmixing time series Landsat imagery. The proposed approach is to obtain the "purified" pixels in order to achieve optimal unmixing performance. The vertex component analysis (VCA) is used to extract endmembers for endmember initialization. First, nonnegative least square (NNLS) is used to estimate abundance maps by using the endmember. Then, the estimated endmember is the mean value of "purified" pixels, which is the residual of the mixed pixel after excluding the contribution of all nondominant endmembers. Assembling two main steps (abundance estimation and endmember update) into the iterative optimization framework generates the complete algorithm. Experiments using both simulated and real Landsat 8 images show that the proposed "joint unmixing" approach provides more accurate endmember and abundance estimation results compared with "separate unmixing" approach.