Turbulent Fluid Motion 6: Turbulence, Nonlinear Dynamics, and Deterministic Chaos

Science.gov (United States)

Deissler, Robert G.

1996-01-01

Several turbulent and nonturbulent solutions of the Navier-Stokes equations are obtained. The unaveraged equations are used numerically in conjunction with tools and concepts from nonlinear dynamics, including time series, phase portraits, Poincare sections, Liapunov exponents, power spectra, and strange attractors. Initially neighboring solutions for a low-Reynolds-number fully developed turbulence are compared. The turbulence is sustained by a nonrandom time-independent external force. The solutions, on the average, separate exponentially with time, having a positive Liapunov exponent. Thus, the turbulence is characterized as chaotic. In a search for solutions which contrast with the turbulent ones, the Reynolds number (or strength of the forcing) is reduced. Several qualitatively different flows are noted. These are, respectively, fully chaotic, complex periodic, weakly chaotic, simple periodic, and fixed-point. Of these, we classify only the fully chaotic flows as turbulent. Those flows have both a positive Liapunov exponent and Poincare sections without pattern. By contrast, the weakly chaotic flows, although having positive Liapunov exponents, have some pattern in their Poincare sections. The fixed-point and periodic flows are nonturbulent, since turbulence, as generally understood, is both time-dependent and aperiodic.

Gaussian vs non-Gaussian turbulence: impact on wind turbine loads

DEFF Research Database (Denmark)

Berg, Jacob; Natarajan, Anand; Mann, Jakob

2016-01-01

taking into account the safety factor for extreme moments. Other extreme load moments as well as the fatigue loads are not affected because of the use of non-Gaussian turbulent inflow. It is suggested that the turbine thus acts like a low-pass filter that averages out the non-Gaussian behaviour, which......From large-eddy simulations of atmospheric turbulence, a representation of Gaussian turbulence is constructed by randomizing the phases of the individual modes of variability. Time series of Gaussian turbulence are constructed and compared with its non-Gaussian counterpart. Time series from the two...

Validity of the assumption of Gaussian turbulence; Gyldighed af antagelsen om Gaussisk turbulence

Energy Technology Data Exchange (ETDEWEB)

Nielsen, M.; Hansen, K.S.; Juul Pedersen, B.

2000-07-01

Wind turbines are designed to withstand the impact of turbulent winds, which fluctuations usually are assumed of Gaussian probability distribution. Based on a large number of measurements from many sites, this seems a reasonable assumption in flat homogeneous terrain whereas it may fail in complex terrain. At these sites the wind speed often has a skew distribution with more frequent lulls than gusts. In order to simulate aerodynamic loads, a numerical turbulence simulation method was developed and implemented. This method may simulate multiple time series of variable not necessarily Gaussian distribution without distortion of the spectral distribution or spatial coherence. The simulated time series were used as input to the dynamic-response simulation program Vestas Turbine Simulator (VTS). In this way we simulated the dynamic response of systems exposed to turbulence of either Gaussian or extreme, yet realistic, non-Gaussian probability distribution. Certain loads on turbines with active pitch regulation were enhanced by up to 15% compared to pure Gaussian turbulence. It should, however, be said that the undesired effect depends on the dynamic system, and it might be mitigated by optimisation of the wind turbine regulation system after local turbulence characteristics. (au)

Non-gaussian turbulence

Energy Technology Data Exchange (ETDEWEB)

Hoejstrup, J [NEG Micon Project Development A/S, Randers (Denmark); Hansen, K S [Denmarks Technical Univ., Dept. of Energy Engineering, Lyngby (Denmark); Pedersen, B J [VESTAS Wind Systems A/S, Lem (Denmark); Nielsen, M [Risoe National Lab., Wind Energy and Atmospheric Physics, Roskilde (Denmark)

1999-03-01

The pdf`s of atmospheric turbulence have somewhat wider tails than a Gaussian, especially regarding accelerations, whereas velocities are close to Gaussian. This behaviour is being investigated using data from a large WEB-database in order to quantify the amount of non-Gaussianity. Models for non-Gaussian turbulence have been developed, by which artificial turbulence can be generated with specified distributions, spectra and cross-correlations. The artificial time series will then be used in load models and the resulting loads in the Gaussian and the non-Gaussian cases will be compared. (au)

Simulation of Ground Winds Time Series for the NASA Crew Launch Vehicle (CLV)

Science.gov (United States)

Adelfang, Stanley I.

2008-01-01

Simulation of wind time series based on power spectrum density (PSD) and spectral coherence models for ground wind turbulence is described. The wind models, originally developed for the Shuttle program, are based on wind measurements at the NASA 150-m meteorological tower at Cape Canaveral, FL. The current application is for the design and/or protection of the CLV from wind effects during on-pad exposure during periods from as long as days prior to launch, to seconds or minutes just prior to launch and seconds after launch. The evaluation of vehicle response to wind will influence the design and operation of constraint systems for support of the on-pad vehicle. Longitudinal and lateral wind component time series are simulated at critical vehicle locations. The PSD model for wind turbulence is a function of mean wind speed, elevation and temporal frequency. Integration of the PSD equation over a selected frequency range yields the variance of the time series to be simulated. The square root of the PSD defines a low-pass filter that is applied to adjust the components of the Fast Fourier Transform (FFT) of Gaussian white noise. The first simulated time series near the top of the launch vehicle is the inverse transform of the adjusted FFT. Simulation of the wind component time series at the nearest adjacent location (and all other succeeding next nearest locations) is based on a model for the coherence between winds at two locations as a function of frequency and separation distance, where the adjacent locations are separated vertically and/or horizontally. The coherence function is used to calculate a coherence weighted FFT of the wind at the next nearest location, given the FFT of the simulated time series at the previous location and the essentially incoherent FFT of the wind at the selected location derived a priori from the PSD model. The simulated time series at each adjacent location is the inverse Fourier transform of the coherence weighted FFT. For a selected

Tetrahedral-Mesh Simulation of Turbulent Flows with the Space-Time Conservative Schemes

Science.gov (United States)

Chang, Chau-Lyan; Venkatachari, Balaji; Cheng, Gary C.

2015-01-01

Direct numerical simulations of turbulent flows are predominantly carried out using structured, hexahedral meshes despite decades of development in unstructured mesh methods. Tetrahedral meshes offer ease of mesh generation around complex geometries and the potential of an orientation free grid that would provide un-biased small-scale dissipation and more accurate intermediate scale solutions. However, due to the lack of consistent multi-dimensional numerical formulations in conventional schemes for triangular and tetrahedral meshes at the cell interfaces, numerical issues exist when flow discontinuities or stagnation regions are present. The space-time conservative conservation element solution element (CESE) method - due to its Riemann-solver-free shock capturing capabilities, non-dissipative baseline schemes, and flux conservation in time as well as space - has the potential to more accurately simulate turbulent flows using unstructured tetrahedral meshes. To pave the way towards accurate simulation of shock/turbulent boundary-layer interaction, a series of wave and shock interaction benchmark problems that increase in complexity, are computed in this paper with triangular/tetrahedral meshes. Preliminary computations for the normal shock/turbulence interactions are carried out with a relatively coarse mesh, by direct numerical simulations standards, in order to assess other effects such as boundary conditions and the necessity of a buffer domain. The results indicate that qualitative agreement with previous studies can be obtained for flows where, strong shocks co-exist along with unsteady waves that display a broad range of scales, with a relatively compact computational domain and less stringent requirements for grid clustering near the shock. With the space-time conservation properties, stable solutions without any spurious wave reflections can be obtained without a need for buffer domains near the outflow/farfield boundaries. Computational results for the

De-trending of turbulence measurements

DEFF Research Database (Denmark)

Hansen, Kurt Schaldemose; Larsen, Gunner Chr.

2006-01-01

contribution to the wind speed turbulence intensity for a number of representative locations. A linear de-trending process has been implemented during indexing of the time-series. The observed de-trended turbulence intensities are reduced 3 – 15 % compared to the raw turbulence intensity. This reduction...... depends primarily on site characteristics and local mean wind speed variations. Reduced turbulence intensity will result in lower design fatigue loads. This aspect of de-trending is discussed by use of a simple heuristic load model. Finally an empirical model for de-trending wind resource data...

Comparisons between two wavelet functions in extracting coherent structures from solar wind time series

International Nuclear Information System (INIS)

Bolzani, M.J.A.; Guarnieri, F.L.; Vieira, Paulo Cesar

2009-01-01

Nowadays, wavelet analysis of turbulent flows have become increasingly popular. However, the study of geometric characteristics from wavelet functions is still poorly explored. In this work we compare the performance of two wavelet functions in extracting the coherent structures from solar wind velocity time series. The data series are from years 1996 to 2002 (except 1998 and 1999). The wavelet algorithm decomposes the annual time-series in two components: the coherent part and non-coherent one, using the daubechies-4 and haar wavelet function. The threshold assumed is based on a percentage of maximum variance found in each dyadic scale. After the extracting procedure, we applied the power spectral density on the original time series and coherent time series to obtain spectral indices. The results from spectral indices show higher values for the coherent part obtained by daubechies-4 than those obtained by the haar wavelet function. Using the kurtosis statistical parameter, on coherent and non-coherent time series, it was possible to conjecture that the differences found between two wavelet functions may be associated with their geometric forms. (author)

A time-localized response of wave growth process under turbulent winds

Directory of Open Access Journals (Sweden)

Z. Ge

2007-06-01

Full Text Available Very short time series (with lengths of approximately 40 s or 5~7 wave periods of wind velocity fluctuations and wave elevation were recorded simultaneously and investigated using the wavelet bispectral analysis. Rapid changes in the wave and wind spectra were detected, which were found to be intimately related to significant energy transfers through transient quadratic wind-wave and wave-wave interactions. A possible pattern of energy exchange between the wind and wave fields was further deduced. In particular, the generation and variation of the strong wave-induced perturbation velocity in the wind can be explained by the strengthening and diminishing of the associated quadratic interactions, which cannot be unveiled by linear theories. On small time scales, the wave-wave quadratic interactions were as active and effective in transferring energy as the wind-wave interactions. The results also showed that the wind turbulence was occasionally effective in transferring energy between the wind and the wave fields, so that the background turbulence in the wind cannot be completely neglected. Although these effects are all possibly significant over short times, the time-localized growth of the wave spectrum may not considerably affect the long-term process of wave development.

Time series analysis of wind speed using VAR and the generalized impulse response technique

Energy Technology Data Exchange (ETDEWEB)

Ewing, Bradley T. [Area of Information Systems and Quantitative Sciences, Rawls College of Business and Wind Science and Engineering Research Center, Texas Tech University, Lubbock, TX 79409-2101 (United States); Kruse, Jamie Brown [Center for Natural Hazard Research, East Carolina University, Greenville, NC (United States); Schroeder, John L. [Department of Geosciences and Wind Science and Engineering Research Center, Texas Tech University, Lubbock, TX (United States); Smith, Douglas A. [Department of Civil Engineering and Wind Science and Engineering Research Center, Texas Tech University, Lubbock, TX (United States)

2007-03-15

This research examines the interdependence in time series wind speed data measured in the same location at four different heights. A multiple-equation system known as a vector autoregression is proposed for characterizing the time series dynamics of wind. Additionally, the recently developed method of generalized impulse response analysis provides insight into the cross-effects of the wind series and their responses to shocks. Findings are based on analysis of contemporaneous wind speed time histories taken at 13, 33, 70 and 160 ft above ground level with a sampling rate of 10 Hz. The results indicate that wind speeds measured at 70 ft was the most variable. Further, the turbulence persisted longer at the 70-ft measurement than at the other heights. The greatest interdependence is observed at 13 ft. Gusts at 160 ft led to the greatest persistence to an 'own' shock and led to greatest persistence in the responses of the other wind series. (author)

Stochastic Subspace Modelling of Turbulence

DEFF Research Database (Denmark)

Sichani, Mahdi Teimouri; Pedersen, B. J.; Nielsen, Søren R.K.

2009-01-01

positive definite cross-spectral density matrix a frequency response matrix is constructed which determines the turbulence vector as a linear filtration of Gaussian white noise. Finally, an accurate state space modelling method is proposed which allows selection of an appropriate model order......, and estimation of a state space model for the vector turbulence process incorporating its phase spectrum in one stage, and its results are compared with a conventional ARMA modelling method.......Turbulence of the incoming wind field is of paramount importance to the dynamic response of civil engineering structures. Hence reliable stochastic models of the turbulence should be available from which time series can be generated for dynamic response and structural safety analysis. In the paper...

On the Space-Time Structure of Sheared Turbulence

DEFF Research Database (Denmark)

de Mare, Martin Tobias; Mann, Jakob

2016-01-01

We develop a model that predicts all two-point correlations in high Reynolds number turbulent flow, in both space and time. This is accomplished by combining the design philosophies behind two existing models, the Mann spectral velocity tensor, in which isotropic turbulence is distorted according......-assisted feed forward control and wind-turbine wake modelling....

Time-series analysis to study the impact of an intersection on dispersion along a street canyon.

Science.gov (United States)

Richmond-Bryant, Jennifer; Eisner, Alfred D; Hahn, Intaek; Fortune, Christopher R; Drake-Richman, Zora E; Brixey, Laurie A; Talih, M; Wiener, Russell W; Ellenson, William D

2009-12-01

This paper presents data analysis from the Brooklyn Traffic Real-Time Ambient Pollutant Penetration and Environmental Dispersion (B-TRAPPED) study to assess the transport of ultrafine particulate matter (PM) across urban intersections. Experiments were performed in a street canyon perpendicular to a highway in Brooklyn, NY, USA. Real-time ultrafine PM samplers were positioned on either side of an intersection at multiple locations along a street to collect time-series number concentration data. Meteorology equipment was positioned within the street canyon and at an upstream background site to measure wind speed and direction. Time-series analysis was performed on the PM data to compute a transport velocity along the direction of the street for the cases where background winds were parallel and perpendicular to the street. The data were analyzed for sampler pairs located (1) on opposite sides of the intersection and (2) on the same block. The time-series analysis demonstrated along-street transport, including across the intersection when background winds were parallel to the street canyon and there was minimal transport and no communication across the intersection when background winds were perpendicular to the street canyon. Low but significant values of the cross-correlation function (CCF) underscore the turbulent nature of plume transport along the street canyon. The low correlations suggest that flow switching around corners or traffic-induced turbulence at the intersection may have aided dilution of the PM plume from the highway. This observation supports similar findings in the literature. Furthermore, the time-series analysis methodology applied in this study is introduced as a technique for studying spatiotemporal variation in the urban microscale environment.

New time scale based k-epsilon model for near-wall turbulence

Science.gov (United States)

Yang, Z.; Shih, T. H.

1993-01-01

A k-epsilon model is proposed for wall bonded turbulent flows. In this model, the eddy viscosity is characterized by a turbulent velocity scale and a turbulent time scale. The time scale is bounded from below by the Kolmogorov time scale. The dissipation equation is reformulated using this time scale and no singularity exists at the wall. The damping function used in the eddy viscosity is chosen to be a function of R(sub y) = (k(sup 1/2)y)/v instead of y(+). Hence, the model could be used for flows with separation. The model constants used are the same as in the high Reynolds number standard k-epsilon model. Thus, the proposed model will be also suitable for flows far from the wall. Turbulent channel flows at different Reynolds numbers and turbulent boundary layer flows with and without pressure gradient are calculated. Results show that the model predictions are in good agreement with direct numerical simulation and experimental data.

Effects of forcing time scale on the simulated turbulent flows and turbulent collision statistics of inertial particles

International Nuclear Information System (INIS)

Rosa, B.; Parishani, H.; Ayala, O.; Wang, L.-P.

2015-01-01

In this paper, we study systematically the effects of forcing time scale in the large-scale stochastic forcing scheme of Eswaran and Pope [“An examination of forcing in direct numerical simulations of turbulence,” Comput. Fluids 16, 257 (1988)] on the simulated flow structures and statistics of forced turbulence. Using direct numerical simulations, we find that the forcing time scale affects the flow dissipation rate and flow Reynolds number. Other flow statistics can be predicted using the altered flow dissipation rate and flow Reynolds number, except when the forcing time scale is made unrealistically large to yield a Taylor microscale flow Reynolds number of 30 and less. We then study the effects of forcing time scale on the kinematic collision statistics of inertial particles. We show that the radial distribution function and the radial relative velocity may depend on the forcing time scale when it becomes comparable to the eddy turnover time. This dependence, however, can be largely explained in terms of altered flow Reynolds number and the changing range of flow length scales present in the turbulent flow. We argue that removing this dependence is important when studying the Reynolds number dependence of the turbulent collision statistics. The results are also compared to those based on a deterministic forcing scheme to better understand the role of large-scale forcing, relative to that of the small-scale turbulence, on turbulent collision of inertial particles. To further elucidate the correlation between the altered flow structures and dynamics of inertial particles, a conditional analysis has been performed, showing that the regions of higher collision rate of inertial particles are well correlated with the regions of lower vorticity. Regions of higher concentration of pairs at contact are found to be highly correlated with the region of high energy dissipation rate

Simple Analytical Forms of the Perpendicular Diffusion Coefficient for Two-component Turbulence. III. Damping Model of Dynamical Turbulence

Energy Technology Data Exchange (ETDEWEB)

Gammon, M.; Shalchi, A., E-mail: andreasm4@yahoo.com [Department of Physics and Astronomy, University of Manitoba, Winnipeg, Manitoba R3T 2N2 (Canada)

2017-10-01

In several astrophysical applications one needs analytical forms of cosmic-ray diffusion parameters. Some examples are studies of diffusive shock acceleration and solar modulation. In the current article we explore perpendicular diffusion based on the unified nonlinear transport theory. While we focused on magnetostatic turbulence in Paper I, we included the effect of dynamical turbulence in Paper II of the series. In the latter paper we assumed that the temporal correlation time does not depend on the wavenumber. More realistic models have been proposed in the past, such as the so-called damping model of dynamical turbulence. In the present paper we derive analytical forms for the perpendicular diffusion coefficient of energetic particles in two-component turbulence for this type of time-dependent turbulence. We present new formulas for the perpendicular diffusion coefficient and we derive a condition for which the magnetostatic result is recovered.

An advection-based model to increase the temporal resolution of PIV time series.

Science.gov (United States)

Scarano, Fulvio; Moore, Peter

A numerical implementation of the advection equation is proposed to increase the temporal resolution of PIV time series. The method is based on the principle that velocity fluctuations are transported passively, similar to Taylor's hypothesis of frozen turbulence . In the present work, the advection model is extended to unsteady three-dimensional flows. The main objective of the method is that of lowering the requirement on the PIV repetition rate from the Eulerian frequency toward the Lagrangian one. The local trajectory of the fluid parcel is obtained by forward projection of the instantaneous velocity at the preceding time instant and backward projection from the subsequent time step. The trajectories are approximated by the instantaneous streamlines, which yields accurate results when the amplitude of velocity fluctuations is small with respect to the convective motion. The verification is performed with two experiments conducted at temporal resolutions significantly higher than that dictated by Nyquist criterion. The flow past the trailing edge of a NACA0012 airfoil closely approximates frozen turbulence , where the largest ratio between the Lagrangian and Eulerian temporal scales is expected. An order of magnitude reduction of the needed acquisition frequency is demonstrated by the velocity spectra of super-sampled series. The application to three-dimensional data is made with time-resolved tomographic PIV measurements of a transitional jet. Here, the 3D advection equation is implemented to estimate the fluid trajectories. The reduction in the minimum sampling rate by the use of super-sampling in this case is less, due to the fact that vortices occurring in the jet shear layer are not well approximated by sole advection at large time separation. Both cases reveal that the current requirements for time-resolved PIV experiments can be revised when information is poured from space to time . An additional favorable effect is observed by the analysis in the

Chaotic radiation/turbulence interactions in flames

Energy Technology Data Exchange (ETDEWEB)

Menguec, M.P.; McDonough, J.M.

1998-11-01

In this paper, the authors present a review of their recent efforts to model chaotic radiation-turbulence interactions in flames. The main focus is to characterize soot volume fraction fluctuations in turbulent diffusion flames, as they strongly contribute to these interaction. The approach is based on the hypothesis that the fluctuations of properties in turbulent flames are deterministic in nature, rather than random. The authors first discuss the theoretical details and then they briefly outline the experiments conducted to measure the scattered light signals from fluctuating soot particles along the axis of an ethylene-air diffusion flame. They compare the power spectra and time series obtained from experiments against the ad-hoc and rigorous models derived using a series of logistic maps. These logistic maps can be used in simulation of the fluctuations in these type of flames, without extensive computational effort or sacrifice of physical detail. Availability of accurate models of these kinds allows investigation of radiation-turbulence interactions at a more fundamental level than it was previously possible.

Characterizing the Severe Turbulence Environments Associated With Commercial Aviation Accidents: A Real-Time Turbulence Model (RTTM) Designed for the Operational Prediction of Hazardous Aviation Turbulence Environments

Science.gov (United States)

Kaplan, Michael L.; Lux, Kevin M.; Cetola, Jeffrey D.; Huffman, Allan W.; Riordan, Allen J.; Slusser, Sarah W.; Lin, Yuh-Lang; Charney, Joseph J.; Waight, Kenneth T.

2004-01-01

Real-time prediction of environments predisposed to producing moderate-severe aviation turbulence is studied. We describe the numerical model and its postprocessing system designed for said prediction of environments predisposed to severe aviation turbulence as well as presenting numerous examples of its utility. The numerical model is MASS version 5.13, which is integrated over three different grid matrices in real time on a university work station in support of NASA Langley Research Center s B-757 turbulence research flight missions. The postprocessing system includes several turbulence-related products, including four turbulence forecasting indices, winds, streamlines, turbulence kinetic energy, and Richardson numbers. Additionally, there are convective products including precipitation, cloud height, cloud mass fluxes, lifted index, and K-index. Furthermore, soundings, sounding parameters, and Froude number plots are also provided. The horizontal cross-section plot products are provided from 16 000 to 46 000 ft in 2000-ft intervals. Products are available every 3 hours at the 60- and 30-km grid interval and every 1.5 hours at the 15-km grid interval. The model is initialized from the NWS ETA analyses and integrated two times a day.

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

A turbulent time scale based k–ε model for probability density function modeling of turbulence/chemistry interactions: Application to HCCI combustion

International Nuclear Information System (INIS)

Maroteaux, Fadila; Pommier, Pierre-Lin

2013-01-01

Highlights: ► Turbulent time evolution is introduced in stochastic modeling approach. ► The particles number is optimized trough a restricted initial distribution. ► The initial distribution amplitude is modeled by magnitude of turbulence field. -- Abstract: Homogenous Charge Compression Ignition (HCCI) engine technology is known as an alternative to reduce NO x and particulate matter (PM) emissions. As shown by several experimental studies published in the literature, the ideally homogeneous mixture charge becomes stratified in composition and temperature, and turbulent mixing is found to play an important role in controlling the combustion progress. In a previous study, an IEM model (Interaction by Exchange with the Mean) has been used to describe the micromixing in a stochastic reactor model that simulates the HCCI process. The IEM model is a deterministic model, based on the principle that the scalar value approaches the mean value over the entire volume with a characteristic mixing time. In this previous model, the turbulent time scale was treated as a fixed parameter. The present study focuses on the development of a micro-mixing time model, in order to take into account the physical phenomena it stands for. For that purpose, a (k–ε) model is used to express this micro-mixing time model. The turbulence model used here is based on zero dimensional energy cascade applied during the compression and the expansion cycle; mean kinetic energy is converted to turbulent kinetic energy. Turbulent kinetic energy is converted to heat through viscous dissipation. Besides, in this study a relation to calculate the initial heterogeneities amplitude is proposed. The comparison of simulation results against experimental data shows overall satisfactory agreement at variable turbulent time scale

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...

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.

Radiation turbulence interactions in pulverized coal flames: Chaotic map models of soot fluctuations in turbulent diffusion flames. Quarterly report, October 1995--December 1995

Energy Technology Data Exchange (ETDEWEB)

McDonough, J.M.; Menguc, M.P.; Mukerji, S.; Swabb, S.; Manickavasagam, S.; Ghosal, S.

1995-12-31

In this paper, we introduce a methodology to characterize soot volume fraction fluctuations in turbulent diffusion flames via chaotic maps. The approach is based on the hypothesis that the fluctuations of properties in turbulent flames is deterministic in nature, rather than statistical. Out objective is to develop models to mimic these fluctuations. The models will be used eventually in comprehensive algorithms to study the true physics of turbulent flames and the interaction of turbulence with radiation. To this extent, we measured the time series of soot scattering coefficient in an ethylene diffusion flame from light scattering experiments. Following this, corresponding power spectra and delay maps were calculated. It was shown that if the data were averaged, the characteristics of the fluctuations were almost completely washed out. The psds from experiments were successfully modeled using a series of logistic maps.

Investigation of a turbulent spot and a tripped turbulent boundary layer flow using time-resolved tomographic PIV

NARCIS (Netherlands)

Schröder, A.; Geisler, R.; Elsinga, G.E.; Scarano, F.; Dierksheide, U.

2007-01-01

In this feasibility study the tomographic PIV technique has been applied to time resolved PIV recordings for the study of the growth of a turbulent spot in a laminar flat plate boundary layer and to visualize the topology of coherent flow structures within a tripped turbulent flat plate boundary

Characterizing and estimating noise in InSAR and InSAR time series with MODIS

Science.gov (United States)

Barnhart, William D.; Lohman, Rowena B.

2013-01-01

InSAR time series analysis is increasingly used to image subcentimeter displacement rates of the ground surface. The precision of InSAR observations is often affected by several noise sources, including spatially correlated noise from the turbulent atmosphere. Under ideal scenarios, InSAR time series techniques can substantially mitigate these effects; however, in practice the temporal distribution of InSAR acquisitions over much of the world exhibit seasonal biases, long temporal gaps, and insufficient acquisitions to confidently obtain the precisions desired for tectonic research. Here, we introduce a technique for constraining the magnitude of errors expected from atmospheric phase delays on the ground displacement rates inferred from an InSAR time series using independent observations of precipitable water vapor from MODIS. We implement a Monte Carlo error estimation technique based on multiple (100+) MODIS-based time series that sample date ranges close to the acquisitions times of the available SAR imagery. This stochastic approach allows evaluation of the significance of signals present in the final time series product, in particular their correlation with topography and seasonality. We find that topographically correlated noise in individual interferograms is not spatially stationary, even over short-spatial scales (<10 km). Overall, MODIS-inferred displacements and velocities exhibit errors of similar magnitude to the variability within an InSAR time series. We examine the MODIS-based confidence bounds in regions with a range of inferred displacement rates, and find we are capable of resolving velocities as low as 1.5 mm/yr with uncertainties increasing to ∼6 mm/yr in regions with higher topographic relief.

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

ON QUIET-TIME SOLAR WIND ELECTRON DISTRIBUTIONS IN DYNAMICAL EQUILIBRIUM WITH LANGMUIR TURBULENCE

International Nuclear Information System (INIS)

Zaheer, S.; Yoon, P. H.

2013-01-01

A recent series of papers put forth a self-consistent theory of an asymptotically steady-state electron distribution function and Langmuir turbulence intensity. The theory was developed in terms of the κ distribution which features Maxwellian low-energy electrons and a non-Maxwellian energetic power-law tail component. The present paper discusses a generalized κ distribution that features a Davydov-Druyvesteyn type of core component and an energetic power-law tail component. The physical motivation for such a generalization is so that the model may reflect the influence of low-energy electrons interacting with low-frequency kinetic Alfvénic turbulence as well as with high-frequency Langmuir turbulence. It is shown that such a solution and the accompanying Langmuir wave spectrum rigorously satisfy the balance requirement between the spontaneous and induced emission processes in both the particle and wave kinetic equations, and approximately satisfy the similar balance requirement between the spontaneous and induced scattering processes, which are nonlinear. In spite of the low velocity modification of the electron distribution function, it is shown that the resulting asymptotic velocity power-law index α, where f e ∼ v –α is close to the average index observed during the quiet-time solar wind condition, i.e., α ∼ O(6.5) whereas α average ∼ 6.69, according to observation

The deterministic chaos and random noise in turbulent jet

International Nuclear Information System (INIS)

Yao, Tian-Liang; Liu, Hai-Feng; Xu, Jian-Liang; Li, Wei-Feng

2014-01-01

A turbulent flow is usually treated as a superposition of coherent structure and incoherent turbulence. In this paper, the largest Lyapunov exponent and the random noise in the near field of round jet and plane jet are estimated with our previously proposed method of chaotic time series analysis [T. L. Yao, et al., Chaos 22, 033102 (2012)]. The results show that the largest Lyapunov exponents of the round jet and plane jet are in direct proportion to the reciprocal of the integral time scale of turbulence, which is in accordance with the results of the dimensional analysis, and the proportionality coefficients are equal. In addition, the random noise of the round jet and plane jet has the same linear relation with the Kolmogorov velocity scale of turbulence. As a result, the random noise may well be from the incoherent disturbance in turbulence, and the coherent structure in turbulence may well follow the rule of chaotic motion

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...

Time-Filtered Navier-Stokes Approach and Emulation of Turbulence-Chemistry Interaction

Science.gov (United States)

Liu, Nan-Suey; Wey, Thomas; Shih, Tsan-Hsing

2013-01-01

This paper describes the time-filtered Navier-Stokes approach capable of capturing unsteady flow structures important for turbulent mixing and an accompanying subgrid model directly accounting for the major processes in turbulence-chemistry interaction. They have been applied to the computation of two-phase turbulent combustion occurring in a single-element lean-direct-injection combustor. Some of the preliminary results from this computational effort are presented in this paper.

Transport equation for the time scale of a turbulent scalar field

International Nuclear Information System (INIS)

Kurbatskij, A.F.

1999-01-01

The two-parametric turbulence models cause serious difficulties by modeling the near-wall flows due to absence of the natural boundary condition on the wall for dissipation of the ε turbulence energy and the ε θ scalar field destruction. This difficulty may be overcome, if instead of the ε and ε θ , as the second parameter of the model, to apply the time scales of the turbulent dynamic and scalar fields. The equation of the scalar field is derived and numerical coefficients included therein, are determined from the simplest problems on the turbulent heat transfer [ru

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.

Near bed suspended sediment flux by single turbulent events

Science.gov (United States)

Amirshahi, Seyed Mohammad; Kwoll, Eva; Winter, Christian

2018-01-01

The role of small scale single turbulent events in the vertical mixing of near bed suspended sediments was explored in a shallow shelf sea environment. High frequency velocity and suspended sediment concentration (SSC; calibrated from the backscatter intensity) were collected using an Acoustic Doppler Velocimeter (ADV). Using quadrant analysis, the despiked velocity time series was divided into turbulent events and small background fluctuations. Reynolds stress and Turbulent Kinetic Energy (TKE) calculated from all velocity samples, were compared to the same turbulent statistics calculated only from velocity samples classified as turbulent events (Reevents and TKEevents). The comparison showed that Reevents and TKEevents was increased 3 and 1.6 times, respectively, when small background fluctuations were removed and that the correlation with SSC for TKE could be improved through removal of the latter. The correlation between instantaneous vertical turbulent flux (w ‧) and SSC fluctuations (SSC ‧) exhibits a tidal pattern with the maximum correlation at peak ebb and flood currents, when strong turbulent events appear. Individual turbulent events were characterized by type, strength, duration and length. Cumulative vertical turbulent sediment fluxes and average SSC associated with individual turbulent events were calculated. Over the tidal cycle, ejections and sweeps were the most dominant events, transporting 50% and 36% of the cumulative vertical turbulent event sediment flux, respectively. Although the contribution of outward interactions to the vertical turbulent event sediment flux was low (11%), single outward interaction events were capable of inducing similar SSC ‧ as sweep events. The results suggest that on time scales of tens of minutes to hours, TKE may be appropriate to quantify turbulence in sediment transport studies, but that event characteristics, particular the upward turbulent flux need to be accounted for when considering sediment transport

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

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...

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

Measurements of Turbulent Convection Speeds in Multistream Jets Using Time-Resolved PIV

Science.gov (United States)

Bridges, James; Wernet, Mark P.

2017-01-01

Convection speeds of turbulent velocities in jets, including multi-stream jets with and without flight stream, were measured using an innovative application of time-resolved particle image velocimetry. The paper describes the unique instrumentation and data analysis that allows the measurement to be made. Extensive data is shown that relates convection speed, mean velocity, and turbulent velocities for multiple jet cases. These data support the overall observation that the local turbulent convection speed is roughly that of the local mean velocity, biased by the relative intensity of turbulence.

Measurements of Turbulence Convection Speeds in Multistream Jets Using Time-Resolved PIV

Science.gov (United States)

Bridges, James; Wernet, Mark P.

2017-01-01

Convection speeds of turbulent velocities in jets, including multi-stream jets with and without flight stream, were measured using an innovative application of time-resolved particle image velocimetry. The paper describes the unique instrumentation and data analysis that allows the measurement to be made. Extensive data is shown that relates convection speed, mean velocity, and turbulent velocities for multiple jet cases. These data support the overall observation that the local turbulent convection speed is roughly that of the local mean velocity, biased by the relative intensity of turbulence.

Sensible Heat Flux Related to Variations in Atmospheric Turbulence Kinetic Energy on a Sandy Beach

Science.gov (United States)

2017-06-01

production, turbulent transport by pressure fluctuations, dissipation and flux divergence . The TKE budget as explained by Srivastava and Sarthi (2002...generation of turbulence. Term 3 is flux divergence , which describes the differential transport of TKE by turbulent eddies. Term 4, dissipation, is a sink...the time series data to align all signals to the same time base. Winds were rotated into a shore-normal frame of reference. All data outside of T

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 ...

Scaling for turbulent viscosity of buoyant plumes in stratified fluids: PIV measurement with implications for submarine hydrothermal plume turbulence

Science.gov (United States)

Zhang, Wei; He, Zhiguo; Jiang, Houshuo

2017-11-01

Time-resolved particle image velocimetry (PIV) has been used to measure instantaneous two-dimensional velocity vector fields of laboratory-generated turbulent buoyant plumes in linearly stratified saltwater over extended periods of time. From PIV-measured time-series flow data, characteristics of plume mean flow and turbulence have been quantified. To be specific, maximum plume penetration scaling and entrainment coefficient determined from the mean flow agree well with the theory based on the entrainment hypothesis for buoyant plumes in stratified fluids. Besides the well-known persistent entrainment along the plume stem (i.e., the 'plume-stem' entrainment), the mean plume velocity field shows persistent entrainment along the outer edge of the plume cap (i.e., the 'plume-cap' entrainment), thereby confirming predictions from previous numerical simulation studies. To our knowledge, the present PIV investigation provides the first measured flow field data in the plume cap region. As to measured plume turbulence, both the turbulent kinetic energy field and the turbulence dissipation rate field attain their maximum close to the source, while the turbulent viscosity field reaches its maximum within the plume cap region; the results also show that maximum turbulent viscosity scales as νt,max = 0.030(B/N)1/2, where B is source buoyancy flux and N is ambient buoyancy frequency. These PIV data combined with previously published numerical simulation results have implications for understanding the roles of hydrothermal plume turbulence, i.e. plume turbulence within the cap region causes the 'plume-cap' entrainment that plays an equally important role as the 'plume-stem' entrainment in supplying the final volume flux at the plume spreading level.

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 ...

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…

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...

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.

Bed slope effects on turbulent wave boundary layers: 1. Model validation and quantification of rough-turbulent results

DEFF Research Database (Denmark)

Fuhrman, David R.; Fredsøe, Jørgen; Sumer, B. Mutlu

2009-01-01

measurements for steady streaming induced by a skewed free stream velocity signal is also provided. We then simulate a series of experiments involving oscillatory flow in a convergent-divergent smooth tunnel, and a good match with respect to bed shear stresses and streaming velocities is achieved......A numerical model solving incompressible Reynolds-averaged Navier-Stokes equations, combined with a two-equation k-omega turbulence closure, is used to study converging-diverging effects from a sloping bed on turbulent (oscillatory) wave boundary layers. Bed shear stresses from the numerical model....... The streaming is conceptually explained using analogies from steady converging and diffuser flows. A parametric study is undertaken to assess both the peak and time-averaged bed shear stresses in converging and diverging half periods under rough-turbulent conditions. The results are presented as friction factor...

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 ...

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.

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.

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.

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.

Turbulence-induced persistence in laser beam wandering.

Science.gov (United States)

Zunino, Luciano; Gulich, Damián; Funes, Gustavo; Pérez, Darío G

2015-07-01

We have experimentally confirmed the presence of long-memory correlations in the wandering of a thin Gaussian laser beam over a screen after propagating through a turbulent medium. A laboratory-controlled experiment was conducted in which coordinate fluctuations of the laser beam were recorded at a sufficiently high sampling rate for a wide range of turbulent conditions. Horizontal and vertical displacements of the laser beam centroid were subsequently analyzed by implementing detrended fluctuation analysis. This is a very well-known and widely used methodology to unveil memory effects from time series. Results obtained from this experimental analysis allow us to confirm that both coordinates behave as highly persistent signals for strong turbulent intensities. This finding is relevant for a better comprehension and modeling of the turbulence effects in free-space optical communication systems and other applications related to propagation of optical signals in the atmosphere.

Turbulent wind at the equatorial segment of an operating Darrieus wind turbine blade

Science.gov (United States)

Connell, J. R.; Morris, V. R.

1989-09-01

Six turbulent wind time series, measured at equally spaced equator-height locations on a circle 3 m outside a 34-m Darrieus rotor, are analyzed to approximate the wind fluctuations experienced by the rotor. The flatwise lower root-bending stress of one blade was concurrently recorded. The wind data are analyzed in three ways: wind components that are radial and tangential to the rotation of a blade were rotationally sampled; induction and wake effects of the rotor were estimated from the six Eulerian time series; and turbulence spectra of both the measured wind and the modeled wind from the PNL theory of rotationally sampled turbulence. The wind and the rotor response are related by computing the spectral response function of the flatwise lower root-bending stress. Two bands of resonant response that surround the first and second flatwise modal frequencies shift with the rotor rotation rate.

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.

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

Time change and universality in turbulence

DEFF Research Database (Denmark)

Barndorff-Nielsen, Ole Eiler; Schmiegel, Jürgen

of the probability densities of turbulent velocity increments. Furthermore, the application of a time change in terms of the scale parameter δ of the normal inverse Gaussian distribution results in a collapse of the densities of velocity increments onto Reynolds number independent distributions. We discuss this kind...... experiment. Taylor Reynolds numbers range from Rλ = 80 for the wind tunnel experiment up to Rλ = 17000 for the atmospheric boundary layer experiment. Empirical findings strongly support the appropriateness of normal inverse Gaussian distributions for a parsimonious and universal description...

Kinetic features of interchange turbulence

International Nuclear Information System (INIS)

Sarazin, Y; Grandgirard, V; Fleurence, E; Garbet, X; Ghendrih, Ph; Bertrand, P; Depret, G

2005-01-01

Non-linear gyrokinetic simulations of the interchange instability are discussed. The semi-Lagrangian numerical scheme allows one to address two critical points achieved with simulations lasting several confinement times: an accurate statistical analysis of the fluctuations and the back reaction of the turbulence on equilibrium profiles. Zonal flows are found to quench a 2D + 1D interchange turbulence when one of the species has a vanishing response to zonal modes. Conversely, when streamers dominate, the equilibrium profiles are found to be stiff. In the non-linear regime and steady-state turbulence, the distribution function exhibits a significant departure from a Maxwellian distribution. This property is characterized by an expansion on generalized Laguerre functions with a slow decay of the series of moments. This justifies the use of gyrokinetic simulations since a standard fluid approach, based on a limited number of moments, would certainly require a complex closure so as to take into account the impact of these non-vanishing high order moments

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.

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

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...

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.

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.

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

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...

Time dependent plasma viscosity and relation between neoclassical transport and turbulent transport

International Nuclear Information System (INIS)

Shaing, K.C.

2005-01-01

Time dependent plasma viscosities for asymmetric toroidal plasmas in various collisionality regimes are calculated. It is known that in the symmetric limit the time dependent plasma viscosities accurately describe plasma flow damping rate. Thus, time dependent plasma viscosities are important in modeling the radial electric field of the zonal flow. From the momentum balance equation, it is shown that, at the steady state, the balance of the viscosity force and the momentum source determines the radial electric field of the zonal flow. Thus, for a fixed source, the smaller the viscous force is, the larger the value of the radial electric field is, which in turn suppresses the turbulence fluctuations more and improves turbulence transport. However, the smaller the viscous force also implies the smaller the neoclassical transport fluxes based on the neoclassical flux-force relationship. We thus show that when neoclassical transport fluxes are improved so are the turbulent fluxes in toroidal plasmas. (author)

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.

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

Nonlinear time-series analysis of current signal in cathodic contact glow discharge electrolysis

International Nuclear Information System (INIS)

Allagui, Anis; Abdelkareem, Mohammad Ali; Rojas, Andrea Espinel; Bonny, Talal; Elwakil, Ahmed S.

2016-01-01

In the standard two-electrode configuration employed in electrolytic process, when the control dc voltage is brought to a critical value, the system undergoes a transition from conventional electrolysis to contact glow discharge electrolysis (CGDE), which has also been referred to as liquid-submerged micro-plasma, glow discharge plasma electrolysis, electrode effect, electrolytic plasma, etc. The light-emitting process is associated with the development of an irregular and erratic current time-series which has been arbitrarily labelled as “random,” and thus dissuaded further research in this direction. Here, we examine the current time-series signals measured in cathodic CGDE configuration in a concentrated KOH solution at different dc bias voltages greater than the critical voltage. We show that the signals are, in fact, not random according to the NIST SP. 800-22 test suite definition. We also demonstrate that post-processing low-pass filtered sequences requires less time than the native as-measured sequences, suggesting a superposition of low frequency chaotic fluctuations and high frequency behaviors (which may be produced by more than one possible source of entropy). Using an array of nonlinear time-series analyses for dynamical systems, i.e., the computation of largest Lyapunov exponents and correlation dimensions, and re-construction of phase portraits, we found that low-pass filtered datasets undergo a transition from quasi-periodic to chaotic to quasi-hyper-chaotic behavior, and back again to chaos when the voltage controlling-parameter is increased. The high frequency part of the signals is discussed in terms of highly nonlinear turbulent motion developed around the working electrode.

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.

Stirring turbulence with turbulence

NARCIS (Netherlands)

Cekli, H.E.; Joosten, R.; van de Water, W.

2015-01-01

We stir wind-tunnel turbulence with an active grid that consists of rods with attached vanes. The time-varying angle of these rods is controlled by random numbers. We study the response of turbulence on the statistical properties of these random numbers. The random numbers are generated by the

Propagation of Porro "petal" beams through a turbulent atmosphere

CSIR Research Space (South Africa)

Burger, L

2009-07-01

Full Text Available . Construct a series of pseudo–random phase screens from the basis. 3. Implement optical wavefront changes from the pseudo–random phase screens. 4. Propagate the resulting beam to the far field and measure …. Page 11 Phase screen construction 20 40 60 80... constant h is height asl k is the wave number Atmospheric propagation Kolmogorov Turbulence Model Page 10 Atmospheric propagation How to measure turbulence 1. Decompose the turbulence model into a series of orthogonal functions (basis set). 2...

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.

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

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.

Strange attractors in weakly turbulent Couette-Taylor flow

Science.gov (United States)

Brandstater, A.; Swinney, Harry L.

1987-01-01

An experiment is conducted on the transition from quasi-periodic to weakly turbulent flow of a fluid contained between concentric cylinders with the inner cylinder rotating and the outer cylinder at rest. Power spectra, phase-space portraits, and circle maps obtained from velocity time-series data indicate that the nonperiodic behavior observed is deterministic, that is, it is described by strange attractors. Various problems that arise in computing the dimension of strange attractors constructed from experimental data are discussed and it is shown that these problems impose severe requirements on the quantity and accuracy of data necessary for determining dimensions greater than about 5. In the present experiment the attractor dimension increases from 2 at the onset of turbulence to about 4 at a Reynolds number 50-percent above the onset of turbulence.

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.

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)""…

The nature of turbulence in a triangular lattice gas automaton

Science.gov (United States)

Duong-Van, Minh; Feit, M. D.; Keller, P.; Pound, M.

1986-12-01

Power spectra calculated from the coarse-graining of a simple lattice gas automaton, and those of time averaging other stochastic times series that we have investigated, have exponents in the range -1.6 to -2, consistent with observation of fully developed turbulence. This power spectrum is a natural consequence of coarse-graining; the exponent -2 represents the continuum limit.

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.

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.

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....

Structure function scaling in a Reλ = 250 turbulent mixing layer

KAUST Repository

Attili, Antonio

2011-12-22

A highly resolved Direct Numerical Simulation of a spatially developing turbulent mixing layer is presented. In the fully developed region, the flow achieves a turbulent Reynolds number Reλ = 250, high enough for a clear separation between large and dissipative scales, so for the presence of an inertial range. Structure functions have been calculated in the self-similar region using velocity time series and Taylor\\'s frozen turbulence hypothesis. The Extended Self-Similarity (ESS) concept has been employed to evaluate relative scaling exponents. A wide range of scales with scaling exponents and intermittency levels equal to homogeneous isotropic turbulence has been identified. Moreover an additional scaling range exists for larger scales; it is characterized by smaller exponents, similar to the values reported in the literature for flows with strong shear.

Structure function scaling in a Reλ = 250 turbulent mixing layer

KAUST Repository

Attili, Antonio; Bisetti, Fabrizio

2011-01-01

A highly resolved Direct Numerical Simulation of a spatially developing turbulent mixing layer is presented. In the fully developed region, the flow achieves a turbulent Reynolds number Reλ = 250, high enough for a clear separation between large and dissipative scales, so for the presence of an inertial range. Structure functions have been calculated in the self-similar region using velocity time series and Taylor's frozen turbulence hypothesis. The Extended Self-Similarity (ESS) concept has been employed to evaluate relative scaling exponents. A wide range of scales with scaling exponents and intermittency levels equal to homogeneous isotropic turbulence has been identified. Moreover an additional scaling range exists for larger scales; it is characterized by smaller exponents, similar to the values reported in the literature for flows with strong shear.

Statistical description of turbulent transport for flux driven toroidal plasmas

Science.gov (United States)

Anderson, J.; Imadera, K.; Kishimoto, Y.; Li, J. Q.; Nordman, H.

2017-06-01

A novel methodology to analyze non-Gaussian probability distribution functions (PDFs) of intermittent turbulent transport in global full-f gyrokinetic simulations is presented. In this work, the auto-regressive integrated moving average (ARIMA) model is applied to time series data of intermittent turbulent heat transport to separate noise and oscillatory trends, allowing for the extraction of non-Gaussian features of the PDFs. It was shown that non-Gaussian tails of the PDFs from first principles based gyrokinetic simulations agree with an analytical estimation based on a two fluid model.

Inverse scattering problem in turbulent magnetic fluctuations

Directory of Open Access Journals (Sweden)

R. A. Treumann

2016-08-01

Full Text Available We apply a particular form of the inverse scattering theory to turbulent magnetic fluctuations in a plasma. In the present note we develop the theory, formulate the magnetic fluctuation problem in terms of its electrodynamic turbulent response function, and reduce it to the solution of a special form of the famous Gelfand–Levitan–Marchenko equation of quantum mechanical scattering theory. The last of these applies to transmission and reflection in an active medium. The theory of turbulent magnetic fluctuations does not refer to such quantities. It requires a somewhat different formulation. We reduce the theory to the measurement of the low-frequency electromagnetic fluctuation spectrum, which is not the turbulent spectral energy density. The inverse theory in this form enables obtaining information about the turbulent response function of the medium. The dynamic causes of the electromagnetic fluctuations are implicit to it. Thus, it is of vital interest in low-frequency magnetic turbulence. The theory is developed until presentation of the equations in applicable form to observations of turbulent electromagnetic fluctuations as input from measurements. Solution of the final integral equation should be done by standard numerical methods based on iteration. We point to the possibility of treating power law fluctuation spectra as an example. Formulation of the problem to include observations of spectral power densities in turbulence is not attempted. This leads to severe mathematical problems and requires a reformulation of inverse scattering theory. One particular aspect of the present inverse theory of turbulent fluctuations is that its structure naturally leads to spatial information which is obtained from the temporal information that is inherent to the observation of time series. The Taylor assumption is not needed here. This is a consequence of Maxwell's equations, which couple space and time evolution. The inversion procedure takes

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.

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.

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.

Turbulent Statistics From Time-Resolved PIV Measurements of a Jet Using Empirical Mode Decomposition

Science.gov (United States)

Dahl, Milo D.

2013-01-01

Empirical mode decomposition is an adaptive signal processing method that when applied to a broadband signal, such as that generated by turbulence, acts as a set of band-pass filters. This process was applied to data from time-resolved, particle image velocimetry measurements of subsonic jets prior to computing the second-order, two-point, space-time correlations from which turbulent phase velocities and length and time scales could be determined. The application of this method to large sets of simultaneous time histories is new. In this initial study, the results are relevant to acoustic analogy source models for jet noise prediction. The high frequency portion of the results could provide the turbulent values for subgrid scale models for noise that is missed in large-eddy simulations. The results are also used to infer that the cross-correlations between different components of the decomposed signals at two points in space, neglected in this initial study, are important.

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.

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…

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...

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...

Turbulence

CERN Document Server

Bailly, Christophe

2015-01-01

This book covers the major problems of turbulence and turbulent processes, including  physical phenomena, their modeling and their simulation. After a general introduction in Chapter 1 illustrating many aspects dealing with turbulent flows, averaged equations and kinetic energy budgets are provided in Chapter 2. The concept of turbulent viscosity as a closure of the Reynolds stress is also introduced. Wall-bounded flows are presented in Chapter 3, and aspects specific to boundary layers and channel or pipe flows are also pointed out. Free shear flows, namely free jets and wakes, are considered in Chapter 4. Chapter 5 deals with vortex dynamics. Homogeneous turbulence, isotropy, and dynamics of isotropic turbulence are presented in Chapters 6 and 7. Turbulence is then described both in the physical space and in the wave number space. Time dependent numerical simulations are presented in Chapter 8, where an introduction to large eddy simulation is offered. The last three chapters of the book summarize remarka...

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

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

Experimental studies of occupation times in turbulent flows

DEFF Research Database (Denmark)

Mann, J.; Ott, Søren; Pécseli, H.L.

2003-01-01

The motion of passively convected particles in turbulent flows is studied experimentally in approximately homogeneous and isotropic turbulent flows, generated in water by two moving grids. The simultaneous trajectories of many small passively convected, neutrally buoyant, polystyrene particles...

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...

Two-scale analysis of intermittency in fully developed turbulence

Energy Technology Data Exchange (ETDEWEB)

Badii, R; Talkner, P [Paul Scherrer Inst. (PSI), Villigen (Switzerland)

1999-08-01

A self-affinity test for turbulent time series is applied to experimental data for the estimation of intermittency exponents. The method employs exact relations satisfied by joint expectations of observables computed across two different length scales. One of these constitutes a verification tool for the existence and the extent of the inertial range. (author) 2 figs., 13 refs.

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

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...

Constantin Carathéodory mathematics and politics in turbulent times

CERN Document Server

Georgiadou, Maria

2004-01-01

Constantin Carathéodory - Mathematics and Politics in Turbulent Times is the biography of a mathematician, born in Berlin in 1873, who became famous during his life time, but has hitherto been ignored by historians for half a century since his death in 1950, in Munich. In a thought-provoking approach, Maria Georgiadou devotes to Constantin Carathéodory all the attention such a personality deserves. With breathtaking detail and the appropriate scrutiny she elucidates his oeuvre, life and turbulent political and historical surroundings. A descendant of the the Greek élite of Constantinople, Carathéodory graduated from the military school of Brussels, became engineer at the Assiout dam in Egypt and finally dedicated a life of effort to mathematics and education. He studied and embarked on an international academic career, haunted by wars, catastrophes and personal tragedies. Over the last years of his life, he stayed in Munich despite World War II, an ambiguous decision upon which the author sheds unprecede...

PROTOSTELLAR OUTFLOW EVOLUTION IN TURBULENT ENVIRONMENTS

International Nuclear Information System (INIS)

Cunningham, Andrew J.; Frank, Adam; Carroll, Jonathan; Blackman, Eric G.; Quillen, Alice C.

2009-01-01

The link between turbulence in star-forming environments and protostellar jets remains controversial. To explore issues of turbulence and fossil cavities driven by young stellar outflows, we present a series of numerical simulations tracking the evolution of transient protostellar jets driven into a turbulent medium. Our simulations show both the effect of turbulence on outflow structures and, conversely, the effect of outflows on the ambient turbulence. We demonstrate how turbulence will lead to strong modifications in jet morphology. More importantly, we demonstrate that individual transient outflows have the capacity to re-energize decaying turbulence. Our simulations support a scenario in which the directed energy/momentum associated with cavities is randomized as the cavities are disrupted by dynamical instabilities seeded by the ambient turbulence. Consideration of the energy power spectra of the simulations reveals that the disruption of the cavities powers an energy cascade consistent with Burgers'-type turbulence and produces a driving scale length associated with the cavity propagation length. We conclude that fossil cavities interacting either with a turbulent medium or with other cavities have the capacity to sustain or create turbulent flows in star-forming environments. In the last section, we contrast our work and its conclusions with previous studies which claim that jets cannot be the source of turbulence.

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.

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...

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).

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...

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.

Describing temporal variability of the mean Estonian precipitation series in climate time scale

Science.gov (United States)

Post, P.; Kärner, O.

2009-04-01

,1,1) model can be interpreted to be consisting of random walk in a noisy environment (Box and Jenkins, 1976). The fitted model appears to be weakly non-stationary, that gives us the possibility to use stationary approximation if only the noise component from that sum of white noise and random walk is exploited. We get a convenient routine to generate a stationary precipitation climatology with a reasonable accuracy, since the noise component variance is much larger than the dispersion of the random walk generator. This interpretation emphasizes dominating role of a random component in the precipitation series. The result is understandable due to a small territory of Estonia that is situated in the mid-latitude cyclone track. References Box, J.E.P. and G. Jenkins 1976: Time Series Analysis, Forecasting and Control (revised edn.), Holden Day San Francisco, CA, 575 pp. Davis, A., Marshak, A., Wiscombe, W. and R. Cahalan 1996: Multifractal characterizations of intermittency in nonstationary geophysical signals and fields.in G. Trevino et al. (eds) Current Topics in Nonsstationarity Analysis. World-Scientific, Singapore, 97-158. Kärner, O. 2002: On nonstationarity and antipersistency in global temperature series. J. Geophys. Res. D107; doi:10.1029/2001JD002024. Kärner, O. 2005: Some examples on negative feedback in the Earth climate system. Centr. European J. Phys. 3; 190-208. Monin, A.S. and A.M. Yaglom 1975: Statistical Fluid Mechanics, Vol 2. Mechanics of Turbulence , MIT Press Boston Mass, 886 pp.

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.

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

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...

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...

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 ...

Influence of turbulence on bed load sediment transport

DEFF Research Database (Denmark)

Sumer, B. Mutlu; Chua, L.; Cheng, N. S.

2003-01-01

This paper summarizes the results of an experimental study on the influence of an external turbulence field on the bedload sediment transport in an open channel. The external turbulence was generated by: (1) with a horizontal pipe placed halfway through the depth, h; (2) with a series of grids......-bed experiments and the ripple-covered-bed experiments. In the former case, the flow in the presence of the turbulence generator was adjusted so that the mean bed shear stress was the same as in the case without the turbulence generator in order to single out the effect of the external turbulence on the sediment...... correlated with the sediment transport rate. The sediment transport increases markedly with increasing turbulence level....

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...

Coastal Microstructure: From Active Overturn to Fossil Turbulence

Science.gov (United States)

Tau Leung, Pak

2011-11-01

The Remote Anthropogenic Sensing Program was a five year effort (2001- 2005) to examine subsurface phenomena related to a sewage outfall off the coast of Oahu, Hawaii. This research has implications for basic ocean hydrodynamics, particularly for a greatly improved understanding of the evolution of turbulent patches. It was the first time a microstructure measurement was used to study such a buoyancy-driven turbulence generated by a sea-floor diffuser. In 2004, two stations were selected to represent the near field and ambient conditions. They have nearly identical bathymetrical and hydrographical features and provide an ideal environment for a control experiment. Repeated vertical microstructure measurements were performed at both stations for 20 days. A time series of physical parameters was collected and used for statistical analysis. After comparing the data from both stations, it can be concluded that the turbulent mixing generated by the diffuser contributes to the elevated dissipation rate observed in the pycnocline and bottom boundary layer. To further understand the mixing processes in both regions, data were plotted on a Hydrodynamic Phase Diagram. The overturning stages of the turbulent patches are identified by Hydrodynamic Phase Diagram. This technique provides detailed information on the evolution of the turbulent patches from active overturns to fossilized scalar microstructures in the water column. Results from this study offer new evidence to support the fossil turbulence theory. This study concluded that: 1. Field Data collected near a sea-floor outfall diffuser show that turbulent patches evolve from active (overturning) to fossil (buoyancy-inhibited) stages, consistent with the process of turbulent patch evolution proposed by fossil turbulence theory. 2. The data show that active (overturning) and fossil (buoyancy-inhibited) patches have smaller length scales than the active+fossil (intermediate) stage of patch evolution, consistent with fossil

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.

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...

Turbulence modulation induced by interaction between a bubble swarm and decaying turbulence in oscillating-grid turbulence

International Nuclear Information System (INIS)

Imaizumi, Ryota; Morikawa, Koichi; Higuchi, Masamori; Saito, Takayuki

2009-01-01

In this study, the interaction between a bubble swarm and homogeneous isotropic turbulence was experimentally investigated. The objective is to clarify the turbulence modulation induced by interaction between the bubble swarm and the homogeneous isotropic turbulence without mean flow. In order to generate simultaneously ideally homogeneous isotropic turbulence and a sufficiently controlled bubble swarm, we employed both oscillating grid and bubble generators equipped with audio speakers. First, the homogeneous isotropic turbulence was formed by operating the oscillating grid cylindrical acrylic pipe (height: 600 mm, inner diameter: 149 mm) filled with ion-exchanged and degassed water. Second, we stopped the oscillating-grid in arbitrary time after the homogeneous isotropic turbulence was achieved. A few moments later, the controlled bubble swarm (number of bubbles: 3, average equivalent diameter of bubble: 3 mm, bubble Reynolds number: 859, Weber number: 3.48) was launched into the decaying turbulence described above, using the bubble generators. The bubble formation, bubble size and bubble-launch timing are controlled arbitrarily and precisely by this device. In this study, we conducted the following experiments: 1) measurement of the motion of bubbles in rest water and oscillating grid turbulence via high-speed visualization, 2) measurement of the liquid phase motion around the bubbles in rest water via PIV system with LIF method, 3) measurement of the liquid phase motion around the bubbles in oscillating-grid turbulence via PIV system with LIF method. In the vitalization of the liquid-phase motion of both experiments, two high speed video cameras were employed in order to simultaneously film large- and small-scale interrogation areas. The liquid-phase ambient turbulence hastened the change of the bubble motion from zigzag mode to spiral mode. The interaction between the bubble swarm and liquid-phase turbulence increased decay-rate of the turbulence. (author)

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.

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. .

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...

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...

Time series analysis of continuous-wave coherent Doppler Lidar wind measurements

International Nuclear Information System (INIS)

Sjoeholm, M; Mikkelsen, T; Mann, J; Enevoldsen, K; Courtney, M

2008-01-01

The influence of spatial volume averaging of a focused 1.55 μm continuous-wave coherent Doppler Lidar on observed wind turbulence measured in the atmospheric surface layer over homogeneous terrain is described and analysed. Comparison of Lidar-measured turbulent spectra with spectra simultaneously obtained from a mast-mounted sonic anemometer at 78 meters height at the test station for large wind turbines at Hoevsoere in Western Jutland, Denmark is presented for the first time

Fractal-Markovian scaling of turbulent bursting process in open channel flow

International Nuclear Information System (INIS)

Keshavarzi, Ali Reza; Ziaei, Ali Naghi; Homayoun, Emdad; Shirvani, Amin

2005-01-01

The turbulent coherent structure of flow in open channel is a chaotic and stochastic process in nature. The coherence structure of the flow or bursting process consists of a series of eddies with a variety of different length scales and it is very important for the entrainment of sediment particles from the bed. In this study, a fractal-Markovian process is applied to the measured turbulent data in open channel. The turbulent data was measured in an experimental flume using three-dimensional acoustic Doppler velocity meter (ADV). A fractal interpolation function (FIF) algorithm was used to simulate more than 500,000 time series data of measured instantaneous velocity fluctuations and Reynolds shear stress. The fractal interpolation functions (FIF) enables to simulate and construct time series of u', v', and u'v' for any particular movement and state in the Markov process. The fractal dimension of the bursting events is calculated for 16 particular movements with the transition probability of the events based on 1st order Markov process. It was found that the average fractal dimensions of the streamwise flow velocity (u') are; 1.73, 1.74, 1.71 and 1.74 with the transition probability of 60.82%, 63.77%, 59.23% and 62.09% for the 1-1, 2-2, 3-3 and 4-4 movements, respectively. It was also found that the fractal dimensions of Reynold stress u'v' for quadrants 1, 2, 3 and 4 are 1.623, 1.623, 1.625 and 1.618, respectively

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.

Reynolds number dependency in equilibrium two-dimensional turbulence

Science.gov (United States)

Bracco, A.; McWilliams, J.

2009-04-01

We use the Navier-Stokes equations for barotropic turbulence as a zero-order approximation of chaotic space-time patterns and equilibrium distributions that mimic turbulence in geophysical flows. In this overly-simplified set-up for which smooth-solutions exist, we investigate if is possible to bound the uncertainty associated with the numerical domain discretization, i.e. with the limitation imposed by the Reynolds number range we can explore. To do so we analyze a series of stationary barotropic turbulence simulations spanning a large range of Reynolds numbers and run over a three year period for over 300,000 CPU hours. We find a persistent Reynolds number dependency in the energy power spectra and second order vorticity structure function, while distributions of dynamical quantities such as velocity, vorticity, dissipation rates and others are invariant in shape and have variances scaling with the viscosity coefficient according to simple power-laws. The relevance to this work to the possibility of conceptually reducing uncertainties in climate models will be discussed.

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.

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.

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.

Application of simulated lidar scanning patterns to constrained Gaussian turbulence fields for load validation

DEFF Research Database (Denmark)

Dimitrov, Nikolay Krasimirov; Natarajan, Anand

2017-01-01

We demonstrate a method for incorporating wind velocity measurements from multiple-point scanning lidars into threedimensional wind turbulence time series serving as input to wind turbine load simulations. Simulated lidar scanning patterns are implemented by imposing constraints on randomly gener...

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...

Time behaviours of visible lines in turbulently heated TRIAM-1 plasma

Energy Technology Data Exchange (ETDEWEB)

Hiraki, N; Nakamura, K; Nakamura, Y; Itoh, S [Kyushu Univ., Fukuoka (Japan). Research Inst. for Applied Mechanics

1981-08-01

Spectroscopic studies were carried out on turbulently heated TRIAM-1 tokamak plasma. The temporal evolutions of the line radiance of visible lines were measured and two types of time behaviours of the line radiance were identified. The observed remarkable reduction of the line radiance of visible lines which have low ionization potential and are localized in the skin-layer due to the application of a pulsed electric-field for turbulent heating is attributed to the strong plasma heating in the peripherical region. Spatial profiles of neutrals and ions which are related to these lines are calculated, and the temporal variations of these profiles caused by the application of the heating pulse are discussed.

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.

Applicability of Taylor's hypothesis in thermally driven turbulence

Science.gov (United States)

Kumar, Abhishek; Verma, Mahendra K.

2018-04-01

In this paper, we show that, in the presence of large-scale circulation (LSC), Taylor's hypothesis can be invoked to deduce the energy spectrum in thermal convection using real-space probes, a popular experimental tool. We perform numerical simulation of turbulent convection in a cube and observe that the velocity field follows Kolmogorov's spectrum (k-5/3). We also record the velocity time series using real-space probes near the lateral walls. The corresponding frequency spectrum exhibits Kolmogorov's spectrum (f-5/3), thus validating Taylor's hypothesis with the steady LSC playing the role of a mean velocity field. The aforementioned findings based on real-space probes provide valuable inputs for experimental measurements used for studying the spectrum of convective turbulence.

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.

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.

Constrained dynamics of an inertial particle in a turbulent flow

International Nuclear Information System (INIS)

Obligado, M; Baudet, C; Gagne, Y; Bourgoin, M

2011-01-01

Most of theoretical and numerical works for free advected particles in a turbulent flow, which only consider the drag force acting on the particles, fails to predict recent experimental results for the transport of finite size particles. These questions have motivated a series of experiments trying to emphasize the actual role of the drag force by imposing this one as an unambiguous leading forcing term acting on a particle in a turbulent background. This is achieved by considering the constrained dynamics of towed particles in a turbulent environment. In the present work, we focus on the influence of particles inertia on its velocity and acceleration Lagrangian statistics and energy spectral density. Our results are consistent with a filtering scenario resulting from the viscous response time of an inertial particle whose dynamics is coupled to the surrounding fluid via strong contribution of drag.

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

Time Resolved Scanning PIV measurements at fine scales in a turbulent jet

International Nuclear Information System (INIS)

Cheng, Y.; Torregrosa, M.M.; Villegas, A.; Diez, F.J.

2011-01-01

The temporal and spatial complexity of turbulent flows at intermediate and small scales has prevented the acquisition of full three-dimensional experimental data sets for validating classical turbulent theory and Direct Numerical Simulations (DNS). Experimental techniques like Particle Velocimetry, PIV, allow non-intrusive planar measurements of turbulent flows. The present work applied a Time Resolved Scanning PIV system, TRS-PIV, capable of obtaining three-dimensional two-component velocities to measure the small scales of a turbulent jet. When probing the small scales of these flows with PIV, the uncertainty of the measured turbulent properties are determined by the characteristics of the PIV system and specially the thickness of the laser sheet. A measurement of the particle distribution across the thickness of the laser sheet is proposed as a more detailed description of the PIV sheet thickness. The high temporal and spatial resolution of the TRS-PIV system allowed obtaining quasi-instantaneous volumetric vector fields at the far field of a round turbulent jet in water, albeit for a low Reynolds number of 1478 due to the speed limitations of the present camera and scanning system. Six of the nine components of the velocity gradient tensor were calculated from the velocity measurements. This allowed the visualization with near Kolmogorov-scale resolution of the velocity gradient structures in three-dimensional space. In general, these structures had a complex geometry corresponding to elongated shapes in the form of sheets and tubes. An analysis of the probability density function, pdf, of the velocity gradients calculated showed that the on-diagonal (off-diagonal) velocity gradient components were very similar to each other even for events at the tails of the pdfs, as required for homogeneous isotropy. The root mean square of the components of the velocity gradients is also calculated and their ratio of off-diagonal components to on-diagonal components

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.

Eulerian short-time statistics of turbulent flow at large Reynolds number

NARCIS (Netherlands)

Brouwers, J.J.H.

2004-01-01

An asymptotic analysis is presented of the short-time behavior of second-order temporal velocity structure functions and Eulerian acceleration correlations in a frame that moves with the local mean velocity of the turbulent flow field. Expressions in closed-form are derived which cover the viscous

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

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

Study of the fractal dimension of the wind and its relationships with turbulent and stability parameters

Science.gov (United States)

Tijera, Manuel; Maqueda, Gregorio; Cano, José L.; López, Pilar; Yagüe, Carlos

2010-05-01

The wind velocity series of the atmospheric turbulent flow in the planetary boundary layer (PBL), in spite of being highly erratic, present a self-similarity structure (Frisch, 1995; Peitgen et., 2004; Falkovich et., 2006). So, the wind velocity can be seen as a fractal magnitude. We calculate the fractal dimension (Komolgorov capacity or box-counting dimension) of the wind perturbation series (u' = u- ) in the physical spaces (namely velocity-time). It has been studied the time evolution of the fractal dimension along different days and at three levels above the ground (5.8 m, 13.5 m, 32 m). The data analysed was recorded in the experimental campaign SABLES-98 (Cuxart et al., 2000) at the Research Centre for the Lower Atmosphere (CIBA) located in Valladolid (Spain). In this work the u, v and w components of wind velocity series have been measured by sonic anemometers (20 Hz sampling rate). The fractal dimension versus the integral length scales of the mean wind series have been studied, as well as the influence of different turbulent parameters. A method for estimating these integral scales is developed using the normalized autocorrelation function and a Gaussian fit. Finally, it will be analysed the variation of the fractal dimension versus stability parameters (as Richardson number) in order to explain some of the dominant features which are likely immersed in the fractal nature of these turbulent flows. References - Cuxart J, Yagüe C, Morales G, Terradellas E, Orbe J, Calvo J, Fernández A, Soler MR, Infante C, Buenestado P, Espinalt A, Joergensen HE, Rees JM, Vilá J, Redondo JM, Cantalapiedra IR and Conangla L (2000) Stable atmospheric boundary-layer experiment in Spain (SABLES98): a report. Boundary- Layer Meteorol 96:337-370 - Falkovich G and Kattepalli R. Sreenivasan (2006) Lessons from Hidrodynamic Turbulence. Physics Today 59: 43-49 - Frisch U (1995) Turbulence the legacy of A.N. Kolmogorov Cambridge University Press 269pp - Peitgen H, Jürgens H and

Progress in turbulence research

International Nuclear Information System (INIS)

Bradshaw, P.

1990-01-01

Recent developments in experiments and eddy simulations, as an introduction to a discussion of turbulence modeling for engineers is reviewed. The most important advances in the last decade rely on computers: microcomputers to control laboratory experiments, especially for multidimensional imaging, and supercomputers to simulate turbulence. These basic studies in turbulence research are leading to genuine breakthroughs in prediction methods for engineers and earth scientists. The three main branches of turbulence research: experiments, simulations (numerically-accurate three-dimensional, time-dependent solutions of the Navier-Stokes equations, with any empiricism confined to the smallest eddies), and modeling (empirical closure of time-averaged equations for turbulent flow) are discussed. 33 refs

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.

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.

Experimental study of a DMD based compressive line sensing imaging system in the turbulence environment

Science.gov (United States)

Ouyang, Bing; Hou, Weilin; Gong, Cuiling; Caimi, Frank M.; Dalgleish, Fraser R.; Vuorenkoski, Anni K.

2016-05-01

The Compressive Line Sensing (CLS) active imaging system has been demonstrated to be effective in scattering mediums, such as turbid coastal water through simulations and test tank experiments. Since turbulence is encountered in many atmospheric and underwater surveillance applications, a new CLS imaging prototype was developed to investigate the effectiveness of the CLS concept in a turbulence environment. Compared with earlier optical bench top prototype, the new system is significantly more robust and compact. A series of experiments were conducted at the Naval Research Lab's optical turbulence test facility with the imaging path subjected to various turbulence intensities. In addition to validating the system design, we obtained some unexpected exciting results - in the strong turbulence environment, the time-averaged measurements using the new CLS imaging prototype improved both SNR and resolution of the reconstructed images. We will discuss the implications of the new findings, the challenges of acquiring data through strong turbulence environment, and future enhancements.

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.

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.

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

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.

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.

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.

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...

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...

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.

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...

Investigation of coolant mixing in WWER-440/213 RPV with improved turbulence model

International Nuclear Information System (INIS)

Kiss, B.; Aszodi, A.

2011-01-01

A detailed and complex RPV model of WWER-440/213 type reactor was developed in Budapest University of Technology and Economics Institute of Nuclear Techniques in the previous years. This model contains the main structural elements as inlet and outlet nozzles, guide baffles of hydro-accumulators coolant, alignment drifts, perforated plates, brake- and guide tube chamber and simplified core. With the new vessel model a series of parameter studies were performed considering turbulence models, discretization schemes, and modeling methods with ANSYS CFX. In the course of parameter studies the coolant mixing was investigated in the RPV. The coolant flow was 'traced' with different scalar concentration at the inlet nozzles and its distribution was calculated at the core bottom. The simulation results were compared with PAKS NPP measured mixing factors data (available from FLOMIX project. Based on the comparison the SST turbulence model was chosen for the further simulations, which unifies the advantages of two-equation (kω and kε) models. The most widely used turbulence models are Reynolds-averaged Navier-Stokes models that are based on time-averaging of the equations. Time-averaging filters out all turbulent scales from the simulation, and the effect of turbulence on the mean flow is then re-introduced through appropriate modeling assumptions. Because of this characteristic of SST turbulence model a decision was made in year 2011 to investigate the coolant mixing with improved turbulence model as well. The hybrid SAS-SST turbulence model was chosen, which is capable of resolving large scale turbulent structures without the time and grid-scale resolution restrictions of LES, often allowing the use of existing grids created for Reynolds-averaged Navier-Stokes simulations. As a first step the coolant mixing was investigated in the downcomer only. Eddies are occurred after the loop connection because of the steep flow direction change. This turbulent, vertiginous flow was

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.

Numerical study on turbulent flow inside a channel with an extended chamber

International Nuclear Information System (INIS)

Lee, Young Tae; Lim, Hee Chang

2009-01-01

The paper presents a LES numerical simulation of turbulent flow around an extended chamber. The simulations are carried out on a series of 3-dimensional cavities placed in a turbulent boundary layer at a Reynolds number of 1.0x10 5 based on U and h, which are the velocity at the upper top of the cavity and the depth height, respectively. In order to get an appropriate solution in the Filtered Navier-Stokes equation for the incompressible flow, the computational mesh is densely attracted to the cavity surface and coarsely far-field, as this aids saving the computation cost and rapid convergence. The Boussinesq hypothesis is employed in the subgrid-scale turbulence model. In order to obtain the subgrid-scale turbulent viscosity, the Smagorinsky-Lilly SGS model is applied and the CFL number for time marching is 1.0. The results include the flow variations inside a cavity with the different sizes and shapes.

Numerical Study on Turbulent Flow Inside a Channel with an Extended Chamber

International Nuclear Information System (INIS)

Lee, Young Tae; Lim, Hee Chang

2010-01-01

The paper describes a Large Eddy Simulation (LES) study of turbulent flow around a cavity. A series of three-dimensional cavities placed in a turbulent boundary layer are simulated at a Reynolds number of 1.0 x 10 5 by considering U and h, which represent the velocity at the top and the depth of the cavity, respectively. In order to obtain the appropriate solution for the filtered Navier-Stokes equation for incompressible flow, the computational mesh forms dense close to the wall of the cavity but relatively coarse away from the wall; this helps reduce computation cost and ensure rapid convergence. The Boussinesq hypothesis is employed in the subgrid-scale turbulence model. In order to determine the subgrid-scale turbulent viscosity, the Smagorinsky-Lilly SGS model is applied and the CFL number for time marching is set as 1.0. The results show the flow variations inside cavities of different sizes and shapes

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.

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...

The time scale for the transition to turbulence in a high Reynolds number, accelerated flow

International Nuclear Information System (INIS)

Robey, H.F.; Zhou Ye; Buckingham, A.C.; Keiter, P.; Remington, B.A.; Drake, R.P.

2003-01-01

An experiment is described in which an interface between materials of different density is subjected to an acceleration history consisting of a strong shock followed by a period of deceleration. The resulting flow at this interface, initiated by the deposition of strong laser radiation into the initially well characterized solid materials, is unstable to both the Richtmyer-Meshkov (RM) and Rayleigh-Taylor (RT) instabilities. These experiments are of importance in their ability to access a difficult experimental regime characterized by very high energy density (high temperature and pressure) as well as large Reynolds number and Mach number. Such conditions are of interest, for example, in the study of the RM/RT induced mixing that occurs during the explosion of a core-collapse supernova. Under these experimental conditions, the flow is in the plasma state and given enough time will transition to turbulence. By analysis of the experimental data and a corresponding one-dimensional numerical simulation of the experiment, it is shown that the Reynolds number is sufficiently large (Re>10 5 ) to support a turbulent flow. An estimate of three key turbulence length scales (the Taylor and Kolmogorov microscales and a viscous diffusion scale), however, shows that the temporal duration of the present flow is insufficient to allow for the development of a turbulent inertial subrange. A methodology is described for estimating the time required under these conditions for the development of a fully turbulent flow

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...

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...

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.

TIDAL TURBULENCE SPECTRA FROM A COMPLIANT MOORING

Energy Technology Data Exchange (ETDEWEB)

Thomson, Jim; Kilcher, Levi; Richmond, Marshall C.; Talbert, Joe; deKlerk, Alex; Polagye, Brian; Guerra, Maricarmen; Cienfuegos, Rodrigo

2013-06-13

A compliant mooring to collect high frequency turbulence data at a tidal energy site is evaluated in a series of short demon- stration deployments. The Tidal Turbulence Mooring (TTM) improves upon recent bottom-mounted approaches by suspend- ing Acoustic Doppler Velocimeters (ADVs) at mid-water depths (which are more relevant to tidal turbines). The ADV turbulence data are superior to Acoustic Doppler Current Profiler (ADCP) data, but are subject to motion contamination when suspended on a mooring in strong currents. In this demonstration, passive stabilization is shown to be sufficient for acquiring bulk statistics of the turbulence, without motion correction. With motion cor- rection (post-processing), data quality is further improved; the relative merits of direct and spectral motion correction are dis- cussed.

Flume experiments on intermittency and zero-crossing properties of canopy turbulence

Science.gov (United States)

Poggi, Davide; Katul, Gabriel

2009-06-01

How the presence of a canopy alters the clustering and the fine scale intermittency exponents and any possible connections between them remains a vexing research problem in canopy turbulence. To begin progress on this problem, detailed flume experiments in which the longitudinal and vertical velocity time series were acquired using laser Doppler anemometry within and above a uniform canopy composed of densely arrayed rods. The time series analysis made use of the telegraphic approximation (TA) and phase-randomization (PR) methods. The TA preserved the so-called zero-crossing properties in the original turbulent velocity time series but eliminated amplitude variations, while the PR generated surrogate data that preserved the spectral scaling laws in the velocity series but randomized the acceleration statistics. Based on these experiments, it was shown that the variations in the dissipation intermittency exponents were well described by the Taylor microscale Reynolds number (Reλ) within and above the canopy. In terms of clustering, quantified here using the variance in zero-crossing density across scales, two scaling regimes emerged. For spatial scales much larger than the canopy height hc, representing the canonical scale of the vortices dominating the flow, no significant clustering was detected. For spatial scales much smaller than hc, significant clustering was discernable and follows an extensive scaling law inside the canopy. Moreover, the canopy signatures on the clustering scaling laws were weak. When repeating these clustering measures on the PR data, the results were indistinguishable from the original series. Hence, clustering exponents derived from variances in zero-crossing density across scales primarily depended on the velocity correlation function and not on the distributional properties of the acceleration. In terms of the connection between dissipation intermittency and clustering exponents, there was no significant relationship. While the former

Turbulence modulation induced by bubble swarm in oscillating-grid turbulence

International Nuclear Information System (INIS)

Morikawa, Koichi; Urano, Shigeyuki; Saito, Takayuki

2007-01-01

In the present study, liquid-phase turbulence modulation induced by a bubble swarm ascending in arbitrary turbulence was experimentally investigated. Liquid-phase homogeneous isotropic turbulence was formed using an oscillating grid in a cylindrical acrylic vessel of 149 mm in inner diameter. A bubble swarm consisting of 19 bubbles of 2.8 mm in equivalent diameter was examined; the bubble size and launching time were completely controlled using a bubble launching device through audio speakers. This bubble launching device was able to repeatedly control the bubble swarm arbitrarily and precisely. The bubble swarm was launched at a frequency of 4 Hz. The liquid phase motion was measured via two LDA (Laser Doppler Anemometer) probes. The turbulence intensity, spatial correlation and integral scale were calculated from LDA data obtained by the two spatially-separate-point measurement. When the bubble swarm was added, the turbulence intensity dramatically changed. The original isotropic turbulence was modulated to the anisotropic turbulence by the mutual interference between the bubble swarm and ambient isotropic turbulence. The integral scales were calculated from the spatial correlation function. The effects of the bubble swarm on the integral scales showed the tendencies similar to those on turbulence intensity. (author)

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.

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.

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.

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

Fluid particles only separate exponentially in the dissipation range of turbulence after extremely long times

Science.gov (United States)

Dhariwal, Rohit; Bragg, Andrew D.

2018-03-01

In this paper, we consider how the statistical moments of the separation between two fluid particles grow with time when their separation lies in the dissipation range of turbulence. In this range, the fluid velocity field varies smoothly and the relative velocity of two fluid particles depends linearly upon their separation. While this may suggest that the rate at which fluid particles separate is exponential in time, this is not guaranteed because the strain rate governing their separation is a strongly fluctuating quantity in turbulence. Indeed, Afik and Steinberg [Nat. Commun. 8, 468 (2017), 10.1038/s41467-017-00389-8] argue that there is no convincing evidence that the moments of the separation between fluid particles grow exponentially with time in the dissipation range of turbulence. Motivated by this, we use direct numerical simulations (DNS) to compute the moments of particle separation over very long periods of time in a statistically stationary, isotropic turbulent flow to see if we ever observe evidence for exponential separation. Our results show that if the initial separation between the particles is infinitesimal, the moments of the particle separation first grow as power laws in time, but we then observe convincing evidence that at sufficiently long times the moments do grow exponentially. However, this exponential growth is only observed after extremely long times ≳200 τη , where τη is the Kolmogorov time scale. This is due to fluctuations in the strain rate about its mean value measured along the particle trajectories, the effect of which on the moments of the particle separation persists for very long times. We also consider the backward-in-time (BIT) moments of the article separation, and observe that they too grow exponentially in the long-time regime. However, a dramatic consequence of the exponential separation is that at long times the difference between the rate of the particle separation forward in time (FIT) and BIT grows

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.

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").

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.

In-silico experiments on characteristic time scale at a shear-free gas-liquid interface in fully developed turbulence

International Nuclear Information System (INIS)

Nagaosa, Ryuichi; Handler, Robert A

2011-01-01

The purpose of this study is to model scalar transfer mechanisms in a fully developed turbulence for accurate predictions of the turbulent scalar flux across a shear-free gas-liquid interface. The concept of the surface-renewal approximation (Dankwerts, 1951) is introduced in this study to establish the predictive models for the interfacial scalar flux. Turbulent flow realizations obtained by a direct numerical simulation technique are employed to prepare details of three-dimensional information on turbulence in the region very close to the interface. Two characteristic time scales at the interface have been examined for exact prediction of the scalar transfer flux. One is the time scale which is reciprocal of the root-mean-square surface divergence, T γ = (γγ) −1/2 , where γ is the surface divergence. The other time scale to be examined is T S = Λ/V, where Λ is the zero-correlation length of the surface divergence as the interfacial length scale, and V is the root-mean-square velocity fluctuation in the streamwise direction as the interfacial velocity scale. The results of this study suggests that T γ is slightly unsatisfactory to correlate the turbulent scalar flux at the gas-liquid interface based on the surface-renewal approximation. It is also found that the proportionality constant appear to be 0.19, which is different with that observed in the laboratory experiments, 0.34 (Komori, Murakami, and Ueda, 1989). It is concluded that the time scale, T γ , is considered a different kind of the time scale observed in the laboratory experiments. On the other hand, the present in-silico experiments indicate that T s predicts the turbulent scalar flux based on the surface-renewal approximation in a satisfactory manner. It is also elucidated that the proportionality constant for T s is approximately 0.36, which is very close to that found by the laboratory experiments. This fact shows that the time scale T s appears to be essentially the same as the time scale

In-silico experiments on characteristic time scale at a shear-free gas-liquid interface in fully developed turbulence

Energy Technology Data Exchange (ETDEWEB)

Nagaosa, Ryuichi [Research Center for Compact Chemical System (CCS), AIST, 4-2-1 Nigatake, Miyagino, Sendai 983-8551 (Japan); Handler, Robert A, E-mail: ryuichi.nagaosa@aist.go.jp [Department of Mechanical Engineering, Texas A and M University, College Station, TX 77843-3123 (United States)

2011-12-22

The purpose of this study is to model scalar transfer mechanisms in a fully developed turbulence for accurate predictions of the turbulent scalar flux across a shear-free gas-liquid interface. The concept of the surface-renewal approximation (Dankwerts, 1951) is introduced in this study to establish the predictive models for the interfacial scalar flux. Turbulent flow realizations obtained by a direct numerical simulation technique are employed to prepare details of three-dimensional information on turbulence in the region very close to the interface. Two characteristic time scales at the interface have been examined for exact prediction of the scalar transfer flux. One is the time scale which is reciprocal of the root-mean-square surface divergence, T{sub {gamma}} = ({gamma}{gamma}){sup -1/2}, where {gamma} is the surface divergence. The other time scale to be examined is T{sub S} = {Lambda}/V, where {Lambda} is the zero-correlation length of the surface divergence as the interfacial length scale, and V is the root-mean-square velocity fluctuation in the streamwise direction as the interfacial velocity scale. The results of this study suggests that T{sub {gamma}} is slightly unsatisfactory to correlate the turbulent scalar flux at the gas-liquid interface based on the surface-renewal approximation. It is also found that the proportionality constant appear to be 0.19, which is different with that observed in the laboratory experiments, 0.34 (Komori, Murakami, and Ueda, 1989). It is concluded that the time scale, T{sub {gamma}}, is considered a different kind of the time scale observed in the laboratory experiments. On the other hand, the present in-silico experiments indicate that T{sub s} predicts the turbulent scalar flux based on the surface-renewal approximation in a satisfactory manner. It is also elucidated that the proportionality constant for T{sub s} is approximately 0.36, which is very close to that found by the laboratory experiments. This fact shows

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.

The effect of existing turbulence on stratified shear instability

Science.gov (United States)

Kaminski, Alexis; Smyth, William

2017-11-01

Ocean turbulence is an essential process governing, for example, heat uptake by the ocean. In the stably-stratified ocean interior, this turbulence occurs in discrete events driven by vertical variations of the horizontal velocity. Typically, these events have been modelled by assuming an initially laminar stratified shear flow which develops wavelike instabilities, becomes fully turbulent, and then relaminarizes into a stable state. However, in the real ocean there is always some level of turbulence left over from previous events, and it is not yet understood how this turbulence impacts the evolution of future mixing events. Here, we perform a series of direct numerical simulations of turbulent events developing in stratified shear flows that are already at least weakly turbulent. We do so by varying the amplitude of the initial perturbations, and examine the subsequent development of the instability and the impact on the resulting turbulent fluxes. This work is supported by NSF Grant OCE1537173.

A self-affine multi-fractal wave turbulence discrimination method using data from single point fast response sensors in a nocturnal atmospheric boundary layer

OpenAIRE

Kamada, Ray; Decaria, Alex Joseph

1992-01-01

We present DA, a self-affine, multi-fractal which may become the first routine wave/turbulence discriminant for time series data. Using nocturnal atmospheric data, we show the advantages of D A over self-similar fractals and standard turbulence measures such as FFTs, Richardson number, Brunt-Vaisala frequency, buoyancy length scale, variances, turbulent kinetic energy, and phase averaging. DA also shows promise in resolving "wave-break" events. Since it uses local basis functions, DA may be...

Strategic thinking in turbulent times

Directory of Open Access Journals (Sweden)

Bratianu Constantin

2017-07-01

Full Text Available The purpose of this paper is to present a structural analysis of strategic thinking spectrum in turbulent times. Business excellence cannot be achieved without a well-defined strategic thinking spectrum able to elaborate and implement strategies in a fast changeable and unpredictable business environment. Strategic thinking means to think for a desirable future which can be ahead 4-5 years of the present time and to make decisions to the best of our knowledge for that unknown business environment. Thus, the research question is: How can we conceive the spectrum of strategic thinking such that we shall be able to deal with a complex and unknown future in achieving a competitive advantage? The methodology used to answer this question is based on metaphorical thinking, and multidimensional analysis. I shall consider four main dimensions: time, complexity, uncertainty, and novelty. On each of these dimensions I shall analyze the known thinking models and their attributes with respect to request formulated in the research question. Then, I shall choose those thinking models that correspond to the future characteristics and integrate them in a continuous spectrum. On each dimension I shall consider three basic thinking models. On the time dimension they are: inertial, dynamic and entropic thinking. On the complexity dimension they are: linear, nonlinear and systemic thinking. On the uncertainty dimension they are: deterministic, probabilistic and chaotic thinking. Finally, on the novelty dimension we have: template, intelligent and creative thinking. Considering all requirements for the unknown future, we conclude that strategic thinking spectrum should contain: entropic, nonlinear and systemic, probabilistic and chaotic, intelligent and creative thinking models. Such a spectrum increases the capacity of our understanding and as a consequence it enhances the capability of making adequate decisions in conditions of complexity and uncertainty.

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.

Comparison of turbulence mitigation algorithms

Science.gov (United States)

Kozacik, Stephen T.; Paolini, Aaron; Sherman, Ariel; Bonnett, James; Kelmelis, Eric

2017-07-01

When capturing imagery over long distances, atmospheric turbulence often degrades the data, especially when observation paths are close to the ground or in hot environments. These issues manifest as time-varying scintillation and warping effects that decrease the effective resolution of the sensor and reduce actionable intelligence. In recent years, several image processing approaches to turbulence mitigation have shown promise. Each of these algorithms has different computational requirements, usability demands, and degrees of independence from camera sensors. They also produce different degrees of enhancement when applied to turbulent imagery. Additionally, some of these algorithms are applicable to real-time operational scenarios while others may only be suitable for postprocessing workflows. EM Photonics has been developing image-processing-based turbulence mitigation technology since 2005. We will compare techniques from the literature with our commercially available, real-time, GPU-accelerated turbulence mitigation software. These comparisons will be made using real (not synthetic), experimentally obtained data for a variety of conditions, including varying optical hardware, imaging range, subjects, and turbulence conditions. Comparison metrics will include image quality, video latency, computational complexity, and potential for real-time operation. Additionally, we will present a technique for quantitatively comparing turbulence mitigation algorithms using real images of radial resolution targets.

The Fiftieth Anniversary of Brookhaven National Laboratory: A Turbulent Time

Science.gov (United States)

Bond, Peter D.

2018-03-01

The fiftieth anniversary year of Brookhaven National Laboratory was momentous, but for reasons other than celebrating its scientific accomplishments. Legacy environmental contamination, community unrest, politics, and internal Department of Energy issues dominated the year. It was the early days of perhaps the most turbulent time in the lab's history. The consequences resulted in significant changes at the lab, but in addition they brought a change to contracts to manage the Department of Energy laboratories.

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.

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...

Density Effects on Post-shock Turbulence Structure

Science.gov (United States)

Tian, Yifeng; Jaberi, Farhad; Livescu, Daniel; Li, Zhaorui; Michigan State University Collaboration; Los Alamos National Laboratory Collaboration; Texas A&M University-Corpus Christi Collaboration

2017-11-01

The effects of density variations due to mixture composition on post-shock turbulence structure are studied using turbulence-resolving shock-capturing simulations. This work extends the canonical Shock-Turbulence Interaction (STI) problem to involve significant variable density effects. The numerical method has been verified using a series of grid and LIA convergence tests, and is used to generate accurate post-shock turbulence data for a detailed flow study. Density effects on post-shock turbulent statistics are shown to be significant, leading to an increased amplification of turbulent kinetic energy (TKE). Eulerian and Lagrangian analyses show that the increase in the post-shock correlation between rotation and strain is weakened in the case with significant density variations (referred to as the ``multi-fluid'' case). Similar to previous single-fluid results and LIA predictions, the shock wave significantly changes the topology of the turbulent structures, exhibiting a symmetrization of the joint PDF of second and third invariant of the deviatoric part of velocity gradient tensor. In the multi-fluid case, this trend is more significant and mainly manifested in the heavy fluid regions. Lagrangian data are also used to study the evolution of turbulence structure away from the shock wave and assess the accuracy of Lagrangian dynamical models.

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.

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.

TURBULENT DISKS ARE NEVER STABLE: FRAGMENTATION AND TURBULENCE-PROMOTED PLANET FORMATION

Energy Technology Data Exchange (ETDEWEB)

Hopkins, Philip F. [TAPIR, Mailcode 350-17, California Institute of Technology, Pasadena, CA 91125 (United States); Christiansen, Jessie L., E-mail: phopkins@caltech.edu [SETI Institute/NASA Ames Research Center, M/S 244-30, Moffett Field, CA 94035 (United States)

2013-10-10

A fundamental assumption in our understanding of disks is that when the Toomre Q >> 1, the disk is stable against fragmentation into self-gravitating objects (and so cannot form planets via direct collapse). But if disks are turbulent, this neglects a spectrum of stochastic density fluctuations that can produce rare, high-density mass concentrations. Here, we use a recently developed analytic framework to predict the statistics of these fluctuations, i.e., the rate of fragmentation and mass spectrum of fragments formed in a turbulent Keplerian disk. Turbulent disks are never completely stable: we calculate the (always finite) probability of forming self-gravitating structures via stochastic turbulent density fluctuations in such disks. Modest sub-sonic turbulence above Mach number M∼0.1 can produce a few stochastic fragmentation or 'direct collapse' events over ∼Myr timescales, even if Q >> 1 and cooling is slow (t{sub cool} >> t{sub orbit}). In transsonic turbulence this extends to Q ∼ 100. We derive the true Q-criterion needed to suppress such events, which scales exponentially with Mach number. We specify to turbulence driven by magneto-rotational instability, convection, or spiral waves and derive equivalent criteria in terms of Q and the cooling time. Cooling times ∼> 50 t{sub dyn} may be required to completely suppress fragmentation. These gravo-turbulent events produce mass spectra peaked near ∼(Q M{sub disk}/M{sub *}){sup 2} M{sub disk} (rocky-to-giant planet masses, increasing with distance from the star). We apply this to protoplanetary disk models and show that even minimum-mass solar nebulae could experience stochastic collapse events, provided a source of turbulence.

TURBULENT DISKS ARE NEVER STABLE: FRAGMENTATION AND TURBULENCE-PROMOTED PLANET FORMATION

International Nuclear Information System (INIS)

Hopkins, Philip F.; Christiansen, Jessie L.

2013-01-01

A fundamental assumption in our understanding of disks is that when the Toomre Q >> 1, the disk is stable against fragmentation into self-gravitating objects (and so cannot form planets via direct collapse). But if disks are turbulent, this neglects a spectrum of stochastic density fluctuations that can produce rare, high-density mass concentrations. Here, we use a recently developed analytic framework to predict the statistics of these fluctuations, i.e., the rate of fragmentation and mass spectrum of fragments formed in a turbulent Keplerian disk. Turbulent disks are never completely stable: we calculate the (always finite) probability of forming self-gravitating structures via stochastic turbulent density fluctuations in such disks. Modest sub-sonic turbulence above Mach number M∼0.1 can produce a few stochastic fragmentation or 'direct collapse' events over ∼Myr timescales, even if Q >> 1 and cooling is slow (t cool >> t orbit ). In transsonic turbulence this extends to Q ∼ 100. We derive the true Q-criterion needed to suppress such events, which scales exponentially with Mach number. We specify to turbulence driven by magneto-rotational instability, convection, or spiral waves and derive equivalent criteria in terms of Q and the cooling time. Cooling times ∼> 50 t dyn may be required to completely suppress fragmentation. These gravo-turbulent events produce mass spectra peaked near ∼(Q M disk /M * ) 2 M disk (rocky-to-giant planet masses, increasing with distance from the star). We apply this to protoplanetary disk models and show that even minimum-mass solar nebulae could experience stochastic collapse events, provided a source of turbulence

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...

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.

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.

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

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...

Turbulence closure: turbulence, waves and the wave-turbulence transition – Part 1: Vanishing mean shear

Directory of Open Access Journals (Sweden)

H. Z. Baumert

2009-03-01

Full Text Available This paper extends a turbulence closure-like model for stably stratified flows into a new dynamic domain in which turbulence is generated by internal gravity waves rather than mean shear. The model turbulent kinetic energy (TKE, K balance, its first equation, incorporates a term for the energy transfer from internal waves to turbulence. This energy source is in addition to the traditional shear production. The second variable of the new two-equation model is the turbulent enstrophy (Ω. Compared to the traditional shear-only case, the Ω-equation is modified to account for the effect of the waves on the turbulence time and space scales. This modification is based on the assumption of a non-zero constant flux Richardson number in the limit of vanishing mean shear when turbulence is produced exclusively by internal waves. This paper is part 1 of a continuing theoretical development. It accounts for mean shear- and internal wave-driven mixing only in the two limits of mean shear and no waves and waves but no mean shear, respectively.

The new model reproduces the wave-turbulence transition analyzed by D'Asaro and Lien (2000b. At small energy density E of the internal wave field, the turbulent dissipation rate (ε scales like ε~E². This is what is observed in the deep sea. With increasing E, after the wave-turbulence transition has been passed, the scaling changes to ε~E¹. This is observed, for example, in the highly energetic tidal flow near a sill in Knight Inlet. The new model further exhibits a turbulent length scale proportional to the Ozmidov scale, as observed in the ocean, and predicts the ratio between the turbulent Thorpe and Ozmidov length scales well within the range observed in the ocean.

An alternative way to track the hot money in turbulent times

Science.gov (United States)

Sensoy, Ahmet

2015-02-01

During recent years, networks have proven to be an efficient way to characterize and investigate a wide range of complex financial systems. In this study, we first obtain the dynamic conditional correlations between filtered exchange rates (against US dollar) of several countries and introduce a time-varying threshold correlation level to define dynamic strong correlations between these exchange rates. Then, using evolving networks obtained from strong correlations, we propose an alternative approach to track the hot money in turbulent times. The approach is demonstrated for the time period including the financial turmoil of 2008. Other applications are also discussed.

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.

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

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

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...

Eulerian and Lagrangian views of a turbulent boundary layer flow using time-resolved tomographic PIV

NARCIS (Netherlands)

Schröder, A.; Geisler, R.; Staack, K.; Elsinga, G.E.; Scarano, F.; Wieneke, B.; Henning, A.; Poelma, C.; Westerweel, J.

2010-01-01

Coherent structures and their time evolution in the logarithmic region of a turbulent boundary layer investigated by means of 3D space–time correlations and time-dependent conditional averaging techniques are the focuses of the present paper. Experiments have been performed in the water tunnel at TU

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)…

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 ...

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.

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

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...

Transitional-turbulent spots and turbulent-turbulent spots in boundary layers.

Science.gov (United States)

Wu, Xiaohua; Moin, Parviz; Wallace, James M; Skarda, Jinhie; Lozano-Durán, Adrián; Hickey, Jean-Pierre

2017-07-03

Two observations drawn from a thoroughly validated direct numerical simulation of the canonical spatially developing, zero-pressure gradient, smooth, flat-plate boundary layer are presented here. The first is that, for bypass transition in the narrow sense defined herein, we found that the transitional-turbulent spot inception mechanism is analogous to the secondary instability of boundary-layer natural transition, namely a spanwise vortex filament becomes a [Formula: see text] vortex and then, a hairpin packet. Long streak meandering does occur but usually when a streak is infected by a nearby existing transitional-turbulent spot. Streak waviness and breakdown are, therefore, not the mechanisms for the inception of transitional-turbulent spots found here. Rather, they only facilitate the growth and spreading of existing transitional-turbulent spots. The second observation is the discovery, in the inner layer of the developed turbulent boundary layer, of what we call turbulent-turbulent spots. These turbulent-turbulent spots are dense concentrations of small-scale vortices with high swirling strength originating from hairpin packets. Although structurally quite similar to the transitional-turbulent spots, these turbulent-turbulent spots are generated locally in the fully turbulent environment, and they are persistent with a systematic variation of detection threshold level. They exert indentation, segmentation, and termination on the viscous sublayer streaks, and they coincide with local concentrations of high levels of Reynolds shear stress, enstrophy, and temperature fluctuations. The sublayer streaks seem to be passive and are often simply the rims of the indentation pockets arising from the turbulent-turbulent spots.

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.

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 ...

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.

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 ...

Lagrangian statistics across the turbulent-nonturbulent interface in a turbulent plane jet.

Science.gov (United States)

Taveira, Rodrigo R; Diogo, José S; Lopes, Diogo C; da Silva, Carlos B

2013-10-01

Lagrangian statistics from millions of particles are used to study the turbulent entrainment mechanism in a direct numerical simulation of a turbulent plane jet at Re(λ) ≈ 110. The particles (tracers) are initially seeded at the irrotational region of the jet near the turbulent shear layer and are followed as they are drawn into the turbulent region across the turbulent-nonturbulent interface (TNTI), allowing the study of the enstrophy buildup and thereby characterizing the turbulent entrainment mechanism in the jet. The use of Lagrangian statistics following fluid particles gives a more correct description of the entrainment mechanism than in previous works since the statistics in relation to the TNTI position involve data from the trajectories of the entraining fluid particles. The Lagrangian statistics for the particles show the existence of a velocity jump and a characteristic vorticity jump (with a thickness which is one order of magnitude greater than the Kolmogorov microscale), in agreement with previous results using Eulerian statistics. The particles initially acquire enstrophy by viscous diffusion and later by enstrophy production, which becomes "active" only deep inside the turbulent region. Both enstrophy diffusion and production near the TNTI differ substantially from inside the turbulent region. Only about 1% of all particles find their way into pockets of irrotational flow engulfed into the turbulent shear layer region, indicating that "engulfment" is not significant for the present flow, indirectly suggesting that the entrainment is largely due to "nibbling" small-scale mechanisms acting along the entire TNTI surface. Probability density functions of particle positions suggests that the particles spend more time crossing the region near the TNTI than traveling inside the turbulent region, consistent with the particles moving tangent to the interface around the time they cross it.

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.

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.

Electromagnetic radiation from strong Langmuir turbulence

International Nuclear Information System (INIS)

Akimoto, K.; Rowland, H.L.; Papadopoulos, K.

1988-01-01

A series of computer simulations is reported showing the generation of electromagnetic radiation by strong Langmuir turbulence. The simulations were carried out with a fully electromagnetic 2 1/2 -dimensional fluid code. The radiation process takes place in two stages that reflect the evolution of the electrostatic turbulence. During the first stage while the electrostatic turbulence is evolving from an initial linear wave packet into a planar soliton, the radiation is primarily at ω/sub e/. During the second stage when transverse instabilities lead to the collapse and dissipation of the solitons, 2ω/sub e/ and ω/sub e/ radiation are comparable, and 3ω/sub e/ is also present. The radiation power at ω = 2ω/sub e/ is in good agreement with theoretical predictions for electromagnetic emissions by collapsing solitons

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.

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.

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.

Time-dependent transport of energetic particles in magnetic turbulence: computer simulations versus analytical theory

Science.gov (United States)

Arendt, V.; Shalchi, A.

2018-06-01

We explore numerically the transport of energetic particles in a turbulent magnetic field configuration. A test-particle code is employed to compute running diffusion coefficients as well as particle distribution functions in the different directions of space. Our numerical findings are compared with models commonly used in diffusion theory such as Gaussian distribution functions and solutions of the cosmic ray Fokker-Planck equation. Furthermore, we compare the running diffusion coefficients across the mean magnetic field with solutions obtained from the time-dependent version of the unified non-linear transport theory. In most cases we find that particle distribution functions are indeed of Gaussian form as long as a two-component turbulence model is employed. For turbulence setups with reduced dimensionality, however, the Gaussian distribution can no longer be obtained. It is also shown that the unified non-linear transport theory agrees with simulated perpendicular diffusion coefficients as long as the pure two-dimensional model is excluded.

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...

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.

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…

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.

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.

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

LDA measurements and turbulence spectral analysis in an agitated vessel

Directory of Open Access Journals (Sweden)

Chára Zdeněk

2013-04-01

Full Text Available During the last years considerable improvement of the derivation of turbulence power spectrum from Laser Doppler Anemometry (LDA has been achieved. The irregularly sampled LDA data is proposed to approximate by several methods e.g. Lomb-Scargle method, which estimates amplitude and phase of spectral lines from missing data, methods based on the reconstruction of the auto-correlation function (referred to as correlation slotting technique, methods based on the reconstruction of the time series using interpolation between the uneven sampling and subsequent resampling etc. These different methods were used on the LDA data measured in an agitated vessel and the results of the power spectrum calculations were compared. The measurements were performed in the mixing vessel with flat bottom. The vessel was equipped with four baffles and agitated with a six-blade pitched blade impeller. Three values of the impeller speed (Reynolds number were tested. Long time series of the axial velocity component were measured in selected points. In each point the time series were analyzed and evaluated in a form of power spectrum.

LDA measurements and turbulence spectral analysis in an agitated vessel

Science.gov (United States)

Kysela, Bohuš; Konfršt, Jiří; Chára, Zdeněk

2013-04-01

During the last years considerable improvement of the derivation of turbulence power spectrum from Laser Doppler Anemometry (LDA) has been achieved. The irregularly sampled LDA data is proposed to approximate by several methods e.g. Lomb-Scargle method, which estimates amplitude and phase of spectral lines from missing data, methods based on the reconstruction of the auto-correlation function (referred to as correlation slotting technique), methods based on the reconstruction of the time series using interpolation between the uneven sampling and subsequent resampling etc. These different methods were used on the LDA data measured in an agitated vessel and the results of the power spectrum calculations were compared. The measurements were performed in the mixing vessel with flat bottom. The vessel was equipped with four baffles and agitated with a six-blade pitched blade impeller. Three values of the impeller speed (Reynolds number) were tested. Long time series of the axial velocity component were measured in selected points. In each point the time series were analyzed and evaluated in a form of power spectrum.

Characterizing time series: when Granger causality triggers complex networks

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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.

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)

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

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

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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.

The Hurst Phenomenon in Error Estimates Related to Atmospheric Turbulence

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Dias, Nelson Luís; Crivellaro, Bianca Luhm; Chamecki, Marcelo

2018-05-01

The Hurst phenomenon is a well-known feature of long-range persistence first observed in hydrological and geophysical time series by E. Hurst in the 1950s. It has also been found in several cases in turbulence time series measured in the wind tunnel, the atmosphere, and in rivers. Here, we conduct a systematic investigation of the value of the Hurst coefficient H in atmospheric surface-layer data, and its impact on the estimation of random errors. We show that usually H > 0.5 , which implies the non-existence (in the statistical sense) of the integral time scale. Since the integral time scale is present in the Lumley-Panofsky equation for the estimation of random errors, this has important practical consequences. We estimated H in two principal ways: (1) with an extension of the recently proposed filtering method to estimate the random error (H_p ), and (2) with the classical rescaled range introduced by Hurst (H_R ). Other estimators were tried but were found less able to capture the statistical behaviour of the large scales of turbulence. Using data from three micrometeorological campaigns we found that both first- and second-order turbulence statistics display the Hurst phenomenon. Usually, H_R is larger than H_p for the same dataset, raising the question that one, or even both, of these estimators, may be biased. For the relative error, we found that the errors estimated with the approach adopted by us, that we call the relaxed filtering method, and that takes into account the occurrence of the Hurst phenomenon, are larger than both the filtering method and the classical Lumley-Panofsky estimates. Finally, we found that there is no apparent relationship between H and the Obukhov stability parameter. The relative errors, however, do show stability dependence, particularly in the case of the error of the kinematic momentum flux in unstable conditions, and that of the kinematic sensible heat flux in stable conditions.

Behaviour of turbulence models near a turbulent/non-turbulent interface revisited

International Nuclear Information System (INIS)

Ferrey, P.; Aupoix, B.

2006-01-01

The behaviour of turbulence models near a turbulent/non-turbulent interface is investigated. The analysis holds as well for two-equation as for Reynolds stress turbulence models using Daly and Harlow diffusion model. The behaviour near the interface is shown not to be a power law, as usually considered, but a more complex parametric solution. Why previous works seemed to numerically confirm the power law solution is explained. Constraints for turbulence modelling, i.e., for ensuring that models have a good behaviour near a turbulent/non-turbulent interface so that the solution is not sensitive to small turbulence levels imposed in the irrotational flow, are drawn

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.

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.

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

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.

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.

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.

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.

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.

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)

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

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.

Time-Series Analysis: A Cautionary Tale

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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.

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...

The Skipheia Wind Measurement Station. Instrumentation, Wind Speed Profiles and Turbulence Spectra

Energy Technology Data Exchange (ETDEWEB)

Aasen, S E

1995-10-01

This thesis describes the design of a measurement station for turbulent wind and presents results from an analysis of the collected data. The station is located at Skipheia near the south-west end of Froeya, an island off the coast of Mid-Norway. The station is unique for studies of turbulent winds because of the large numbers of sensors, which are located at various heights above ground up to 100 m, a sampling rate of 0.85 Hz and storage of the complete time series. The frequency of lightning and atmospheric discharges to the masts are quite high and much effort has gone into minimizing the damage caused by lightning activity. A major part of the thesis deals with data analysis and modelling. There are detailed discussions on the various types of wind sensors and their calibration, the data acquisition system and operating experiences with it, the database, data quality control, the wind speed profile and turbulence. 40 refs., 78 figs., 17 tabs.

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.

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.

Time-resolved measurements of coherent structures in the turbulent boundary layer

Science.gov (United States)

LeHew, J. A.; Guala, M.; McKeon, B. J.

2013-04-01

Time-resolved particle image velocimetry was used to examine the structure and evolution of swirling coherent structure (SCS), one interpretation of which is a marker for a three-dimensional coherent vortex structure, in wall-parallel planes of a turbulent boundary layer with a large field of view, 4.3 δ × 2.2 δ. Measurements were taken at four different wall-normal locations ranging from y/ δ = 0.08-0.48 at a friction Reynolds number, Re τ = 410. The data set yielded statistically converged results over a larger field of view than typically observed in the literature. The method for identifying and tracking swirling coherent structure is discussed, and the resulting trajectories, convection velocities, and lifespan of these structures are analyzed at each wall-normal location. The ability of a model in which the entirety of an individual SCS travels at a single convection velocity, consistent with the attached eddy hypothesis of Townsend (The structure of turbulent shear flows. Cambridge University Press, Cambridge, 1976), to describe the data is investigated. A methodology for determining whether such structures are "attached" or "detached" from the wall is also proposed and used to measure the lifespan and convection velocity distributions of these different structures. SCS were found to persist for longer periods of time further from the wall, particularly those inferred to be "detached" from the wall, which could be tracked for longer than 5 eddy turnover times.

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.

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.

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.

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

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.

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.

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)

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

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

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.

Turbulence from a microorganism's perspective: Does the open ocean feel different than a coral reef?

Science.gov (United States)

Pepper, Rachel; Variano, Evan; Koehl, M. A. R.

2012-11-01

Microorganisms in the ocean live in turbulent flows. Swimming microorganisms navigate through the water (e.g. larvae land on suitable substrata, predators find patches of prey), but the mechanisms by which they do so in turbulent flow are poorly understood as are the roles of passive transport versus active behaviors. Because microorganisms are smaller than the Kolmagorov length (the smallest scale of eddies in turbulent flow), they experience turbulence as a series of linear gradients in the velocity that vary in time. While the average strength of these gradients and a timescale can be computed from some typical characteristics of the flow, such as the turbulent kinetic energy or the dissipation rate, there are indications that organisms are disproportionally affected by rare, extreme events. Understanding the frequency of such events in different environments will be critical to understanding how microorganisms respond to and navigate in turbulence. To understand the hydrodynamic cues that microorganisms experience in the ocean we must measure velocity gradients in realistic turbulent flow on the spatial and temporal scales encountered by microorganisms. We have been exploring the effect of the spatial resolution of PIV and DNS of turbulent flow on the presence of velocity gradients of different magnitudes at the scale of microorganisms. Here we present some results of PIV taken at different resolutions in turbulent flow over rough biological substrata to illustrate the challenges of quantifying the fluctuations in velocity gradients encountered by aquatic microorganisms.

Space-time statistics of the turbulence in the PRETEXT and TEXT tokamak edge plasmas

International Nuclear Information System (INIS)

Levinson, S.J.

1986-01-01

A study of the statistical space-time properties of the turbulence observed in the edge regions of the PRETEXT and the TEXT tokamaks are reported. Computer estimates of the particle-transport spectrum T(omega), and of the local wavenumber-frequency spectra S(K,omega) for poloidal (k/sub y/) and toroidal (k/sub z/) wavenumbers was determined. A conventional fast-Fourier-transform technique is used initially for the analyses of the potential and density fluctuations obtained from spatially fixed Langmuir-probe pairs. Measurements of the fluctuation-induced particle transport revealed that the particle flux is outward for both PRETEXT and TEXT, and it results primarily from the low-frequency, long-wavelength components of the turbulence. The S(K/sub y/, omega) spectra are dominated by low frequencies ( -1 ) and appear broadened about an approximately linear statistical dispersion relation, anti k(omega). The broadening is characterized by a spectral width sigma/sub k/(omega) (rms deviation about anti k(omega)). In PRETEXT, sigma/sub k/(omega) is of the order of anti k(omega), and the turbulence appears to propagate poloidally with an apparent mean phase velocity of 1-2 x 10 5 cm/s in the ion diamagnetic drift direction. In TEXT, a reversal in the phase velocity of the turbulence in the edge plasma was observed

Torque fluctuations caused by upstream mean flow and turbulence

Science.gov (United States)

Farr, T. D.; Hancock, P. E.

2014-12-01

A series of studies are in progress investigating the effects of turbine-array-wake interactions for a range of atmospheric boundary layer states by means of the EnFlo meteorological wind tunnel. The small, three-blade model wind turbines drive 4-quadrant motor-generators. Only a single turbine in neutral flow is considered here. The motor-generator current can be measured with adequate sensitivity by means of a current sensor allowing the mean and fluctuating torque to be inferred. Spectra of torque fluctuations and streamwise velocity fluctuations ahead of the rotor, between 0.1 and 2 diameters, show that only the large-scale turbulent motions contribute significantly to the torque fluctuations. Time-lagged cross-correlation between upstream velocity and torque fluctuations are largest over the inner part of the blade. They also show the turbulence to be frozen in behaviour over the 2 diameters upstream of the turbine.

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...

New Models for Velocity/Pressure-Gradient Correlations in Turbulent Boundary Layers

Science.gov (United States)

Poroseva, Svetlana; Murman, Scott

2014-11-01

To improve the performance of Reynolds-Averaged Navier-Stokes (RANS) turbulence models, one has to improve the accuracy of models for three physical processes: turbulent diffusion, interaction of turbulent pressure and velocity fluctuation fields, and dissipative processes. The accuracy of modeling the turbulent diffusion depends on the order of a statistical closure chosen as a basis for a RANS model. When the Gram-Charlier series expansions for the velocity correlations are used to close the set of RANS equations, no assumption on Gaussian turbulence is invoked and no unknown model coefficients are introduced into the modeled equations. In such a way, this closure procedure reduces the modeling uncertainty of fourth-order RANS (FORANS) closures. Experimental and direct numerical simulation data confirmed the validity of using the Gram-Charlier series expansions in various flows including boundary layers. We will address modeling the velocity/pressure-gradient correlations. New linear models will be introduced for the second- and higher-order correlations applicable to two-dimensional incompressible wall-bounded flows. Results of models' validation with DNS data in a channel flow and in a zero-pressure gradient boundary layer over a flat plate will be demonstrated. A part of the material is based upon work supported by NASA under award NNX12AJ61A.

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.

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.

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

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

THE MATRYOSHKA RUN. II. TIME-DEPENDENT TURBULENCE STATISTICS, STOCHASTIC PARTICLE ACCELERATION, AND MICROPHYSICS IMPACT IN A MASSIVE GALAXY CLUSTER

International Nuclear Information System (INIS)

Miniati, Francesco

2015-01-01

We use the Matryoshka run to study the time-dependent statistics of structure-formation-driven turbulence in the intracluster medium of a 10 15 M ☉ galaxy cluster. We investigate the turbulent cascade in the inner megaparsec for both compressional and incompressible velocity components. The flow maintains approximate conditions of fully developed turbulence, with departures thereof settling in about an eddy-turnover time. Turbulent velocity dispersion remains above 700 km s –1 even at low mass accretion rate, with the fraction of compressional energy between 10% and 40%. The normalization and the slope of the compressional turbulence are susceptible to large variations on short timescales, unlike the incompressible counterpart. A major merger occurs around redshift z ≅ 0 and is accompanied by a long period of enhanced turbulence, ascribed to temporal clustering of mass accretion related to spatial clustering of matter. We test models of stochastic acceleration by compressional modes for the origin of diffuse radio emission in galaxy clusters. The turbulence simulation model constrains an important unknown of this complex problem and brings forth its dependence on the elusive microphysics of the intracluster plasma. In particular, the specifics of the plasma collisionality and the dissipation physics of weak shocks affect the cascade of compressional modes with strong impact on the acceleration rates. In this context radio halos emerge as complex phenomena in which a hierarchy of processes acting on progressively smaller scales are at work. Stochastic acceleration by compressional modes implies statistical correlation of radio power and spectral index with merging cores distance, both testable in principle with radio surveys

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

Hierarchy compensation of non-homogeneous intermittent atmospheric turbulence

Science.gov (United States)

Redondo, Jose M.; Mahjoub, Otman B.; Cantalapiedra, Inma R.

2010-05-01

In this work a study both the internal turbulence energy cascade intermittency evaluated from wind speed series in the atmospheric boundary layer, as well as the role of external or forcing intermittency based on the flatness (Vindel et al 2008)is carried out. The degree of intermittency in the stratified ABL flow (Cuxart et al. 2000) can be studied as the deviation, from the linear form, of the absolute scaling exponents of the structure functions as well as generalizing for non-isotropic and non-homogeneous turbulence, even in non-inertial ranges (in the Kolmogorov-Kraichnan sense) where the scaling exponents are not constant. The degree of intermittency, evaluated in the non-local quasi-inertial range, is explained from the variation with scale of the energy transfer as well as the dissipation. The scale to scale transfer and the structure function scaling exponents are calculated and from these the intermittency parametres. The turbulent diffusivity could also be estimated and compared with Richardson's law. Some two point correlations and time lag calculations are used to investigate the time and spatial integral length scales obtained from both Lagrangian and Eulerian correlations and functions, and we compare these results with both theoretical and laboratory data. We develop a theoretical description of how to measure the different levels of intermittency following (Mahjoub et al. 1998, 2000) and the role of locality in higher order exponents of structure function analysis. Vindel J.M., Yague C. and Redondo J.M. (2008) Structure function analysis and intermittency in the ABL. Nonlin. Processes Geophys., 15, 915-929. Cuxart J, Yague C, Morales G, Terradellas E, Orbe J, Calvo J, Fernández A, Soler M R, Infante C, Buenestado P, Espinalt A, Joergensen H E, Rees J M, Vilá J, Redondo J M, Cantalapiedra R and Conangla L (2000): Stable atmospheric boundary-layer experiment in Spain (Sables 98): a report, Boundary-Layer Meteorology 96, 337-370 Mahjoub O

Dynamic structure in self-sustained turbulence

International Nuclear Information System (INIS)

Itoh, K.; Itoh, S.; Yagi, M.; Fukuyama, A.

1995-06-01

Dynamical equation for the self-sustained and pressure-driven turbulence in toroidal plasmas is derived. The growth rate of the dressed-test mode, which belongs to the subcritical turbulence, is obtained as a function of the turbulent transport coefficient. In the limit of the low fluctuation level, the mode has the feature of the nonlinear instability and shows the explosive growth. The growth rate vanishes when the driven transport reaches to the stationarily-turbulent level. The stationary solution is thermodynamically stable. The characteristic time, by which the stationary and self-sustained turbulence is established, scales with the ion-sound transit time and is accelerated by the bad magnetic curvature. Influences of the pressure gradient as well as the radial electric field inhomogeneity are quantified. (author)

Airborne-Measured Spatially-Averaged Temperature and Moisture Turbulent Structure Parameters Over a Heterogeneous Surface

Science.gov (United States)

Platis, Andreas; Martinez, Daniel; Bange, Jens

2014-05-01

Turbulent structure parameters of temperature and humidity can be derived from scintillometer measurements along horizontal paths of several 100 m to several 10 km. These parameters can be very useful to estimate the vertical turbulent heat fluxes at the surface (applying MOST). However, there are many assumptions required by this method which can be checked using in situ data, e.g. 1) Were CT2 and CQ2 correctly derived from the initial CN2 scintillometer data (structure parameter of density fluctuations or refraction index, respectively)? 2) What is the influence of the surround hetereogeneous surface regarding its footprint and the weighted averaging effect of the scintillometer method 3) Does MOST provide the correct turbulent fluxes from scintillometer data. To check these issues, in situ data from low-level flight measurements are well suited, since research aircraft cover horizontal distances in very short time (Taylor's hypothesis of a frozen turbulence structure can be applyed very likely). From airborne-measured time series the spatial series are calculated and then their structure functions that finally provide the structure parameters. The influence of the heterogeneous surface can be controlled by the definition of certain moving-average window sizes. A very useful instrument for this task are UAVs since they can fly very low and maintain altitude very precisely. However, the data base of such unmanned operations is still quite thin. So in this contribution we want to present turbulence data obtained with the Helipod, a turbulence probe hanging below a manned helicopter. The structure parameters of temperature and moisture, CT2 and CQ2, in the lower convective boundary layer were derived from data measured using the Helipod in 2003. The measurements were carried out during the LITFASS03 campaign over a heterogeneous land surface around the boundary-layer field site of the Lindenberg Meteorological Observatory-Richard-Aßmann-Observatory (MOL) of the

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

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

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.

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.

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.

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.

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.

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.

TIME-DEPENDENT TURBULENT HEATING OF OPEN FLUX TUBES IN THE CHROMOSPHERE, CORONA, AND SOLAR WIND

Energy Technology Data Exchange (ETDEWEB)

Woolsey, L. N.; Cranmer, S. R., E-mail: lwoolsey@cfa.harvard.edu [Harvard-Smithsonian Center for Astrophysics, 60 Garden St., Cambridge, MA 02138 (United States)

2015-10-01

We investigate several key questions of plasma heating in open-field regions of the corona that connect to the solar wind. We present results for a model of Alfvén-wave-driven turbulence for three typical open magnetic field structures: a polar coronal hole, an open flux tube neighboring an equatorial streamer, and an open flux tube near a strong-field active region. We compare time-steady, one-dimensional turbulent heating models against fully time-dependent three-dimensional reduced-magnetohydrodynamic modeling of BRAID. We find that the time-steady results agree well with time-averaged results from BRAID. The time dependence allows us to investigate the variability of the magnetic fluctuations and of the heating in the corona. The high-frequency tail of the power spectrum of fluctuations forms a power law whose exponent varies with height, and we discuss the possible physical explanation for this behavior. The variability in the heating rate is bursty and nanoflare-like in nature, and we analyze the amount of energy lost via dissipative heating in transient events throughout the simulation. The average energy in these events is 10{sup 21.91} erg, within the “picoflare” range, and many events reach classical “nanoflare” energies. We also estimated the multithermal distribution of temperatures that would result from the heating-rate variability, and found good agreement with observed widths of coronal differential emission measure distributions. The results of the modeling presented in this paper provide compelling evidence that turbulent heating in the solar atmosphere by Alfvén waves accelerates the solar wind in open flux tubes.

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...

Forest - added Turbulence: A parametric study on Turbulence intensity in and around forests

International Nuclear Information System (INIS)

Pedersen, Henrik Sundgaard; Langreder, Wiebke

2007-01-01

The scope of the investigation is to take on-site measured wind data from a number of sites inside and close to forests. From the collected on-site data the ambient turbulence intensity is calculated and analysed depending on the distance to the forest and height above the forest. From this forest turbulence intensity database it is possible to get an overview of the general behaviour of the turbulence above and down stream from the forest. The database currently consists of 65 measurements points from around the globe, and it will be continually updated as relevant sites are made available. Using the database a number of questions can be answered. How does the ambient turbulence intensity decay with height? What does the turbulence profile look like according to wind speed? Is it the general situation that high wind speeds are creating movement in the canopy tops, resulting in higher turbulence? How does the ambient turbulence intensity decay at different height as a function of distance to the forest? From the forest turbulence database it can be seen that in general, the majority of the turbulence intensity created by the forest is visible within a radius of 5 times the forest height in vertical and 500 meters downstream from the forest edge in horizontal direction. Outside these boundaries the ambient turbulence intensity is rapidly approaching normal values

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.

Flux surface shaping effects on tokamak edge turbulence and flows

Energy Technology Data Exchange (ETDEWEB)

Kendl, A. [Innsbruck Univ., Institut fuer Theoretische Physik, Association EURATOM (Austria); Scott, B.D. [Max-Planck-Institut fuer Plasmaphysik, EURATOM Association, Garching bei Muenchen (Germany)

2004-07-01

The influence of shaping of magnetic flux surfaces in tokamaks on gyro-fluid edge turbulence is studied numerically. Magnetic field shaping in tokamaks is mainly due to elongation, triangularity, shift and the presence of a divertor X-point. A series of tokamak configurations with varying elongation 1 {<=} {kappa} {>=} 2 and triangularity 0 {<=} {delta} {<=} 0.4, and an actual ASDEX Upgrade divertor configuration are obtained with the equilibrium code HELENA and implemented into the gyro-fluid turbulence code GEM. The study finds minimal impact on the zonal flow physics itself, but strong impact on the turbulence and transport. (authors)

Flux surface shaping effects on tokamak edge turbulence and flows

International Nuclear Information System (INIS)

Kendl, A.; Scott, B.D.

2004-01-01

The influence of shaping of magnetic flux surfaces in tokamaks on gyro-fluid edge turbulence is studied numerically. Magnetic field shaping in tokamaks is mainly due to elongation, triangularity, shift and the presence of a divertor X-point. A series of tokamak configurations with varying elongation 1 ≤ κ ≥ 2 and triangularity 0 ≤ δ ≤ 0.4, and an actual ASDEX Upgrade divertor configuration are obtained with the equilibrium code HELENA and implemented into the gyro-fluid turbulence code GEM. The study finds minimal impact on the zonal flow physics itself, but strong impact on the turbulence and transport. (authors)

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…

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.

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.

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 ...

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...

A model for the response of vertical axis wind turbines to turbulent flow: Parts 1 and 2

Science.gov (United States)

Malcolm, D. R.

1988-07-01

This report describes a project intended to incorporate the effects of atmospheric turbulence into the structural response of Darrieus rotor, vertical axis wind turbines. The basis of the technique is the generation of a suitable time series of wind velocities, which are passed through a double multiple streamtube aerodynamic representation of the rotor. The aerodynamic loads are decomposed into components of the real eigenvectors of the rotor and subsequently into full-power and cross-spectral densities. These modal spectra are submitted as input to a modified NASTRAN random load analysis and the power spectra of selected responses are obtained. This procedure appears to be successful. Results at zero turbulence agree with alternative solutions, and when turbulence is included, the predicted stress spectra for the Indal 6400 rotor are in good agreement with field data. The model predicts that the effect of turbulence on harmonic frequency peaks and on all lead-lag bending will not be great. However, it appears that only 11 percent turbulence intensity can almost double the rms of cyclic flatwise blade bending.

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.

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

Time Series Analysis of Wheat Futures Reward in China

Institute of Scientific and Technical Information of China (English)

无

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.

Time series analysis of continuous-wave coherent Doppler Lidar wind measurements

DEFF Research Database (Denmark)

Sjöholm, Mikael; Mikkelsen, Torben; Mann, Jakob

2008-01-01

The influence of spatial volume averaging of a focused 1.55 mu m continuous-wave coherent Doppler Lidar on observed wind turbulence measured in the atmospheric surface layer over homogeneous terrain is described and analysed. Comparison of Lidar-measured turbulent spectra with spectra simultaneou......The influence of spatial volume averaging of a focused 1.55 mu m continuous-wave coherent Doppler Lidar on observed wind turbulence measured in the atmospheric surface layer over homogeneous terrain is described and analysed. Comparison of Lidar-measured turbulent spectra with spectra...

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.

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

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

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...

Simultaneous identification of transfer functions and combustion noise of a turbulent flame

Science.gov (United States)

Merk, M.; Jaensch, S.; Silva, C.; Polifke, W.

2018-05-01

The Large Eddy Simulation/System Identification (LES/SI) approach allows to deduce a flame transfer function (FTF) from LES of turbulent reacting flow: Time series of fluctuations of reference velocity and global heat release rate resulting from broad-band excitation of a simulated turbulent flame are post-processed via SI techniques to derive a low order model of the flame dynamics, from which the FTF is readily deduced. The current work investigates an extension of the established LES/SI approach: In addition to estimation of the FTF, a low order model for the combustion noise source is deduced from the same time series data. By incorporating such a noise model into a linear thermoacoustic model, it is possible to predict the overall level as well as the spectral distribution of sound pressure in confined combustion systems that do not exhibit self-excited thermoacoustic instability. A variety of model structures for estimation of a noise model are tested in the present study. The suitability and quality of these model structures are compared against each other, their sensitivity regarding certain time series properties is studied. The influence of time series length, signal-to-noise ratio as well as acoustic reflection coefficient of the boundary conditions on the identification are examined. It is shown that the Box-Jenkins model structure is superior to simpler approaches for the simultaneous identification of models that describe the FTF as well as the combustion noise source. Subsequent to the question of the most adequate model structure, the choice of optimal model order is addressed, as in particular the optimal parametrization of the noise model is not obvious. Akaike's Information Criterion and a model residual analysis are applied to draw qualitative and quantitative conclusions on the most suitable model order. All investigations are based on a surrogate data model, which allows a Monte Carlo study across a large parameter space with modest

Is Insecurity Worse for Well-Being in Turbulent Times? Mental Health in Context

Science.gov (United States)

Lam, Jack; Fan, Wen; Moen, Phyllis

2014-01-01

Using General Social Survey data, we examine whether any association between job insecurity and well-being is contingent on economic climate (comparing those interviewed in turbulent 2010 vs. pre-recessionary 2006), as well as income and gender. We find respondents with higher levels of job insecurity in 2010 reported lower levels of happiness compared to those similarly insecure in 2006. The positive relationship between job insecurity and days of poor mental health becomes more pronounced for those in the 3rd quartile of personal income in 2010, suggesting middle-class vulnerability during the economic downturn. Men (but not women) with higher insecurity report more days of poor mental health in both 2006 and 2010. These findings reinforce a “cycles of control” theoretical approach, given the mental health-job insecurity relationship is heightened for workers in turbulent times. PMID:25436177

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.

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.

"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).

Premixed autoignition in compressible turbulence

Science.gov (United States)

Konduri, Aditya; Kolla, Hemanth; Krisman, Alexander; Chen, Jacqueline

2016-11-01

Prediction of chemical ignition delay in an autoignition process is critical in combustion systems like compression ignition engines and gas turbines. Often, ignition delay times measured in simple homogeneous experiments or homogeneous calculations are not representative of actual autoignition processes in complex turbulent flows. This is due the presence of turbulent mixing which results in fluctuations in thermodynamic properties as well as chemical composition. In the present study the effect of fluctuations of thermodynamic variables on the ignition delay is quantified with direct numerical simulations of compressible isotropic turbulence. A premixed syngas-air mixture is used to remove the effects of inhomogeneity in the chemical composition. Preliminary results show a significant spatial variation in the ignition delay time. We analyze the topology of autoignition kernels and identify the influence of extreme events resulting from compressibility and intermittency. The dependence of ignition delay time on Reynolds and turbulent Mach numbers is also quantified. Supported by Basic Energy Sciences, Dept of Energy, United States.

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.

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.

Analysis of chaos in plasma turbulence

DEFF Research Database (Denmark)

Pedersen, T.S.; Michelsen, Poul; Juul Rasmussen, J.

1996-01-01

-stationary turbulent state is reached in a finite time, independent of the initial conditions. Different regimes of the turbulent state can be obtained by varying the coupling parameter C, related to the parallel electron dynamics. The turbulence is described by using particle tracking and tools from chaos analysis...

Statistical properties of turbulence in a toroidal magnetized ECR plasma

International Nuclear Information System (INIS)

Yu Yi; Lu Ronghua; Wang Zhijiang; Wen Yizhi; Yu Changxuan; Wan Shude; Liu, Wandong

2008-01-01

The statistical analyses of fluctuation data measured by electrostatic-probe arrays clearly show that the self-organized criticality (SOC) avalanches are not the dominant behaviors in a toroidal ECR plasma in the SMT (Simple Magnetic Torus) mode of KT-5D device. The f -1 index region in the auto-correlation spectra of the floating potential V f and the ion saturation current I s , which is a fingerprint of a SOC system, ranges only in a narrow frequency band. By investigating the Hurst exponents at increasingly coarse grained time series, we find that at a time scale of τ>100 μs, there exists no or a very weak long-range correlation over two decades in τ. The difference between the PDFs of I s and V f clearly shows a more global nature of the latter. The transport flux induced by the turbulence suggests that the natural intermittency of turbulent transport maybe independent of the avalanche induced by near criticality. The drift instability is dominant in a SMT plasma generated by means of ECR discharges

Cosmic turbulence

International Nuclear Information System (INIS)

Drury, L.O.; Stewart, J.M.

1976-01-01

A generalization of a transformation due to Kurskov and Ozernoi is used to rewrite the usual equations governing subsonic turbulence in Robertson-Walker cosmological models as Navier-Stokes equations with a time-dependent viscosity. This paper first rederives some well-known results in a very simple way by means of this transformation. The main result however is that the establishment of a Kolmogorov spectrum at recombination appears to be incompatible with subsonic turbulence. The conditions after recombination are also discussed briefly. (author)

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.

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 ...

Relevant criteria for testing the quality of turbulence models

DEFF Research Database (Denmark)

Frandsen, Sten Tronæs; Ejsing Jørgensen, Hans; Sørensen, J.D.

2007-01-01

Seeking relevant criteria for testing the quality of turbulence models, the scale of turbulence and the gust factor have been estimated from data and compared with predictions from first-order models of these two quantities. It is found that the mean of the measured length scales is approx. 10......% smaller than the IEC model, for wind turbine hub height levels. The mean is only marginally dependent on trends in time series. It is also found that the coefficient of variation of the measured length scales is about 50%. 3sec and 10sec pre-averaging of wind speed data are relevant for MW-size wind...... turbines when seeking wind characteristics that correspond to one blade and the entire rotor, respectively. For heights exceeding 50-60m the gust factor increases with wind speed. For heights larger the 60-80m, present assumptions on the value of the gust factor are significantly conservative, both for 3...

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...

Zero-crossing statistics for non-Markovian time series.

Science.gov (United States)

Nyberg, Markus; Lizana, Ludvig; Ambjörnsson, Tobias

2018-03-01

In applications spanning from image analysis and speech recognition to energy dissipation in turbulence and time-to failure of fatigued materials, researchers and engineers want to calculate how often a stochastic observable crosses a specific level, such as zero. At first glance this problem looks simple, but it is in fact theoretically very challenging, and therefore few exact results exist. One exception is the celebrated Rice formula that gives the mean number of zero crossings in a fixed time interval of a zero-mean Gaussian stationary process. In this study we use the so-called independent interval approximation to go beyond Rice's result and derive analytic expressions for all higher-order zero-crossing cumulants and moments. Our results agree well with simulations for the non-Markovian autoregressive model.

Zero-crossing statistics for non-Markovian time series

Science.gov (United States)

Nyberg, Markus; Lizana, Ludvig; Ambjörnsson, Tobias

2018-03-01

In applications spanning from image analysis and speech recognition to energy dissipation in turbulence and time-to failure of fatigued materials, researchers and engineers want to calculate how often a stochastic observable crosses a specific level, such as zero. At first glance this problem looks simple, but it is in fact theoretically very challenging, and therefore few exact results exist. One exception is the celebrated Rice formula that gives the mean number of zero crossings in a fixed time interval of a zero-mean Gaussian stationary process. In this study we use the so-called independent interval approximation to go beyond Rice's result and derive analytic expressions for all higher-order zero-crossing cumulants and moments. Our results agree well with simulations for the non-Markovian autoregressive model.

System for simulating fluctuation diagnostics for application to turbulence computations

International Nuclear Information System (INIS)

Bravenec, R.V.; Nevins, W.M.

2006-01-01

Present-day nonlinear microstability codes are able to compute the saturated fluctuations of a turbulent fluid versus space and time, whether the fluid be liquid, gas, or plasma. They are therefore able to determine turbulence-induced fluid (or particle) and energy fluxes. These codes, however, must be tested against experimental data not only with respect to transport but also characteristics of the fluctuations. The latter is challenging because of limitations in the diagnostics (e.g., finite spatial resolution) and the fact that the diagnostics typically do not measure exactly the quantities that the codes compute. In this work, we present a system based on IDL registered analysis and visualization software in which user-supplied 'diagnostic filters' are applied to the code outputs to generate simulated diagnostic signals. The same analysis techniques as applied to the measurements, e.g., digital time-series analysis, may then be applied to the synthesized signals. Their statistical properties, such as rms fluctuation level, mean wave numbers, phase and group velocities, correlation lengths and times, and in some cases full S(k,ω) spectra, can then be compared directly to those of the measurements

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

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.

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

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

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

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...

The plasma transport equations derived by multiple time-scale expansions and turbulent transport. I. General theory

International Nuclear Information System (INIS)

Edenstrasser, J.W.

1995-01-01

A multiple time-scale derivative expansion scheme is applied to the dimensionless Fokker--Planck equation and to Maxwell's equations, where the parameter range of a typical fusion plasma was assumed. Within kinetic theory, the four time scales considered are those of Larmor gyration, particle transit, collisions, and classical transport. The corresponding magnetohydrodynamic (MHD) time scales are those of ion Larmor gyration, Alfven, MHD collision, and resistive diffusion. The solution of the zeroth-order equations results in the force-free equilibria and ideal Ohm's law. The solution of the first-order equations leads under the assumption of a weak collisional plasma to the ideal MHD equations. On the MHD-collision time scale, not only the full set of the MHD transport equations is obtained, but also turbulent terms, where the related transport quantities are one order in the expansion parameter larger than those of classical transport. Finally, at the resistive diffusion time scale the known transport equations are arrived at including, however, also turbulent contributions. copyright 1995 American Institute of Physics

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...

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.

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...

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

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 ...

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.

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

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...

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.

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.

MULTIFLUID MAGNETOHYDRODYNAMIC TURBULENT DECAY

International Nuclear Information System (INIS)

Downes, T. P.; O'Sullivan, S.

2011-01-01

It is generally believed that turbulence has a significant impact on the dynamics and evolution of molecular clouds and the star formation that occurs within them. Non-ideal magnetohydrodynamic (MHD) effects are known to influence the nature of this turbulence. We present the results of a suite of 512 3 resolution simulations of the decay of initially super-Alfvenic and supersonic fully multifluid MHD turbulence. We find that ambipolar diffusion increases the rate of decay of the turbulence while the Hall effect has virtually no impact. The decay of the kinetic energy can be fitted as a power law in time and the exponent is found to be -1.34 for fully multifluid MHD turbulence. The power spectra of density, velocity, and magnetic field are all steepened significantly by the inclusion of non-ideal terms. The dominant reason for this steepening is ambipolar diffusion with the Hall effect again playing a minimal role except at short length scales where it creates extra structure in the magnetic field. Interestingly we find that, at least at these resolutions, the majority of the physics of multifluid turbulence can be captured by simply introducing fixed (in time and space) resistive terms into the induction equation without the need for a full multifluid MHD treatment. The velocity dispersion is also examined and, in common with previously published results, it is found not to be power law in nature.

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.

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

Steady turbulent flow in curved rectangular channels

NARCIS (Netherlands)

De Vriend, H.J.

1979-01-01

After the study of fully developed and developing steady laminar flow in curved channels of shallow rectangular wet cross-section (see earlier reports in this series), steady turbulent flow in such channels is investigated as a next step towards a mathematical model of the flow in shallow river

Comparison of turbulence in a transitional boundary layer to turbulence in a developed boundary layer*

Science.gov (United States)

Park, G. I.; Wallace, J.; Wu, X.; Moin, P.

2010-11-01

Using a recent DNS of a flat-plate boundary layer, statistics of turbulence in transition at Reθ= 500 where spots merge (distributions of the mean velocity, rms velocity and vorticity fluctuations, Reynolds shear stress, kinetic energy production and dissipation rates and enstrophy) have been compared to these statistics for the developed boundary layer turbulence at Reθ= 1850. When the distributions in the transitional region, determined in narrow planes 0.03 Reθ wide, exclude regions and times when the flow is not turbulent, they closely resemble those in the developed turbulent state at the higher Reynolds number, especially in the buffer and sublayers. The skin friction coefficient, determined in this conditional manner in the transitional flow is, of course, much larger than that obtained by including both turbulent and non-turbulent information there, and is consistent with a value obtained by extrapolating from the developed turbulent region. We are attempting to perform this data analysis even further upstream in the transitioning flow at Reθ= 300 where the turbulent spots are individuated. These results add further evidence to support the view that the structure of a developed turbulent boundary layer is little different from its structure in its embryonic form in turbulent spots. *CTR 2010 Summer Program research.

Consequences of variations in spatial turbulence characteristics for fatigue life time of wind turbines

Energy Technology Data Exchange (ETDEWEB)

Larsen, G.C.

1998-09-01

The fatigue loading of turbines situated in complex terrain is investigated in order to determine the crucial parameters in the spatial structure of the turbulence in such situations. The parameter study is performed by means of numerical calculations, and it embraces three different wind turbine types, representing a pitch controlled concept, a stall controlled concept, and a stall controlled concept with an extremely flexible tower. For each of the turbine concepts, the fatigue load sensibility to the selected turbulence characteristics are investigated for three different mean wind speeds at hub height. The selected mean wind speeds represent the linear-, the stall-, and the post stall aerodynamic region for the stall controlled turbines and analogously the unregulated-, the partly regulated-, and the fully regulated regime for the pitch controlled turbine. Denoting the turbulence component in the mean wind direction by u, the lateral turbulence component by v, and the vertical turbulence component by w, the selected turbulence characteristics comprise the u-turbulence length scale, the ratio between the v- and w-turbulence intensities and the u-turbulence intensity, the uu-coherence decay factor, and finally the u-v and u-w cross-correlations. The turbulence length scale in the mean wind direction gives rise to significant modification of the fatigue loading on all the investigated wind turbine concepts, but for the other selected parameter variations, large individual differences exists between the turbines. With respect to sensitivity to the performed parameter variations, the Vestas V39 wind turbine is the most robust of the investigated turbines. The Nordtank 500/37 turbine, equipped with the (artificial) soft tower, is by far the most sensitive of the investigated turbine concepts - also much more sensitive than the conventional Nordtank 500/37 turbine equipped with a traditional tower. (au) 2 tabs., 43 ills., 7 refs.

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.

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

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.

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...

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...

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.

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.

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…

On the ""early-time"" evolution of variables relevant to turbulence models for Rayleigh-Taylor instability

Energy Technology Data Exchange (ETDEWEB)

Rollin, Bertrand [Los Alamos National Laboratory; Andrews, Malcolm J [Los Alamos National Laboratory

2010-01-01

We present our progress toward setting initial conditions in variable density turbulence models. In particular, we concentrate our efforts on the BHR turbulence model for turbulent Rayleigh-Taylor instability. Our approach is to predict profiles of relevant parameters before the fully turbulent regime and use them as initial conditions for the turbulence model. We use an idealized model of the mixing between two interpenetrating fluids to define the initial profiles for the turbulence model parameters. Velocities and volume fractions used in the idealized mixing model are obtained respectively from a set of ordinary differential equations modeling the growth of the Rayleigh-Taylor instability and from an idealization of the density profile in the mixing layer. A comparison between predicted initial profiles for the turbulence model parameters and initial profiles of the parameters obtained from low Atwood number three dimensional simulations show reasonable agreement.