Engineering report : technical review of the GPSI model G3000 series multi-sensor controller system
Energy Technology Data Exchange (ETDEWEB)
Gibson, S.; Campbell, I. [Gas Protection Systems Inc., Maple Ridge, BC (Canada)
2001-12-01
The Enviro Sentry 24/7 model G3002 remote computer was developed by Gas Production Services Inc. (GPSI). This low cost Universal Detector/Controller consists of a stand-alone, scalable network connected to a daisy-chain topology. M. Collyer reviewed the capabilities of the GPSI model G3000 series multi-sensor controller system from an engineering perspective and presented an independent opinion on its performance and operation. Its evaluation was based on a randomly selected production unit supplied by GPSI. M. Collyer used widely used industry principles, electronic laboratory testing methods, prepared schematic diagrams, and presented operator ratings. The strength of the system's viability in the global market is that its use is not constrained by regulations or standards of any country, province, state or region. It can be used to provide solutions for air quality, protection and energy management. In particular, the G3000 series provides continuous protection and intelligent management in combustible gas detection; seismic risk mitigation; toxic gas early detection; air quality management; and, energy conservation. 1 tab.
Technical Report Series on Global Modeling and Data Assimilation, Volume 41 : GDIS Workshop Report
Koster, Randal D. (Editor); Schubert, Siegfried; Pozzi, Will; Mo, Kingtse; Wood, Eric F.; Stahl, Kerstin; Hayes, Mike; Vogt, Juergen; Seneviratne, Sonia; Stewart, Ron;
2015-01-01
The workshop "An International Global Drought Information System Workshop: Next Steps" was held on 10-13 December 2014 in Pasadena, California. The more than 60 participants from 15 countries spanned the drought research community and included select representatives from applications communities as well as providers of regional and global drought information products. The workshop was sponsored and supported by the US National Integrated Drought Information System (NIDIS) program, the World Climate Research Program (WCRP: GEWEX, CLIVAR), the World Meteorological Organization (WMO), the Group on Earth Observations (GEO), the European Commission Joint Research Centre (JRC), the US Climate Variability and Predictability (CLIVAR) program, and the US National Oceanic and Atmospheric Administration (NOAA) programs on Modeling, Analysis, Predictions and Projections (MAPP) and Climate Variability & Predictability (CVP). NASA/JPL hosted the workshop with logistical support provided by the GEWEX program office. The goal of the workshop was to build on past Global Drought Information System (GDIS) progress toward developing an experimental global drought information system. Specific goals were threefold: (i) to review recent research results focused on understanding drought mechanisms and their predictability on a wide range of time scales and to identify gaps in understanding that could be addressed by coordinated research; (ii) to help ensure that WRCP research priorities mesh with efforts to build capacity to address drought at the regional level; and (iii) to produce an implementation plan for a short duration pilot project to demonstrate current GDIS capabilities. See http://www.wcrp-climate.org/gdis-wkshp-2014-objectives for more information.
Technical Manual: 2002 Series GED Tests
Ezzelle, Carol; Setzer, J. Carl
2009-01-01
This manual was written to provide technical information regarding the 2002 Series GED (General Educational Development) Tests. Throughout this manual, documentation is provided regarding the development of the GED Tests, data collection activities, as well as reliability and validity evidence. The purpose of this manual is to provide evidence…
DIY Solar Market Analysis Webinar Series: Solar Resource and Technical
Series: Solar Resource and Technical Potential DIY Solar Market Analysis Webinar Series: Solar Resource and Technical Potential Wednesday, June 11, 2014 As part of a Do-It-Yourself Solar Market Analysis Potential | State, Local, and Tribal Governments | NREL DIY Solar Market Analysis Webinar
Koster, Randal D. (Editor); Bosilovich, Michael G.; Akella, Santha; Lawrence, Coy; Cullather, Richard; Draper, Clara; Gelaro, Ronald; Kovach, Robin; Liu, Qing; Molod, Andrea;
2015-01-01
The years since the introduction of MERRA have seen numerous advances in the GEOS-5 Data Assimilation System as well as a substantial decrease in the number of observations that can be assimilated into the MERRA system. To allow continued data processing into the future, and to take advantage of several important innovations that could improve system performance, a decision was made to produce MERRA-2, an updated retrospective analysis of the full modern satellite era. One of the many advances in MERRA-2 is a constraint on the global dry mass balance; this allows the global changes in water by the analysis increment to be near zero, thereby minimizing abrupt global interannual variations due to changes in the observing system. In addition, MERRA-2 includes the assimilation of interactive aerosols into the system, a feature of the Earth system absent from previous reanalyses. Also, in an effort to improve land surface hydrology, observations-corrected precipitation forcing is used instead of model-generated precipitation. Overall, MERRA-2 takes advantage of numerous updates to the global modeling and data assimilation system. In this document, we summarize an initial evaluation of the climate in MERRA-2, from the surface to the stratosphere and from the tropics to the poles. Strengths and weaknesses of the MERRA-2 climate are accordingly emphasized.
Reviving Graduate Seminar Series through Non-Technical Presentations
Madihally, Sundararajan V.
2011-01-01
Most chemical engineering programs that offer M.S. and Ph.D. degrees have a common seminar series for all the graduate students. Many would agree that seminars lack student interest, leading to ineffectiveness. We questioned the possibility of adding value to the seminar series by incorporating non-technical topics that may be more important to…
SERI biomass program annual technical report: 1982
Energy Technology Data Exchange (ETDEWEB)
Bergeron, P.W.; Corder, R.E.; Hill, A.M.; Lindsey, H.; Lowenstein, M.Z.
1983-02-01
The biomass with which this report is concerned includes aquatic plants, which can be converted into liquid fuels and chemicals; organic wastes (crop residues as well as animal and municipal wastes), from which biogas can be produced via anerobic digestion; and organic or inorganic waste streams, from which hydrogen can be produced by photobiological processes. The Biomass Program Office supports research in three areas which, although distinct, all use living organisms to create the desired products. The Aquatic Species Program (ASP) supports research on organisms that are themselves processed into the final products, while the Anaerobic Digestion (ADP) and Photo/Biological Hydrogen Program (P/BHP) deals with organisms that transform waste streams into energy products. The P/BHP is also investigating systems using water as a feedstock and cell-free systems which do not utilize living organisms. This report summarizes the progress and research accomplishments of the SERI Biomass Program during FY 1982.
Introduction to Time Series Modeling
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
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)
Manhattan Project Technical Series: The Chemistry of Uranium (I)
International Nuclear Information System (INIS)
Rabinowitch, E. I.; Katz, J. J.
1947-01-01
This constitutes Chapters 11 through 16, inclusive, of the Survey Volume on Uranium Chemistry prepared for the Manhattan Project Technical Series. Chapters are titled: Uranium Oxides, Sulfides, Selenides, and Tellurides; The Non-Volatile Fluorides of Uranium; Uranium Hexafluoride; Uranium-Chlorine Compounds; Bromides, Iodides, and Pseudo-Halides of Uranium; and Oxyhalides of Uranium.
Manhattan Project Technical Series: The Chemistry of Uranium (I)
Energy Technology Data Exchange (ETDEWEB)
Rabinowitch, E. I. [Argonne National Lab. (ANL), Argonne, IL (United States); Katz, J. J. [Argonne National Lab. (ANL), Argonne, IL (United States)
1947-03-10
This constitutes Chapters 11 through 16, inclusive, of the Survey Volume on Uranium Chemistry prepared for the Manhattan Project Technical Series. Chapters are titled: Uranium Oxides, Sulfides, Selenides, and Tellurides; The Non-Volatile Fluorides of Uranium; Uranium Hexafluoride; Uranium-Chlorine Compounds; Bromides, Iodides, and Pseudo-Halides of Uranium; and Oxyhalides of Uranium.
Gregg, Watson W.; Casey, Nancy W.; Rousseaux, Cecile S.
2013-01-01
MERRA products were used to force an established ocean biogeochemical model to estimate surface carbon inventories and fluxes in the global oceans. The results were compared to public archives of in situ carbon data and estimates. The model exhibited skill for ocean dissolved inorganic carbon (DIC), partial pressure of ocean CO2 (pCO2) and air-sea fluxes (FCO2). The MERRA-forced model produced global mean differences of 0.02% (approximately 0.3 microns) for DIC, -0.3% (about -1.2 (micro) atm; model lower) for pCO2, and -2.3% (-0.003 mol C/sq m/y) for FCO2 compared to in situ estimates. Basin-scale distributions were significantly correlated with observations for all three variables (r=0.97, 0.76, and 0.73, P<0.05, respectively for DIC, pCO2, and FCO2). All major oceanographic basins were represented as sources to the atmosphere or sinks in agreement with in situ estimates. However, there were substantial basin-scale and local departures.
Suarex, Max J. (Editor); Chou, Ming-Dah
1994-01-01
A detailed description of a parameterization for thermal infrared radiative transfer designed specifically for use in global climate models is presented. The parameterization includes the effects of the main absorbers of terrestrial radiation: water vapor, carbon dioxide, and ozone. While being computationally efficient, the schemes compute very accurately the clear-sky fluxes and cooling rates from the Earth's surface to 0.01 mb. This combination of accuracy and speed makes the parameterization suitable for both tropospheric and middle atmospheric modeling applications. Since no transmittances are precomputed the atmospheric layers and the vertical distribution of the absorbers may be freely specified. The scheme can also account for any vertical distribution of fractional cloudiness with arbitrary optical thickness. These features make the parameterization very flexible and extremely well suited for use in climate modeling studies. In addition, the numerics and the FORTRAN implementation have been carefully designed to conserve both memory and computer time. This code should be particularly attractive to those contemplating long-term climate simulations, wishing to model the middle atmosphere, or planning to use a large number of levels in the vertical.
Koster, Randal D. (Editor); Rousseaux, Cecile Severine; Gregg, Watson W.
2014-01-01
In this paper, we investigated whether the assimilation of remotely-sensed chlorophyll data can improve the estimates of air-sea carbon dioxide fluxes (FCO2). Using a global, established biogeochemical model (NASA Ocean Biogeochemical Model, NOBM) for the period 2003-2010, we found that the global FCO2 values produced in the free-run and after assimilation were within -0.6 mol C m(sup -2) y(sup -1) of the observations. The effect of satellite chlorophyll assimilation was assessed in 12 major oceanographic regions. The region with the highest bias was the North Atlantic. Here the model underestimated the fluxes by 1.4 mol C m(sup -2) y(sup -1) whereas all the other regions were within 1 mol C m(sup -2) y(sup -1) of the data. The FCO2 values were not strongly impacted by the assimilation, and the uncertainty in FCO2 was not decreased, despite the decrease in the uncertainty in chlorophyll concentration. Chlorophyll concentrations were within approximately 25% of the database in 7 out of the 12 regions, and the assimilation improved the chlorophyll concentration in the regions with the highest bias by 10-20%. These results suggest that the assimilation of chlorophyll data does not considerably improve FCO2 estimates and that other components of the carbon cycle play a role that could further improve our FCO2 estimates.
Models for dependent time series
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
Stochastic models for time series
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 ...
FOURIER SERIES MODELS THROUGH TRANSFORMATION
African Journals Online (AJOL)
DEPT
monthly temperature data (1996 – 2005) collected from the National Root ... KEY WORDS: Fourier series, square transformation, multiplicative model, ... fluctuations or movements are often periodic(Ekpeyong,2005). .... significant trend or not, if the trend is not significant, the grand mean may be used as an estimate of trend.
Patt, Frederick S.; Hoisington, Charles M.; Gregg, Watson W.; Coronado, Patrick L.; Hooker, Stanford B. (Editor); Firestone, Elaine R. (Editor); Indest, A. W. (Editor)
1993-01-01
An analysis of orbit propagation models was performed by the Mission Operations element of the Sea-viewing Wide Field-of-View Sensor (SeaWiFS) Project, which has overall responsibility for the instrument scheduling. The orbit propagators selected for this analysis are widely available general perturbations models. The analysis includes both absolute accuracy determination and comparisons of different versions of the models. The results show that all of the models tested meet accuracy requirements for scheduling and data acquisition purposes. For internal Project use the SGP4 propagator, developed by the North American Air Defense (NORAD) Command, has been selected. This model includes atmospheric drag effects and, therefore, provides better accuracy. For High Resolution Picture Transmission (HRPT) ground stations, which have less stringent accuracy requirements, the publicly available Brouwer-Lyddane models are recommended. The SeaWiFS Project will make available portable source code for a version of this model developed by the Data Capture Facility (DCF).
Symptomatic thoracic spinal cord herniation: case series and technical report.
Hawasli, Ammar H; Ray, Wilson Z; Wright, Neill M
2014-09-01
Idiopathic spinal cord herniation (ISCH) is an uncommon condition located predominantly in the thoracic spine and often associated with a remote history of a major traumatic injury. ISCH has an incompletely described presentation and unknown etiology. There is no consensus on the treatment algorithm and surgical technique, and there are few data on clinical outcomes. In this case series and technical report, we describe the atypical myelopathy presentation, remote history of traumatic injury, radiographic progression, treatment, and outcomes of 5 patients treated at Washington University for symptomatic ISCH. A video showing surgical repair is presented. In contrast to classic compressive myelopathy symptomatology, ISCH patients presented with an atypical myelopathy, characterized by asymmetric motor and sensory deficits and early-onset urinary incontinence. Clinical deterioration correlated with progressive spinal cord displacement and herniation observed on yearly spinal imaging in a patient imaged serially because of multiple sclerosis. Finally, compared with compressive myelopathy in the thoracic spine, surgical treatment of ISCH led to rapid improvement despite a long duration of symptoms. Symptomatic ISCH presents with atypical myelopathy and slow temporal progression and can be successfully managed with surgical repair.
Multiple Indicator Stationary Time Series Models.
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…
Provencal, Simon; Kishcha, Pavel; Elhacham, Emily; daSilva, Arlindo M.; Alpert, Pinhas; Suarez, Max J.
2014-01-01
NASA's Global Modeling and Assimilation Office has extended the Modern-Era Retrospective Analysis for Research and Application (MERRA) tool with five atmospheric aerosol species (sulfates, organic carbon, black carbon, mineral dust and sea salt). This inclusion of aerosol reanalysis data is now known as MERRAero. This study analyses a ten-year period (July 2002 - June 2012) MERRAero aerosol reanalysis applied to the study of aerosol optical depth (AOD) and its trends for the aforementioned aerosol species over the world's major cities (with a population of over 2 million inhabitants). We found that a proportion of various aerosol species in total AOD exhibited a geographical dependence. Cities in industrialized regions (North America, Europe, central and eastern Asia) are characterized by a strong proportion of sulfate aerosols. Organic carbon aerosols are dominant over cities which are located in regions where biomass burning frequently occurs (South America and southern Africa). Mineral dust dominates other aerosol species in cities located in proximity to the major deserts (northern Africa and western Asia). Sea salt aerosols are prominent in coastal cities but are dominant aerosol species in very few of them. AOD trends are declining over cities in North America, Europe and Japan, as a result of effective air quality regulation. By contrast, the economic boom in China and India has led to increasing AOD trends over most cities in these two highly-populated countries. Increasing AOD trends over cities in the Middle East are caused by increasing desert dust.
Women and Technical Professions. Leonardo da Vinci Series: Good Practices.
Commission of the European Communities, Brussels (Belgium). Directorate-General for Education and Culture.
This document profiles programs for women in technical professions that are offered through the European Commission's Leonardo da Vinci program. The following programs are profiled: (1) Artemis and Diana (vocational guidance programs to help direct girls toward technology-related careers); (2) CEEWIT (an Internet-based information and…
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 modeling, computation, and inference
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
Modelling conditional heteroscedasticity in nonstationary series
Cizek, P.; Cizek, P.; Härdle, W.K.; Weron, R.
2011-01-01
A vast amount of econometrical and statistical research deals with modeling financial time series and their volatility, which measures the dispersion of a series at a point in time (i.e., conditional variance). Although financial markets have been experiencing many shorter and longer periods of
Technical discussions on Emissions and Atmospheric Modeling (TEAM)
Frost, G. J.; Henderson, B.; Lefer, B. L.
2017-12-01
A new informal activity, Technical discussions on Emissions and Atmospheric Modeling (TEAM), aims to improve the scientific understanding of emissions and atmospheric processes by leveraging resources through coordination, communication and collaboration between scientists in the Nation's environmental agencies. TEAM seeks to close information gaps that may be limiting emission inventory development and atmospheric modeling and to help identify related research areas that could benefit from additional coordinated efforts. TEAM is designed around webinars and in-person meetings on particular topics that are intended to facilitate active and sustained informal communications between technical staff at different agencies. The first series of TEAM webinars focuses on emissions of nitrogen oxides, a criteria pollutant impacting human and ecosystem health and a key precursor of ozone and particulate matter. Technical staff at Federal agencies with specific interests in emissions and atmospheric modeling are welcome to participate in TEAM.
Modeling seasonality in bimonthly time series
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
Modeling of Volatility with Non-linear Time Series Model
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.
Bootstrapping a time series model
International Nuclear Information System (INIS)
Son, M.S.
1984-01-01
The bootstrap is a methodology for estimating standard errors. The idea is to use a Monte Carlo simulation experiment based on a nonparametric estimate of the error distribution. The main objective of this dissertation was to demonstrate the use of the bootstrap to attach standard errors to coefficient estimates and multi-period forecasts in a second-order autoregressive model fitted by least squares and maximum likelihood estimation. A secondary objective of this article was to present the bootstrap in the context of two econometric equations describing the unemployment rate and individual income tax in the state of Oklahoma. As it turns out, the conventional asymptotic formulae (both the least squares and maximum likelihood estimates) for estimating standard errors appear to overestimate the true standard errors. But there are two problems: 1) the first two observations y 1 and y 2 have been fixed, and 2) the residuals have not been inflated. After these two factors are considered in the trial and bootstrap experiment, both the conventional maximum likelihood and bootstrap estimates of the standard errors appear to be performing quite well. At present, there does not seem to be a good rule of thumb for deciding when the conventional asymptotic formulae will give acceptable results
Time series modeling in traffic safety research.
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.
Building Chaotic Model From Incomplete Time Series
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
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...
SAM Photovoltaic Model Technical Reference
Energy Technology Data Exchange (ETDEWEB)
Gilman, P. [National Renewable Energy Laboratory (NREL), Golden, CO (United States)
2015-05-27
This manual describes the photovoltaic performance model in the System Advisor Model (SAM). The U.S. Department of Energy’s National Renewable Energy Laboratory maintains and distributes SAM, which is available as a free download from https://sam.nrel.gov. These descriptions are based on SAM 2015.1.30 (SSC 41).
MODELS CONCERNING PREVENTIVE VERIFICATION OF TECHNICAL EQUIPMENT
Directory of Open Access Journals (Sweden)
CÂRLAN M.
2016-12-01
Full Text Available The paper presents three operative models whose purpose is to improve the practice of preventive maintenance to a wide range of technical installations. Although the calculation criteria are different, the goal is the same: to determine the optimum time between two consecutive preventive interventions. The optimum criteria of these models are: - the maximum share of technical entity operating probabilities, in the case of the Ackoff - Sasieni [1] method; -the optimum time interval for preventive verification depending on the preventive-corrective maintenance costs imposed by the deciding factor, for the AsturioBaldin [2] model; - the minimum number of renewals – preventive and/or corrective maintenance operations [3
Modeling vector nonlinear time series using POLYMARS
de Gooijer, J.G.; Ray, B.K.
2003-01-01
A modified multivariate adaptive regression splines method for modeling vector nonlinear time series is investigated. The method results in models that can capture certain types of vector self-exciting threshold autoregressive behavior, as well as provide good predictions for more general vector
Forecasting with periodic autoregressive time series models
Ph.H.B.F. Franses (Philip Hans); R. Paap (Richard)
1999-01-01
textabstractThis paper is concerned with forecasting univariate seasonal time series data using periodic autoregressive models. We show how one should account for unit roots and deterministic terms when generating out-of-sample forecasts. We illustrate the models for various quarterly UK consumption
Series-produced Helium II Cryostats for the LHC Magnets Technical Choices, Industrialisation, Costs
Poncet, A
2008-01-01
Assembled in 8 continuous segments of approximately 2.7 km length each, the He II cryostats for the 1232 cryodipoles and 474 Short Straight Sections (SSS housing the quadrupoles) must fulfil tight technical requirements. They have been produced by industry in large series according to cost-effective industrial production methods to keep expenditure within the financial constraints of the project and assembled under contract at CERN. The specific technical requirements of the generic systems of the cryostat (vacuum, cryogenic, electrical distribution, magnet alignment) are briefly recalled, as well as the basic design choices leading to the definition of their components (vacuum vessels, thermal shielding, supporting systems). Early in the design process emphasis was placed on the feasibility of manufacturing techniques adequate for large series production of components, optimal tooling for time-effective assembly methods, and reliable quality assurance systems. An analytical review of the costs of the cryosta...
Manhattan Project Technical Series The Chemistry of Uranium (I) Chapters 1-10
International Nuclear Information System (INIS)
Rabinowitch, E. I.; Katz, J. J.
1946-01-01
This constitutes Chapters 1 through 10. inclusive, of The Survey Volume on Uranium Chemistry prepared for the Manhattan Project Technical Series. Chapters are titled: Nuclear Properties of Uranium; Properties of the Uranium Atom; Uranium in Nature; Extraction of Uranium from Ores and Preparation of Uranium Metal; Physical Properties of Uranium Metal; Chemical Properties of Uranium Metal; Intermetallic Compounds and Alloy systems of Uranium; the Uranium-Hydrogen System; Uranium Borides, Carbides, and Silicides; Uranium Nitrides, Phosphides, Arsenides, and Antimonides.
On modeling panels of time series
Ph.H.B.F. Franses (Philip Hans)
2002-01-01
textabstractThis paper reviews research issues in modeling panels of time series. Examples of this type of data are annually observed macroeconomic indicators for all countries in the world, daily returns on the individual stocks listed in the S&P500, and the sales records of all items in a
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...
Outlier Detection in Structural Time Series Models
DEFF Research Database (Denmark)
Marczak, Martyna; Proietti, Tommaso
investigate via Monte Carlo simulations how this approach performs for detecting additive outliers and level shifts in the analysis of nonstationary seasonal time series. The reference model is the basic structural model, featuring a local linear trend, possibly integrated of order two, stochastic seasonality......Structural change affects the estimation of economic signals, like the underlying growth rate or the seasonally adjusted series. An important issue, which has attracted a great deal of attention also in the seasonal adjustment literature, is its detection by an expert procedure. The general......–to–specific approach to the detection of structural change, currently implemented in Autometrics via indicator saturation, has proven to be both practical and effective in the context of stationary dynamic regression models and unit–root autoregressions. By focusing on impulse– and step–indicator saturation, we...
Modeling Non-Gaussian Time Series with Nonparametric Bayesian Model.
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.
Estimating High-Dimensional Time Series Models
DEFF Research Database (Denmark)
Medeiros, Marcelo C.; Mendes, Eduardo F.
We study the asymptotic properties of the Adaptive LASSO (adaLASSO) in sparse, high-dimensional, linear time-series models. We assume both the number of covariates in the model and candidate variables can increase with the number of observations and the number of candidate variables is, possibly......, larger than the number of observations. We show the adaLASSO consistently chooses the relevant variables as the number of observations increases (model selection consistency), and has the oracle property, even when the errors are non-Gaussian and conditionally heteroskedastic. A simulation study shows...
FRAM Modelling Complex Socio-technical Systems
Hollnagel, Erik
2012-01-01
There has not yet been a comprehensive method that goes behind 'human error' and beyond the failure concept, and various complicated accidents have accentuated the need for it. The Functional Resonance Analysis Method (FRAM) fulfils that need. This book presents a detailed and tested method that can be used to model how complex and dynamic socio-technical systems work, and understand both why things sometimes go wrong but also why they normally succeed.
From Taylor series to Taylor models
International Nuclear Information System (INIS)
Berz, Martin
1997-01-01
An overview of the background of Taylor series methods and the utilization of the differential algebraic structure is given, and various associated techniques are reviewed. The conventional Taylor methods are extended to allow for a rigorous treatment of bounds for the remainder of the expansion in a similarly universal way. Utilizing differential algebraic and functional analytic arguments on the set of Taylor models, arbitrary order integrators with rigorous remainder treatment are developed. The integrators can meet pre-specified accuracy requirements in a mathematically strict way, and are a stepping stone towards fully rigorous estimates of stability of repetitive systems
From Taylor series to Taylor models
International Nuclear Information System (INIS)
Berz, M.
1997-01-01
An overview of the background of Taylor series methods and the utilization of the differential algebraic structure is given, and various associated techniques are reviewed. The conventional Taylor methods are extended to allow for a rigorous treatment of bounds for the remainder of the expansion in a similarly universal way. Utilizing differential algebraic and functional analytic arguments on the set of Taylor models, arbitrary order integrators with rigorous remainder treatment are developed. The integrators can meet pre-specified accuracy requirements in a mathematically strict way, and are a stepping stone towards fully rigorous estimates of stability of repetitive systems. copyright 1997 American Institute of Physics
Time series modeling for syndromic surveillance
Directory of Open Access Journals (Sweden)
Mandl Kenneth D
2003-01-01
Full Text Available Abstract Background Emergency department (ED based syndromic surveillance systems identify abnormally high visit rates that may be an early signal of a bioterrorist attack. For example, an anthrax outbreak might first be detectable as an unusual increase in the number of patients reporting to the ED with respiratory symptoms. Reliably identifying these abnormal visit patterns requires a good understanding of the normal patterns of healthcare usage. Unfortunately, systematic methods for determining the expected number of (ED visits on a particular day have not yet been well established. We present here a generalized methodology for developing models of expected ED visit rates. Methods Using time-series methods, we developed robust models of ED utilization for the purpose of defining expected visit rates. The models were based on nearly a decade of historical data at a major metropolitan academic, tertiary care pediatric emergency department. The historical data were fit using trimmed-mean seasonal models, and additional models were fit with autoregressive integrated moving average (ARIMA residuals to account for recent trends in the data. The detection capabilities of the model were tested with simulated outbreaks. Results Models were built both for overall visits and for respiratory-related visits, classified according to the chief complaint recorded at the beginning of each visit. The mean absolute percentage error of the ARIMA models was 9.37% for overall visits and 27.54% for respiratory visits. A simple detection system based on the ARIMA model of overall visits was able to detect 7-day-long simulated outbreaks of 30 visits per day with 100% sensitivity and 97% specificity. Sensitivity decreased with outbreak size, dropping to 94% for outbreaks of 20 visits per day, and 57% for 10 visits per day, all while maintaining a 97% benchmark specificity. Conclusions Time series methods applied to historical ED utilization data are an important tool
Fisher information framework for time series modeling
Venkatesan, R. C.; Plastino, A.
2017-08-01
A robust prediction model invoking the Takens embedding theorem, whose working hypothesis is obtained via an inference procedure based on the minimum Fisher information principle, is presented. The coefficients of the ansatz, central to the working hypothesis satisfy a time independent Schrödinger-like equation in a vector setting. The inference of (i) the probability density function of the coefficients of the working hypothesis and (ii) the establishing of constraint driven pseudo-inverse condition for the modeling phase of the prediction scheme, is made, for the case of normal distributions, with the aid of the quantum mechanical virial theorem. The well-known reciprocity relations and the associated Legendre transform structure for the Fisher information measure (FIM, hereafter)-based model in a vector setting (with least square constraints) are self-consistently derived. These relations are demonstrated to yield an intriguing form of the FIM for the modeling phase, which defines the working hypothesis, solely in terms of the observed data. Cases for prediction employing time series' obtained from the: (i) the Mackey-Glass delay-differential equation, (ii) one ECG signal from the MIT-Beth Israel Deaconess Hospital (MIT-BIH) cardiac arrhythmia database, and (iii) one ECG signal from the Creighton University ventricular tachyarrhythmia database. The ECG samples were obtained from the Physionet online repository. These examples demonstrate the efficiency of the prediction model. Numerical examples for exemplary cases are provided.
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.
Shin, Masako T.
This English-Thai lexicon and program introduction for combination welding is one of eight documents in the Multicultural Competency-Based Vocational/Technical Curricula Series. It is intended for use in postsecondary, adult, and preservice teacher and administrator education. The first two sections provide Thai equivalencies of English…
Shin, Masako T.
This English-Thai lexicon and program introduction for automotive mechanics is one of eight documents in the Multicultural Competency-Based Vocational/Technical Curricula Series. It is intended for use in postsecondary, adult, and preservice teacher and administrator education. The first two sections provide Thai equivalencies of English…
Foundations of Sequence-to-Sequence Modeling for Time Series
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...
Neural Network Models for Time Series Forecasts
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 ...
Modeling Time Series Data for Supervised Learning
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…
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.
Koster, Randal D. (Editor); Kimball, John S.; Jones, Lucas A.; Glassy, Joseph; Stavros, E. Natasha; Madani, Nima (Editor); Reichle, Rolf H.; Jackson, Thomas; Colliander, Andreas
2015-01-01
During the post-launch Cal/Val Phase of SMAP there are two objectives for each science product team: 1) calibrate, verify, and improve the performance of the science algorithms, and 2) validate accuracies of the science data products as specified in the L1 science requirements according to the Cal/Val timeline. This report provides analysis and assessment of the SMAP Level 4 Carbon (L4_C) product specifically for the beta release. The beta-release version of the SMAP L4_C algorithms utilizes a terrestrial carbon flux model informed by SMAP soil moisture inputs along with optical remote sensing (e.g. MODIS) vegetation indices and other ancillary biophysical data to estimate global daily NEE and component carbon fluxes, particularly vegetation gross primary production (GPP) and ecosystem respiration (Reco). Other L4_C product elements include surface (<10 cm depth) soil organic carbon (SOC) stocks and associated environmental constraints to these processes, including soil moisture and landscape FT controls on GPP and Reco (Kimball et al. 2012). The L4_C product encapsulates SMAP carbon cycle science objectives by: 1) providing a direct link between terrestrial carbon fluxes and underlying freeze/thaw and soil moisture constraints to these processes, 2) documenting primary connections between terrestrial water, energy and carbon cycles, and 3) improving understanding of terrestrial carbon sink activity in northern ecosystems.
Hidden Markov Models for Time Series An Introduction Using R
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.
Hybrid Corporate Performance Prediction Model Considering Technical Capability
Directory of Open Access Journals (Sweden)
Joonhyuck Lee
2016-07-01
Full Text Available Many studies have tried to predict corporate performance and stock prices to enhance investment profitability using qualitative approaches such as the Delphi method. However, developments in data processing technology and machine-learning algorithms have resulted in efforts to develop quantitative prediction models in various managerial subject areas. We propose a quantitative corporate performance prediction model that applies the support vector regression (SVR algorithm to solve the problem of the overfitting of training data and can be applied to regression problems. The proposed model optimizes the SVR training parameters based on the training data, using the genetic algorithm to achieve sustainable predictability in changeable markets and managerial environments. Technology-intensive companies represent an increasing share of the total economy. The performance and stock prices of these companies are affected by their financial standing and their technological capabilities. Therefore, we apply both financial indicators and technical indicators to establish the proposed prediction model. Here, we use time series data, including financial, patent, and corporate performance information of 44 electronic and IT companies. Then, we predict the performance of these companies as an empirical verification of the prediction performance of the proposed model.
Modelling road accidents: An approach using structural time series
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.
Tracing the History of Technical Communication from 1850-2000: Plus a Series of Survey Studies.
McDowell, Earl E.
This research focuses on the history of technical communication since 1850, with a specific focus on the technological changes that occurred between 1900 and 1950. This paper also discusses the development of professional technical communication organizations and the development of technical communication programs at the bachelor, masters, and…
Fourier Series, the DFT and Shape Modelling
DEFF Research Database (Denmark)
Skoglund, Karl
2004-01-01
This report provides an introduction to Fourier series, the discrete Fourier transform, complex geometry and Fourier descriptors for shape analysis. The content is aimed at undergraduate and graduate students who wish to learn about Fourier analysis in general, as well as its application to shape...
Trend time-series modeling and forecasting with neural networks.
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.
Multiple Time Series Ising Model for Financial Market Simulations
International Nuclear Information System (INIS)
Takaishi, Tetsuya
2015-01-01
In this paper we propose an Ising model which simulates multiple financial time series. Our model introduces the interaction which couples to spins of other systems. Simulations from our model show that time series exhibit the volatility clustering that is often observed in the real financial markets. Furthermore we also find non-zero cross correlations between the volatilities from our model. Thus our model can simulate stock markets where volatilities of stocks are mutually correlated
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.
Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models
Price, Larry R.
2012-01-01
The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…
Estimation of pure autoregressive vector models for revenue series ...
African Journals Online (AJOL)
This paper aims at applying multivariate approach to Box and Jenkins univariate time series modeling to three vector series. General Autoregressive Vector Models with time varying coefficients are estimated. The first vector is a response vector, while others are predictor vectors. By matrix expansion each vector, whether ...
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...
International Nuclear Information System (INIS)
Sa'ad, Suleiman
2010-01-01
This paper stresses the importance of incorporating the effects of improved technical efficiency and exogenous factors when estimating energy demand functions. Using annual time series data for the period 1973-2007 in the STSM (structural time series model) developed by Harvey et al. the paper estimates price and income elasticities of demand for energy as well as the annual growth of the stochastic trend at the end of the estimation period. The results of the study reveal a long-run income elasticity of 1.37 and a price elasticity of -0.19. In addition, the underlying trend is generally stochastic and negatively sloping during the greater part of the estimation period. Finally, the estimated result from the structural time series is compared with the results from the Johansen Cointegration. These results suggest that income is the dominant factor in energy consumption. In addition, the coefficient of linear trend is negative, supporting the results from the STSM.
Energy Technology Data Exchange (ETDEWEB)
Sa' ad, Suleiman [Surrey Centre for Energy Economics (SEEC), University of Surrey, Guildford (United Kingdom)
2010-07-15
This paper stresses the importance of incorporating the effects of improved technical efficiency and exogenous factors when estimating energy demand functions. Using annual time series data for the period 1973-2007 in the STSM (structural time series model) developed by Harvey et al. the paper estimates price and income elasticities of demand for energy as well as the annual growth of the stochastic trend at the end of the estimation period. The results of the study reveal a long-run income elasticity of 1.37 and a price elasticity of -0.19. In addition, the underlying trend is generally stochastic and negatively sloping during the greater part of the estimation period. Finally, the estimated result from the structural time series is compared with the results from the Johansen Cointegration. These results suggest that income is the dominant factor in energy consumption. In addition, the coefficient of linear trend is negative, supporting the results from the STSM. (author)
Modelling and Analysing Socio-Technical Systems
DEFF Research Database (Denmark)
Aslanyan, Zaruhi; Ivanova, Marieta Georgieva; Nielson, Flemming
2015-01-01
Modern organisations are complex, socio-technical systems consisting of a mixture of physical infrastructure, human actors, policies and processes. An in-creasing number of attacks on these organisations exploits vulnerabilities on all different levels, for example combining a malware attack...
Modeling and Analysing Socio-Technical Systems
Aslanyan, Zaruhi; Ivanova, Marieta G.; Nielson, Flemming; Probst, Christian W.
2015-01-01
Modern organisations are complex, socio-technical systems consisting of a mixture of physical infrastructure, human actors, policies and processes. An in- creasing number of attacks on these organisations exploits vulnerabilities on all different levels, for example combining a malware attack with
DEFF Research Database (Denmark)
Madsen, Henrik; Rasmussen, Peter F.; Rosbjerg, Dan
1997-01-01
Two different models for analyzing extreme hydrologic events, based on, respectively, partial duration series (PDS) and annual maximum series (AMS), are compared. The PDS model assumes a generalized Pareto distribution for modeling threshold exceedances corresponding to a generalized extreme value......). In the case of ML estimation, the PDS model provides the most efficient T-year event estimator. In the cases of MOM and PWM estimation, the PDS model is generally preferable for negative shape parameters, whereas the AMS model yields the most efficient estimator for positive shape parameters. A comparison...... of the considered methods reveals that in general, one should use the PDS model with MOM estimation for negative shape parameters, the PDS model with exponentially distributed exceedances if the shape parameter is close to zero, the AMS model with MOM estimation for moderately positive shape parameters, and the PDS...
Technical Communicator: A New Model for the Electronic Resources Librarian?
Hulseberg, Anna
2016-01-01
This article explores whether technical communicator is a useful model for electronic resources (ER) librarians. The fields of ER librarianship and technical communication (TC) originated and continue to develop in relation to evolving technologies. A review of the literature reveals four common themes for ER librarianship and TC. While the…
Time series sightability modeling of animal populations.
ArchMiller, Althea A; Dorazio, Robert M; St Clair, Katherine; Fieberg, John R
2018-01-01
Logistic regression models-or "sightability models"-fit to detection/non-detection data from marked individuals are often used to adjust for visibility bias in later detection-only surveys, with population abundance estimated using a modified Horvitz-Thompson (mHT) estimator. More recently, a model-based alternative for analyzing combined detection/non-detection and detection-only data was developed. This approach seemed promising, since it resulted in similar estimates as the mHT when applied to data from moose (Alces alces) surveys in Minnesota. More importantly, it provided a framework for developing flexible models for analyzing multiyear detection-only survey data in combination with detection/non-detection data. During initial attempts to extend the model-based approach to multiple years of detection-only data, we found that estimates of detection probabilities and population abundance were sensitive to the amount of detection-only data included in the combined (detection/non-detection and detection-only) analysis. Subsequently, we developed a robust hierarchical modeling approach where sightability model parameters are informed only by the detection/non-detection data, and we used this approach to fit a fixed-effects model (FE model) with year-specific parameters and a temporally-smoothed model (TS model) that shares information across years via random effects and a temporal spline. The abundance estimates from the TS model were more precise, with decreased interannual variability relative to the FE model and mHT abundance estimates, illustrating the potential benefits from model-based approaches that allow information to be shared across years.
Wireless Emergency Alerts: Trust Model Technical Report
2014-02-01
responses about big events, received comments on messaging around Sandy, the Derecho – these comments were more about technical glitches with phones...We typically do an after-action program on any exercise or big event for which we stand up the emergency operations center (EOC); the Derecho was...county employee messaging – the OPA may help us craft messages, we are always looking to simplify language, ease understanding; Derecho messaging was
Energy Technology Data Exchange (ETDEWEB)
McLarty, J.M.; DeBonis, V.
1995-12-31
One of the main functions of the SPE is to provide a means for collection, dissemination, and exchange of technical information and to provide technical forums that afford opportunities for members to maintain and upgrade their technical competence. The large chapters (such as Houston SPE) located near many oil company headquarters have the advantage of being able to bring together a cross section of service company and operator personnel representing operations and research from major and independent operators. This paper describes a series of 1-day symposiums on frac pack technology that were organized by the Houston-based Gulf Coast Section SPE Production Operations Study Group. These study sessions provided a means for the local members of the industry to further develop a new technology as a team. Publishing the major focus and contributions of the seminars will allow sharing of the technology with chapters outside of Houston.
Time series modelling of overflow structures
DEFF Research Database (Denmark)
Carstensen, J.; Harremoës, P.
1997-01-01
The dynamics of a storage pipe is examined using a grey-box model based on on-line measured data. The grey-box modelling approach uses a combination of physically-based and empirical terms in the model formulation. The model provides an on-line state estimate of the overflows, pumping capacities...... and available storage capacity in the pipe as well as predictions of future states. A linear overflow relation is found, differing significantly from the traditional modelling approach. This is due to complicated overflow structures in a hydraulic sense where the overflow is governed by inertia from the inflow...... to the overflow structures. The capacity of a pump draining the storage pipe has been estimated for two rain events, revealing that the pump was malfunctioning during the first rain event. The grey-box modelling approach is applicable for automated on-line surveillance and control. (C) 1997 IAWQ. Published...
Time series sightability modeling of animal populations
ArchMiller, Althea A.; Dorazio, Robert; St. Clair, Katherine; Fieberg, John R.
2018-01-01
Logistic regression models—or “sightability models”—fit to detection/non-detection data from marked individuals are often used to adjust for visibility bias in later detection-only surveys, with population abundance estimated using a modified Horvitz-Thompson (mHT) estimator. More recently, a model-based alternative for analyzing combined detection/non-detection and detection-only data was developed. This approach seemed promising, since it resulted in similar estimates as the mHT when applied to data from moose (Alces alces) surveys in Minnesota. More importantly, it provided a framework for developing flexible models for analyzing multiyear detection-only survey data in combination with detection/non-detection data. During initial attempts to extend the model-based approach to multiple years of detection-only data, we found that estimates of detection probabilities and population abundance were sensitive to the amount of detection-only data included in the combined (detection/non-detection and detection-only) analysis. Subsequently, we developed a robust hierarchical modeling approach where sightability model parameters are informed only by the detection/non-detection data, and we used this approach to fit a fixed-effects model (FE model) with year-specific parameters and a temporally-smoothed model (TS model) that shares information across years via random effects and a temporal spline. The abundance estimates from the TS model were more precise, with decreased interannual variability relative to the FE model and mHT abundance estimates, illustrating the potential benefits from model-based approaches that allow information to be shared across years.
The use of synthetic input sequences in time series modeling
International Nuclear Information System (INIS)
Oliveira, Dair Jose de; Letellier, Christophe; Gomes, Murilo E.D.; Aguirre, Luis A.
2008-01-01
In many situations time series models obtained from noise-like data settle to trivial solutions under iteration. This Letter proposes a way of producing a synthetic (dummy) input, that is included to prevent the model from settling down to a trivial solution, while maintaining features of the original signal. Simulated benchmark models and a real time series of RR intervals from an ECG are used to illustrate the procedure
Fourier series models through transformation | Omekara | Global ...
African Journals Online (AJOL)
As a result, the square transformation which outperforms the others is adopted. Consequently, each of the multiplicative and additive FSA models fitted to the transformed data are then subjected to a test for white noise based on spectral analysis. The result of this test shows that only the multiplicative model is adequate.
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.
Long Memory Models to Generate Synthetic Hydrological Series
Directory of Open Access Journals (Sweden)
Guilherme Armando de Almeida Pereira
2014-01-01
Full Text Available In Brazil, much of the energy production comes from hydroelectric plants whose planning is not trivial due to the strong dependence on rainfall regimes. This planning is accomplished through optimization models that use inputs such as synthetic hydrologic series generated from the statistical model PAR(p (periodic autoregressive. Recently, Brazil began the search for alternative models able to capture the effects that the traditional model PAR(p does not incorporate, such as long memory effects. Long memory in a time series can be defined as a significant dependence between lags separated by a long period of time. Thus, this research develops a study of the effects of long dependence in the series of streamflow natural energy in the South subsystem, in order to estimate a long memory model capable of generating synthetic hydrologic series.
Tempered fractional time series model for turbulence in geophysical flows
Meerschaert, Mark M.; Sabzikar, Farzad; Phanikumar, Mantha S.; Zeleke, Aklilu
2014-09-01
We propose a new time series model for velocity data in turbulent flows. The new model employs tempered fractional calculus to extend the classical 5/3 spectral model of Kolmogorov. Application to wind speed and water velocity in a large lake are presented, to demonstrate the practical utility of the model.
Tempered fractional time series model for turbulence in geophysical flows
International Nuclear Information System (INIS)
Meerschaert, Mark M; Sabzikar, Farzad; Phanikumar, Mantha S; Zeleke, Aklilu
2014-01-01
We propose a new time series model for velocity data in turbulent flows. The new model employs tempered fractional calculus to extend the classical 5/3 spectral model of Kolmogorov. Application to wind speed and water velocity in a large lake are presented, to demonstrate the practical utility of the model. (paper)
forecasting with nonlinear time series model: a monte-carlo
African Journals Online (AJOL)
PUBLICATIONS1
Carlo method of forecasting using a special nonlinear time series model, called logistic smooth transition ... We illustrate this new method using some simulation ..... in MATLAB 7.5.0. ... process (DGP) using the logistic smooth transi-.
Structural Equation Modeling of Multivariate Time Series
du Toit, Stephen H. C.; Browne, Michael W.
2007-01-01
The covariance structure of a vector autoregressive process with moving average residuals (VARMA) is derived. It differs from other available expressions for the covariance function of a stationary VARMA process and is compatible with current structural equation methodology. Structural equation modeling programs, such as LISREL, may therefore be…
LPV system identification using series expansion models
Toth, R.; Heuberger, P.S.C.; Hof, Van den P.M.J.; Santos, dos P.L.; Perdicoúlis, T.P.A.; Novara, C.; Ramos, J.A.; Rivera, D.E.
2011-01-01
This review volume reports the state-of-the-art in Linear Parameter Varying (LPV) system identification. Written by world renowned researchers, the book contains twelve chapters, focusing on the most recent LPV identification methods for both discrete-time and continuous-time models, using different
Modelling Social-Technical Attacks with Timed Automata
DEFF Research Database (Denmark)
David, Nicolas; David, Alexandre; Hansen, Rene Rydhof
2015-01-01
. In this paper we develop an approach towards modelling socio-technical systems in general and socio-technical attacks in particular, using timed automata and illustrate its application by a complex case study. Thanks to automated model checking and automata theory, we can automatically generate possible attacks...... in our model and perform analysis and simulation of both model and attack, revealing details about the specific interaction between attacker and victim. Using timed automata also allows for intuitive modelling of systems, in which quantities like time and cost can be easily added and analysed....
Forecasting daily meteorological time series using ARIMA and regression models
Murat, Małgorzata; Malinowska, Iwona; Gos, Magdalena; Krzyszczak, Jaromir
2018-04-01
The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.
Febrian Umbara, Rian; Tarwidi, Dede; Budi Setiawan, Erwin
2018-03-01
The paper discusses the prediction of Jakarta Composite Index (JCI) in Indonesia Stock Exchange. The study is based on JCI historical data for 1286 days to predict the value of JCI one day ahead. This paper proposes predictions done in two stages., The first stage using Fuzzy Time Series (FTS) to predict values of ten technical indicators, and the second stage using Support Vector Regression (SVR) to predict the value of JCI one day ahead, resulting in a hybrid prediction model FTS-SVR. The performance of this combined prediction model is compared with the performance of the single stage prediction model using SVR only. Ten technical indicators are used as input for each model.
With string model to time series forecasting
Pinčák, Richard; Bartoš, Erik
2015-10-01
Overwhelming majority of econometric models applied on a long term basis in the financial forex market do not work sufficiently well. The reason is that transaction costs and arbitrage opportunity are not included, as this does not simulate the real financial markets. Analyses are not conducted on the non equidistant date but rather on the aggregate date, which is also not a real financial case. In this paper, we would like to show a new way how to analyze and, moreover, forecast financial market. We utilize the projections of the real exchange rate dynamics onto the string-like topology in the OANDA market. The latter approach allows us to build the stable prediction models in trading in the financial forex market. The real application of the multi-string structures is provided to demonstrate our ideas for the solution of the problem of the robust portfolio selection. The comparison with the trend following strategies was performed, the stability of the algorithm on the transaction costs for long trade periods was confirmed.
World Magnetic Model 2015 Technical Report
National Oceanic and Atmospheric Administration, Department of Commerce — The World Magnetic Model is the standard model used by the U.S. Department of Defense, the U.K. Ministry of Defence, the North Atlantic Treaty Organization (NATO)...
Teaching Technical Writing and Editing -- In-House Programs That Work. Anthology Series No. 5.
Shaw, James G., Ed.
The 12 articles in this publication provide indepth treatment of important aspects of in-house training programs for technical writing and editing. The articles deal with the following topics: the value of an in-house writing course, teaching in industry, developing an in-house writing course for engineers and scientists, a new approach to…
Technical report on comparative analysis of ASME QA requirements and ISO series
International Nuclear Information System (INIS)
Kim, Kwan Hyun
2000-06-01
This technical report provides the differences on the QA requirement ASME and ISO in nuclear fields. This report applies to the quality assurance(QA) programmes of the design of two requirement. The organization having overall responsibility for the nuclear design, preservation, fabrication shall be described in this report in each stage of design project
Koopman Operator Framework for Time Series Modeling and Analysis
Surana, Amit
2018-01-01
We propose an interdisciplinary framework for time series classification, forecasting, and anomaly detection by combining concepts from Koopman operator theory, machine learning, and linear systems and control theory. At the core of this framework is nonlinear dynamic generative modeling of time series using the Koopman operator which is an infinite-dimensional but linear operator. Rather than working with the underlying nonlinear model, we propose two simpler linear representations or model forms based on Koopman spectral properties. We show that these model forms are invariants of the generative model and can be readily identified directly from data using techniques for computing Koopman spectral properties without requiring the explicit knowledge of the generative model. We also introduce different notions of distances on the space of such model forms which is essential for model comparison/clustering. We employ the space of Koopman model forms equipped with distance in conjunction with classical machine learning techniques to develop a framework for automatic feature generation for time series classification. The forecasting/anomaly detection framework is based on using Koopman model forms along with classical linear systems and control approaches. We demonstrate the proposed framework for human activity classification, and for time series forecasting/anomaly detection in power grid application.
Stochastic modeling of hourly rainfall times series in Campania (Italy)
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
SAM Photovoltaic Model Technical Reference 2016 Update
Energy Technology Data Exchange (ETDEWEB)
Gilman, Paul [National Renewable Energy Laboratory (NREL), Golden, CO (United States); DiOrio, Nicholas A [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Freeman, Janine M [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Janzou, Steven [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Dobos, Aron [No longer NREL employee; Ryberg, David [No longer NREL employee
2018-03-19
This manual describes the photovoltaic performance model in the System Advisor Model (SAM) software, Version 2016.3.14 Revision 4 (SSC Version 160). It is an update to the 2015 edition of the manual, which describes the photovoltaic model in SAM 2015.1.30 (SSC 41). This new edition includes corrections of errors in the 2015 edition and descriptions of new features introduced in SAM 2016.3.14, including: 3D shade calculator Battery storage model DC power optimizer loss inputs Snow loss model Plane-of-array irradiance input from weather file option Support for sub-hourly simulations Self-shading works with all four subarrays, and uses same algorithm for fixed arrays and one-axis tracking Linear self-shading algorithm for thin-film modules Loss percentages replace derate factors. The photovoltaic performance model is one of the modules in the SAM Simulation Core (SSC), which is part of both SAM and the SAM SDK. SAM is a user-friedly desktop application for analysis of renewable energy projects. The SAM SDK (Software Development Kit) is for developers writing their own renewable energy analysis software based on SSC. This manual is written for users of both SAM and the SAM SDK wanting to learn more about the details of SAM's photovoltaic model.
SSM: Inference for time series analysis with State Space Models
Dureau, Joseph; Ballesteros, Sébastien; Bogich, Tiffany
2013-01-01
The main motivation behind the open source library SSM is to reduce the technical friction that prevents modellers from sharing their work, quickly iterating in crisis situations, and making their work directly usable by public authorities to serve decision-making.
Models for Pooled Time-Series Cross-Section Data
Directory of Open Access Journals (Sweden)
Lawrence E Raffalovich
2015-07-01
Full Text Available Several models are available for the analysis of pooled time-series cross-section (TSCS data, defined as “repeated observations on fixed units” (Beck and Katz 1995. In this paper, we run the following models: (1 a completely pooled model, (2 fixed effects models, and (3 multi-level/hierarchical linear models. To illustrate these models, we use a Generalized Least Squares (GLS estimator with cross-section weights and panel-corrected standard errors (with EViews 8 on the cross-national homicide trends data of forty countries from 1950 to 2005, which we source from published research (Messner et al. 2011. We describe and discuss the similarities and differences between the models, and what information each can contribute to help answer substantive research questions. We conclude with a discussion of how the models we present may help to mitigate validity threats inherent in pooled time-series cross-section data analysis.
Technical performance of percutaneous and laminectomy leads analyzed by modeling
Manola, L.; Holsheimer, J.
2004-01-01
The objective of this study was to compare the technical performance of laminectomy and percutaneous spinal cord stimulation leads with similar contact spacing by computer modeling. Monopolar and tripolar (guarded cathode) stimulation with both lead types in a low-thoracic spine model was simulated
Uncertainty and endogenous technical change in climate policy models
International Nuclear Information System (INIS)
Baker, Erin; Shittu, Ekundayo
2008-01-01
Until recently endogenous technical change and uncertainty have been modeled separately in climate policy models. In this paper, we review the emerging literature that considers both these elements together. Taken as a whole the literature indicates that explicitly including uncertainty has important quantitative and qualitative impacts on optimal climate change technology policy. (author)
Small-signal model for the series resonant converter
King, R. J.; Stuart, T. A.
1985-01-01
The results of a previous discrete-time model of the series resonant dc-dc converter are reviewed and from these a small signal dynamic model is derived. This model is valid for low frequencies and is based on the modulation of the diode conduction angle for control. The basic converter is modeled separately from its output filter to facilitate the use of these results for design purposes. Experimental results are presented.
Identification of human operator performance models utilizing time series analysis
Holden, F. M.; Shinners, S. M.
1973-01-01
The results of an effort performed by Sperry Systems Management Division for AMRL in applying time series analysis as a tool for modeling the human operator are presented. This technique is utilized for determining the variation of the human transfer function under various levels of stress. The human operator's model is determined based on actual input and output data from a tracking experiment.
International Nuclear Information System (INIS)
Zedgenidze, G.A.; Narkevich, K.Ya.
1987-01-01
To substantiate the expediency of choosing either vizualization method, a comparative analysis of advantages and disadvantages of roentgen computerized tomography (CT) and ultrasonic investigation (UI) has been performed. Conditions of using CT and UI in developing countries emphasising on organizational and technical and esepecially clinical-diagnostic aspects of this problem have been analysed as well. A manual on clinical indications and correct CT and UI use from the point of view of methodology has been compiled
The DART general equilibrium model: A technical description
Springer, Katrin
1998-01-01
This paper provides a technical description of the Dynamic Applied Regional Trade (DART) General Equilibrium Model. The DART model is a recursive dynamic, multi-region, multi-sector computable general equilibrium model. All regions are fully specified and linked by bilateral trade flows. The DART model can be used to project economic activities, energy use and trade flows for each of the specified regions to simulate various trade policy as well as environmental policy scenarios, and to analy...
Enhanced technical and economic working domains of industrial heat pumps operated in series
DEFF Research Database (Denmark)
Ommen, Torben; Jensen, Jonas Kjær; Markussen, Wiebke Brix
2015-01-01
By operating heat pumps (HPs) in series, it is possible to obtain closer match between working fluid and sink- and source streams, resulting in higher coefficient of performance (COP). For industrial HPs, it was found that serial connection of either two or three units results in an increase in COP...
Modeling and Simulation of Technical Plasmas
Dijk, van J.
2009-01-01
Original title: Challenges in the Modelling of Low-Temperature Plasma Sources Elektrotechnisches Kolloquium. Since its inception in the beginning of the twentieth century, plasma science has grown to a major field of science. Lowtemperature plasma sources and gas discharges can be found in domestic
Technical data report : marine acoustics modelling study
Energy Technology Data Exchange (ETDEWEB)
Chorney, N.; Warner, G.; Austin, M. [Jasco Applied Sciences, Victoria, BC (Canada)
2010-07-01
This study was conducted to predict the ensonification produced by vessel traffic transiting to and from the Enbridge Northern Gateway Project's marine terminal located near Kitimat, British Columbia (BC). An underwater acoustic propagation model was used to model frequency bands from 20 Hz to 5 kHz at a standard depth of 20 metres. The model included bathymetric grids of the modelling area; underwater sound speed as a function of depth; and geo-acoustic profiles based on the stratified composition of the seafloor. The obtained 1/3 octave band levels were then used to determine broadband received sound levels for 4 scenarios along various transit routes: the Langara and Triple Island in Dixon Entrance; the Browning Entrance in Hecate Strait, and Cape St. James in the Queen Charlotte Basin. The scenarios consisted of a tanker transiting at 16 knots, and an accompanying tug boat. Underwater sound level maps for each scenario were presented. 14 refs., 5 tabs., 16 figs.
RADON CONCENTRATION TIME SERIES MODELING AND APPLICATION DISCUSSION.
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.
Technical description of the RIVM trajectory model
Energy Technology Data Exchange (ETDEWEB)
De Waal, E.S.; Van Pul, W.A.J.
1995-12-01
The RIVM trajectory model, described in this report, enables calculation of a backward or forward trajectory. These trajectories are used to `follow` previous released air pollution in a backward mode or to `find` the origin of air pollution in a forward mode. The trajectories are used in the smog forecasting and in the TREND model for the distribution of materials in Europe. Presently 6-hourly ECMWF wind fields at 1000 and 850 hPa, with 3 deg x 3 deg latitude-longitude resolution are used. Wind fields with a different resolution in latitude-longitude can also be used after simple adjustments. An iterative method, described elsewhere, is applied to calculate the trajectories. Within limits, the user is free to choose the time step (1, 2 or 6-hour), transport height, length, starting or arrival date and starting or arrival position of the trajectory. The differences between the trajectories calculated with time steps of 1, 2 and 6 h were small. For the 96-hour trajectories at 1000 and 850 hPa the deviations were generally within 1 deg latitude and longitude, i.e. 100-200 km. The trajectory calculated with the 6-hour time step could be used without a great loss in accuracy compared to the calculations with the 1-hour time step. A typical error in the trajectory path at 1000 and 850 hPa was 500 km, which is about 30% of a typical travel distance. However, close to quickly changing weather systems, such as cyclones, the error can be as large as the travel distance and makes the calculations unreliable. The error in the forecasted trajectory was found to be larger than the above error estimation due to larger uncertainties in the forecasted compared to the analyzed wind fields. A manual on how to run the model is also given. 5 figs., 3 tabs., 5 refs., 6 appendices
High-temperature series expansions for random Potts models
Directory of Open Access Journals (Sweden)
M.Hellmund
2005-01-01
Full Text Available We discuss recently generated high-temperature series expansions for the free energy and the susceptibility of random-bond q-state Potts models on hypercubic lattices. Using the star-graph expansion technique, quenched disorder averages can be calculated exactly for arbitrary uncorrelated coupling distributions while keeping the disorder strength p as well as the dimension d as symbolic parameters. We present analyses of the new series for the susceptibility of the Ising (q=2 and 4-state Potts model in three dimensions up to the order 19 and 18, respectively, and compare our findings with results from field-theoretical renormalization group studies and Monte Carlo simulations.
PVUSA model technical specification for a turnkey photovoltaic power system
Energy Technology Data Exchange (ETDEWEB)
Dows, R.N.; Gough, E.J.
1995-11-01
One of the five objectives of PVUSA is to offer U.S. utilities hands-on experience in designing, procuring, and operating PV systems. The procurement process included the development of a detailed set of technical requirements for a PV system. PVUSA embodied its requirements in a technical specification used as an attachment to its contracts for four utility-scale PV systems in the 200 kW to 500 kW range. The technical specification has also been adapted and used by several utilities. The PVUSA Technical Specification has now been updated and is presented here as a Model Technical Specification (MTS) for utility use. The MTS text is also furnished on a computer disk in Microsoft Word 6.0 so that it may be conveniently adapted by each user. The text includes guidance in the form of comments and by the use of parentheses to indicate where technical information must be developed and inserted. Commercial terms and conditions will reflect the procurement practice of the buyer. The reader is referred to PG&E Report Number 95-3090000. 1, PVUSA Procurement, Acceptance and Rating Practices for Photovoltaic Power Plants (1995) for PVUSA experience and practice. The MTS is regarded by PVUSA as a use-proven document, but needs to be adapted with care and attention to detail.
New series of 3 D lattice integrable models
International Nuclear Information System (INIS)
Mangazeev, V.V.; Sergeev, S.M.; Stroganov, Yu.G.
1993-01-01
In this paper we present a new series of 3-dimensional integrable lattice models with N colors. The weight functions of the models satisfy modified tetrahedron equations with N states and give a commuting family of two-layer transfer-matrices. The dependence on the spectral parameters corresponds to the static limit of the modified tetrahedron equations and weights are parameterized in terms of elliptic functions. The models contain two free parameters: elliptic modulus and additional parameter η. 12 refs
Quality Quandaries- Time Series Model Selection and Parsimony
DEFF Research Database (Denmark)
Bisgaard, Søren; Kulahci, Murat
2009-01-01
Some of the issues involved in selecting adequate models for time series data are discussed using an example concerning the number of users of an Internet server. The process of selecting an appropriate model is subjective and requires experience and judgment. The authors believe an important...... consideration in model selection should be parameter parsimony. They favor the use of parsimonious mixed ARMA models, noting that research has shown that a model building strategy that considers only autoregressive representations will lead to non-parsimonious models and to loss of forecasting accuracy....
Annotated bibliography of structural equation modelling: technical work.
Austin, J T; Wolfle, L M
1991-05-01
Researchers must be familiar with a variety of source literature to facilitate the informed use of structural equation modelling. Knowledge can be acquired through the study of an expanding literature found in a diverse set of publishing forums. We propose that structural equation modelling publications can be roughly classified into two groups: (a) technical and (b) substantive applications. Technical materials focus on the procedures rather than substantive conclusions derived from applications. The focus of this article is the former category; included are foundational/major contributions, minor contributions, critical and evaluative reviews, integrations, simulations and computer applications, precursor and historical material, and pedagogical textbooks. After a brief introduction, we annotate 294 articles in the technical category dating back to Sewall Wright (1921).
Technical Manual for the SAM Biomass Power Generation Model
Energy Technology Data Exchange (ETDEWEB)
Jorgenson, J.; Gilman, P.; Dobos, A.
2011-09-01
This technical manual provides context for the implementation of the biomass electric power generation performance model in the National Renewable Energy Laboratory's (NREL's) System Advisor Model (SAM). Additionally, the report details the engineering and scientific principles behind the underlying calculations in the model. The framework established in this manual is designed to give users a complete understanding of behind-the-scenes calculations and the results generated.
Capturing socio-technical systems with agent-based modelling
Van Dam, K.H.
2009-01-01
What is a suitable modelling approach for socio-technical systems? The answer to this question is of great importance to decision makers in large scale interconnected network systems. The behaviour of these systems is determined by many actors, situated in a dynamic, multi-actor, multi-objective and
Single-Index Additive Vector Autoregressive Time Series Models
LI, YEHUA; GENTON, MARC G.
2009-01-01
We study a new class of nonlinear autoregressive models for vector time series, where the current vector depends on single-indexes defined on the past lags and the effects of different lags have an additive form. A sufficient condition is provided
Book Review: "Hidden Markov Models for Time Series: An ...
African Journals Online (AJOL)
Hidden Markov Models for Time Series: An Introduction using R. by Walter Zucchini and Iain L. MacDonald. Chapman & Hall (CRC Press), 2009. Full Text: EMAIL FULL TEXT EMAIL FULL TEXT · DOWNLOAD FULL TEXT DOWNLOAD FULL TEXT · http://dx.doi.org/10.4314/saaj.v10i1.61717 · AJOL African Journals Online.
Modeling the full-bridge series-resonant power converter
King, R. J.; Stuart, T. A.
1982-01-01
A steady state model is derived for the full-bridge series-resonant power converter. Normalized parametric curves for various currents and voltages are then plotted versus the triggering angle of the switching devices. The calculations are compared with experimental measurements made on a 50 kHz converter and a discussion of certain operating problems is presented.
Multivariate time series modeling of selected childhood diseases in ...
African Journals Online (AJOL)
This paper is focused on modeling the five most prevalent childhood diseases in Akwa Ibom State using a multivariate approach to time series. An aggregate of 78,839 reported cases of malaria, upper respiratory tract infection (URTI), Pneumonia, anaemia and tetanus were extracted from five randomly selected hospitals in ...
Recursive Bayesian recurrent neural networks for time-series modeling.
Mirikitani, Derrick T; Nikolaev, Nikolay
2010-02-01
This paper develops a probabilistic approach to recursive second-order training of recurrent neural networks (RNNs) for improved time-series modeling. A general recursive Bayesian Levenberg-Marquardt algorithm is derived to sequentially update the weights and the covariance (Hessian) matrix. The main strengths of the approach are a principled handling of the regularization hyperparameters that leads to better generalization, and stable numerical performance. The framework involves the adaptation of a noise hyperparameter and local weight prior hyperparameters, which represent the noise in the data and the uncertainties in the model parameters. Experimental investigations using artificial and real-world data sets show that RNNs equipped with the proposed approach outperform standard real-time recurrent learning and extended Kalman training algorithms for recurrent networks, as well as other contemporary nonlinear neural models, on time-series modeling.
Modeling Philippine Stock Exchange Composite Index Using Time Series Analysis
Gayo, W. S.; Urrutia, J. D.; Temple, J. M. F.; Sandoval, J. R. D.; Sanglay, J. E. A.
2015-06-01
This study was conducted to develop a time series model of the Philippine Stock Exchange Composite Index and its volatility using the finite mixture of ARIMA model with conditional variance equations such as ARCH, GARCH, EG ARCH, TARCH and PARCH models. Also, the study aimed to find out the reason behind the behaviorof PSEi, that is, which of the economic variables - Consumer Price Index, crude oil price, foreign exchange rate, gold price, interest rate, money supply, price-earnings ratio, Producers’ Price Index and terms of trade - can be used in projecting future values of PSEi and this was examined using Granger Causality Test. The findings showed that the best time series model for Philippine Stock Exchange Composite index is ARIMA(1,1,5) - ARCH(1). Also, Consumer Price Index, crude oil price and foreign exchange rate are factors concluded to Granger cause Philippine Stock Exchange Composite Index.
Neural network versus classical time series forecasting models
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.
Time series ARIMA models for daily price of palm oil
Ariff, Noratiqah Mohd; Zamhawari, Nor Hashimah; Bakar, Mohd Aftar Abu
2015-02-01
Palm oil is deemed as one of the most important commodity that forms the economic backbone of Malaysia. Modeling and forecasting the daily price of palm oil is of great interest for Malaysia's economic growth. In this study, time series ARIMA models are used to fit the daily price of palm oil. The Akaike Infromation Criterion (AIC), Akaike Infromation Criterion with a correction for finite sample sizes (AICc) and Bayesian Information Criterion (BIC) are used to compare between different ARIMA models being considered. It is found that ARIMA(1,2,1) model is suitable for daily price of crude palm oil in Malaysia for the year 2010 to 2012.
Alamri, A; Hyodo, A; Suzuki, K; Tanaka, Y; Uchida, T; Takano, I; Kowata, K; Iwatate, K; Suzuki, R
2012-11-01
To date, the "monorail snare technique" for the retrieval of entombed microcatheter tips during Onyx(TM) (ev3, Irvine, CA) embolisation of brain arteriovenous malformations (BAVM) has not been described. We report our experiences and some technical aspects in using this technique for the retrieval of entombed Marathon(TM) microcatheter (ev3, Plymouth, MN) tips during Onyx embolisation of BAVM treatment. Onyx was used in the embolisation of 11 patients using 25 feeders over 14 sessions. The 'monorail snare technique' was employed for 14 feeders. Each time, an Amplatz 4 mm Gooseneck Microsnare(TM) (ev3, Plymouth, MN) was loaded into an Excelsior 1018(TM) microcatheter (Boston Scientific, Natick, MA). The Marathon microcatheter was cut just distal to the hub, and the Amplatz/Excelsior combination was introduced along the length of the Marathon microcatheter towards its distal end, as far as possible. The embedded catheter was ensnared and both catheters were pulled free. Microcatheter tip removal was successful in all cases, except for one microcatheter tip becoming detached and needing no further intervention. There were no complications as a direct result of the snare technique. The monorail snare technique is a safe and easy technique for retrieving Onyx-encased microcatheter tips in the treatment of BAVM.
Time Series Analysis, Modeling and Applications A Computational Intelligence Perspective
Chen, Shyi-Ming
2013-01-01
Temporal and spatiotemporal data form an inherent fabric of the society as we are faced with streams of data coming from numerous sensors, data feeds, recordings associated with numerous areas of application embracing physical and human-generated phenomena (environmental data, financial markets, Internet activities, etc.). A quest for a thorough analysis, interpretation, modeling and prediction of time series comes with an ongoing challenge for developing models that are both accurate and user-friendly (interpretable). The volume is aimed to exploit the conceptual and algorithmic framework of Computational Intelligence (CI) to form a cohesive and comprehensive environment for building models of time series. The contributions covered in the volume are fully reflective of the wealth of the CI technologies by bringing together ideas, algorithms, and numeric studies, which convincingly demonstrate their relevance, maturity and visible usefulness. It reflects upon the truly remarkable diversity of methodological a...
Time series regression model for infectious disease and weather.
Imai, Chisato; Armstrong, Ben; Chalabi, Zaid; Mangtani, Punam; Hashizume, Masahiro
2015-10-01
Time series regression has been developed and long used to evaluate the short-term associations of air pollution and weather with mortality or morbidity of non-infectious diseases. The application of the regression approaches from this tradition to infectious diseases, however, is less well explored and raises some new issues. We discuss and present potential solutions for five issues often arising in such analyses: changes in immune population, strong autocorrelations, a wide range of plausible lag structures and association patterns, seasonality adjustments, and large overdispersion. The potential approaches are illustrated with datasets of cholera cases and rainfall from Bangladesh and influenza and temperature in Tokyo. Though this article focuses on the application of the traditional time series regression to infectious diseases and weather factors, we also briefly introduce alternative approaches, including mathematical modeling, wavelet analysis, and autoregressive integrated moving average (ARIMA) models. Modifications proposed to standard time series regression practice include using sums of past cases as proxies for the immune population, and using the logarithm of lagged disease counts to control autocorrelation due to true contagion, both of which are motivated from "susceptible-infectious-recovered" (SIR) models. The complexity of lag structures and association patterns can often be informed by biological mechanisms and explored by using distributed lag non-linear models. For overdispersed models, alternative distribution models such as quasi-Poisson and negative binomial should be considered. Time series regression can be used to investigate dependence of infectious diseases on weather, but may need modifying to allow for features specific to this context. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
Identification of neutral biochemical network models from time series data
Directory of Open Access Journals (Sweden)
Maia Marco
2009-05-01
Full Text Available Abstract Background The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.e., if it is constructed according to strict guidelines. Results In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity. Conclusion The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.
Identification of neutral biochemical network models from time series data.
Vilela, Marco; Vinga, Susana; Maia, Marco A Grivet Mattoso; Voit, Eberhard O; Almeida, Jonas S
2009-05-05
The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.e., if it is constructed according to strict guidelines. In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity. The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.
Degeneracy of time series models: The best model is not always the correct model
International Nuclear Information System (INIS)
Judd, Kevin; Nakamura, Tomomichi
2006-01-01
There are a number of good techniques for finding, in some sense, the best model of a deterministic system given a time series of observations. We examine a problem called model degeneracy, which has the consequence that even when a perfect model of a system exists, one does not find it using the best techniques currently available. The problem is illustrated using global polynomial models and the theory of Groebner bases
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
Univaried models in the series of temperature of the air
International Nuclear Information System (INIS)
Leon Aristizabal Gloria esperanza
2000-01-01
The theoretical framework for the study of the air's temperature time series is the theory of stochastic processes, particularly those known as ARIMA, that make it possible to carry out a univaried analysis. ARIMA models are built in order to explain the structure of the monthly temperatures corresponding to the mean, the absolute maximum, absolute minimum, maximum mean and minimum mean temperatures, for four stations in Colombia. By means of those models, the possible evolution of the latter variables is estimated with predictive aims in mind. The application and utility of the models is discussed
Wave basin model tests of technical-biological bank protection
Eisenmann, J.
2012-04-01
Sloped embankments of inland waterways are usually protected from erosion and other negative im-pacts of ship-induced hydraulic loads by technical revetments consisting of riprap. Concerning the dimensioning of such bank protection there are several design rules available, e.g. the "Principles for the Design of Bank and Bottom Protection for Inland Waterways" or the Code of Practice "Use of Standard Construction Methods for Bank and Bottom Protection on Waterways" issued by the BAW (Federal Waterways Engineering and Research Institute). Since the European Water Framework Directive has been put into action special emphasis was put on natural banks. Therefore the application of technical-biological bank protection is favoured. Currently design principles for technical-biological bank protection on inland waterways are missing. The existing experiences mainly refer to flowing waters with no or low ship-induced hydraulic loads on the banks. Since 2004 the Federal Waterways Engineering and Research Institute has been tracking the re-search and development project "Alternative Technical-Biological Bank Protection on Inland Water-ways" in company with the Federal Institute of Hydrology. The investigation to date includes the ex-amination of waterway sections where technical- biological bank protection is applied locally. For the development of design rules for technical-biological bank protection investigations shall be carried out in a next step, considering the mechanics and resilience of technical-biological bank protection with special attention to ship-induced hydraulic loads. The presentation gives a short introduction into hydraulic loads at inland waterways and their bank protection. More in detail model tests of a willow brush mattress as a technical-biological bank protec-tion in a wave basin are explained. Within the scope of these tests the brush mattresses were ex-posed to wave impacts to determine their resilience towards hydraulic loads. Since the
Pin failure modeling of the A series CABRI tests
International Nuclear Information System (INIS)
Young, M.F.; Portugal, J.L.
1978-01-01
The EXPAND pin fialure model, a research tool designed to model pin failure under prompt burst conditions, has been used to predict failure conditions for several of the A series CABRI tests as part of the United States participation in the CABRI Joint Project. The Project is an international program involving France, Germany, England, Japan, and the United States and has the goal of obtaining experimental data relating to the safety of LMFBR's. The A series, designed to simulate high ramp rate TOP conditions, initially utilizes single, fresh UO 2 pins of the PHENIX type in a flowing sodium loop. The pins are preheated at constant power in the CABRI reactor to establish steady state conditions (480 w/cm at the axial peak) and then subjected to a power pulse of 14 ms to 24 ms duration
A Parsimonious Bootstrap Method to Model Natural Inflow Energy Series
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Fernando Luiz Cyrino Oliveira
2014-01-01
Full Text Available The Brazilian energy generation and transmission system is quite peculiar in its dimension and characteristics. As such, it can be considered unique in the world. It is a high dimension hydrothermal system with huge participation of hydro plants. Such strong dependency on hydrological regimes implies uncertainties related to the energetic planning, requiring adequate modeling of the hydrological time series. This is carried out via stochastic simulations of monthly inflow series using the family of Periodic Autoregressive models, PAR(p, one for each period (month of the year. In this paper it is shown the problems in fitting these models by the current system, particularly the identification of the autoregressive order “p” and the corresponding parameter estimation. It is followed by a proposal of a new approach to set both the model order and the parameters estimation of the PAR(p models, using a nonparametric computational technique, known as Bootstrap. This technique allows the estimation of reliable confidence intervals for the model parameters. The obtained results using the Parsimonious Bootstrap Method of Moments (PBMOM produced not only more parsimonious model orders but also adherent stochastic scenarios and, in the long range, lead to a better use of water resources in the energy operation planning.
Investigating Some Technical Issues on Cohesive Zone Modeling of Fracture
Wang, John T.
2011-01-01
This study investigates some technical issues related to the use of cohesive zone models (CZMs) in modeling fracture processes. These issues include: why cohesive laws of different shapes can produce similar fracture predictions; under what conditions CZM predictions have a high degree of agreement with linear elastic fracture mechanics (LEFM) analysis results; when the shape of cohesive laws becomes important in the fracture predictions; and why the opening profile along the cohesive zone length needs to be accurately predicted. Two cohesive models were used in this study to address these technical issues. They are the linear softening cohesive model and the Dugdale perfectly plastic cohesive model. Each cohesive model constitutes five cohesive laws of different maximum tractions. All cohesive laws have the same cohesive work rate (CWR) which is defined by the area under the traction-separation curve. The effects of the maximum traction on the cohesive zone length and the critical remote applied stress are investigated for both models. For a CZM to predict a fracture load similar to that obtained by an LEFM analysis, the cohesive zone length needs to be much smaller than the crack length, which reflects the small scale yielding condition requirement for LEFM analysis to be valid. For large-scale cohesive zone cases, the predicted critical remote applied stresses depend on the shape of cohesive models used and can significantly deviate from LEFM results. Furthermore, this study also reveals the importance of accurately predicting the cohesive zone profile in determining the critical remote applied load.
Technical Manual for the SAM Physical Trough Model
Energy Technology Data Exchange (ETDEWEB)
Wagner, M. J.; Gilman, P.
2011-06-01
NREL, in conjunction with Sandia National Lab and the U.S Department of Energy, developed the System Advisor Model (SAM) analysis tool for renewable energy system performance and economic analysis. This paper documents the technical background and engineering formulation for one of SAM's two parabolic trough system models in SAM. The Physical Trough model calculates performance relationships based on physical first principles where possible, allowing the modeler to predict electricity production for a wider range of component geometries than is possible in the Empirical Trough model. This document describes the major parabolic trough plant subsystems in detail including the solar field, power block, thermal storage, piping, auxiliary heating, and control systems. This model makes use of both existing subsystem performance modeling approaches, and new approaches developed specifically for SAM.
Modelling of series of types of automated trenchless works tunneling
Gendarz, P.; Rzasinski, R.
2016-08-01
Microtunneling is the newest method for making underground installations. Show method is the result of experience and methods applied in other, previous methods of trenchless underground works. It is considered reasonable to elaborate a series of types of construction of tunneling machines, to develop this particular earthworks method. There are many design solutions of machines, but the current goal is to develop non - excavation robotized machine. Erosion machines with main dimensions of the tunnels which are: 1600, 2000, 2500, 3150 are design with use of the computer aided methods. Series of types of construction of tunneling machines creating process was preceded by analysis of current state. The verification of practical methodology of creating the systematic part series was based on the designed erosion machines series of types. There were developed: method of construction similarity of the erosion machines, algorithmic methods of quantitative construction attributes variant analyzes in the I-DEAS advanced graphical program, relational and program parameterization. There manufacturing process of the parts will be created, which allows to verify the technological process on the CNC machines. The models of designed will be modified and the construction will be consulted with erosion machine users and manufacturers like: Tauber Rohrbau GmbH & Co.KG from Minster, OHL ZS a.s. from Brna,. The companies’ acceptance will result in practical verification by JUMARPOL company.
Physics constrained nonlinear regression models for time series
International Nuclear Information System (INIS)
Majda, Andrew J; Harlim, John
2013-01-01
A central issue in contemporary science is the development of data driven statistical nonlinear dynamical models for time series of partial observations of nature or a complex physical model. It has been established recently that ad hoc quadratic multi-level regression (MLR) models can have finite-time blow up of statistical solutions and/or pathological behaviour of their invariant measure. Here a new class of physics constrained multi-level quadratic regression models are introduced, analysed and applied to build reduced stochastic models from data of nonlinear systems. These models have the advantages of incorporating memory effects in time as well as the nonlinear noise from energy conserving nonlinear interactions. The mathematical guidelines for the performance and behaviour of these physics constrained MLR models as well as filtering algorithms for their implementation are developed here. Data driven applications of these new multi-level nonlinear regression models are developed for test models involving a nonlinear oscillator with memory effects and the difficult test case of the truncated Burgers–Hopf model. These new physics constrained quadratic MLR models are proposed here as process models for Bayesian estimation through Markov chain Monte Carlo algorithms of low frequency behaviour in complex physical data. (paper)
State-space prediction model for chaotic time series
Alparslan, A. K.; Sayar, M.; Atilgan, A. R.
1998-08-01
A simple method for predicting the continuation of scalar chaotic time series ahead in time is proposed. The false nearest neighbors technique in connection with the time-delayed embedding is employed so as to reconstruct the state space. A local forecasting model based upon the time evolution of the topological neighboring in the reconstructed phase space is suggested. A moving root-mean-square error is utilized in order to monitor the error along the prediction horizon. The model is tested for the convection amplitude of the Lorenz model. The results indicate that for approximately 100 cycles of the training data, the prediction follows the actual continuation very closely about six cycles. The proposed model, like other state-space forecasting models, captures the long-term behavior of the system due to the use of spatial neighbors in the state space.
Single-Index Additive Vector Autoregressive Time Series Models
LI, YEHUA
2009-09-01
We study a new class of nonlinear autoregressive models for vector time series, where the current vector depends on single-indexes defined on the past lags and the effects of different lags have an additive form. A sufficient condition is provided for stationarity of such models. We also study estimation of the proposed model using P-splines, hypothesis testing, asymptotics, selection of the order of the autoregression and of the smoothing parameters and nonlinear forecasting. We perform simulation experiments to evaluate our model in various settings. We illustrate our methodology on a climate data set and show that our model provides more accurate yearly forecasts of the El Niño phenomenon, the unusual warming of water in the Pacific Ocean. © 2009 Board of the Foundation of the Scandinavian Journal of Statistics.
Stochastic series expansion simulation of the t -V model
Wang, Lei; Liu, Ye-Hua; Troyer, Matthias
2016-04-01
We present an algorithm for the efficient simulation of the half-filled spinless t -V model on bipartite lattices, which combines the stochastic series expansion method with determinantal quantum Monte Carlo techniques widely used in fermionic simulations. The algorithm scales linearly in the inverse temperature, cubically with the system size, and is free from the time-discretization error. We use it to map out the finite-temperature phase diagram of the spinless t -V model on the honeycomb lattice and observe a suppression of the critical temperature of the charge-density-wave phase in the vicinity of a fermionic quantum critical point.
Disease management with ARIMA model in time series.
Sato, Renato Cesar
2013-01-01
The evaluation of infectious and noninfectious disease management can be done through the use of a time series analysis. In this study, we expect to measure the results and prevent intervention effects on the disease. Clinical studies have benefited from the use of these techniques, particularly for the wide applicability of the ARIMA model. This study briefly presents the process of using the ARIMA model. This analytical tool offers a great contribution for researchers and healthcare managers in the evaluation of healthcare interventions in specific populations.
Computational model for simulation small testing launcher, technical solution
Energy Technology Data Exchange (ETDEWEB)
Chelaru, Teodor-Viorel, E-mail: teodor.chelaru@upb.ro [University POLITEHNICA of Bucharest - Research Center for Aeronautics and Space, Str. Ghe Polizu, nr. 1, Bucharest, Sector 1 (Romania); Cristian, Barbu, E-mail: barbucr@mta.ro [Military Technical Academy, Romania, B-dul. George Coşbuc, nr. 81-83, Bucharest, Sector 5 (Romania); Chelaru, Adrian, E-mail: achelaru@incas.ro [INCAS -National Institute for Aerospace Research Elie Carafoli, B-dul Iuliu Maniu 220, 061126, Bucharest, Sector 6 (Romania)
2014-12-10
The purpose of this paper is to present some aspects regarding the computational model and technical solutions for multistage suborbital launcher for testing (SLT) used to test spatial equipment and scientific measurements. The computational model consists in numerical simulation of SLT evolution for different start conditions. The launcher model presented will be with six degrees of freedom (6DOF) and variable mass. The results analysed will be the flight parameters and ballistic performances. The discussions area will focus around the technical possibility to realize a small multi-stage launcher, by recycling military rocket motors. From technical point of view, the paper is focused on national project 'Suborbital Launcher for Testing' (SLT), which is based on hybrid propulsion and control systems, obtained through an original design. Therefore, while classical suborbital sounding rockets are unguided and they use as propulsion solid fuel motor having an uncontrolled ballistic flight, SLT project is introducing a different approach, by proposing the creation of a guided suborbital launcher, which is basically a satellite launcher at a smaller scale, containing its main subsystems. This is why the project itself can be considered an intermediary step in the development of a wider range of launching systems based on hybrid propulsion technology, which may have a major impact in the future European launchers programs. SLT project, as it is shown in the title, has two major objectives: first, a short term objective, which consists in obtaining a suborbital launching system which will be able to go into service in a predictable period of time, and a long term objective that consists in the development and testing of some unconventional sub-systems which will be integrated later in the satellite launcher as a part of the European space program. This is why the technical content of the project must be carried out beyond the range of the existing suborbital
Modeling Periodic Impulsive Effects on Online TV Series Diffusion.
Fu, Peihua; Zhu, Anding; Fang, Qiwen; Wang, Xi
Online broadcasting substantially affects the production, distribution, and profit of TV series. In addition, online word-of-mouth significantly affects the diffusion of TV series. Because on-demand streaming rates are the most important factor that influences the earnings of online video suppliers, streaming statistics and forecasting trends are valuable. In this paper, we investigate the effects of periodic impulsive stimulation and pre-launch promotion on on-demand streaming dynamics. We consider imbalanced audience feverish distribution using an impulsive susceptible-infected-removed(SIR)-like model. In addition, we perform a correlation analysis of online buzz volume based on Baidu Index data. We propose a PI-SIR model to evolve audience dynamics and translate them into on-demand streaming fluctuations, which can be observed and comprehended by online video suppliers. Six South Korean TV series datasets are used to test the model. We develop a coarse-to-fine two-step fitting scheme to estimate the model parameters, first by fitting inter-period accumulation and then by fitting inner-period feverish distribution. We find that audience members display similar viewing habits. That is, they seek new episodes every update day but fade away. This outcome means that impulsive intensity plays a crucial role in on-demand streaming diffusion. In addition, the initial audience size and online buzz are significant factors. On-demand streaming fluctuation is highly correlated with online buzz fluctuation. To stimulate audience attention and interpersonal diffusion, it is worthwhile to invest in promotion near update days. Strong pre-launch promotion is also a good marketing tool to improve overall performance. It is not advisable for online video providers to promote several popular TV series on the same update day. Inter-period accumulation is a feasible forecasting tool to predict the future trend of the on-demand streaming amount. The buzz in public social communities
Modeling Periodic Impulsive Effects on Online TV Series Diffusion.
Directory of Open Access Journals (Sweden)
Peihua Fu
Full Text Available Online broadcasting substantially affects the production, distribution, and profit of TV series. In addition, online word-of-mouth significantly affects the diffusion of TV series. Because on-demand streaming rates are the most important factor that influences the earnings of online video suppliers, streaming statistics and forecasting trends are valuable. In this paper, we investigate the effects of periodic impulsive stimulation and pre-launch promotion on on-demand streaming dynamics. We consider imbalanced audience feverish distribution using an impulsive susceptible-infected-removed(SIR-like model. In addition, we perform a correlation analysis of online buzz volume based on Baidu Index data.We propose a PI-SIR model to evolve audience dynamics and translate them into on-demand streaming fluctuations, which can be observed and comprehended by online video suppliers. Six South Korean TV series datasets are used to test the model. We develop a coarse-to-fine two-step fitting scheme to estimate the model parameters, first by fitting inter-period accumulation and then by fitting inner-period feverish distribution.We find that audience members display similar viewing habits. That is, they seek new episodes every update day but fade away. This outcome means that impulsive intensity plays a crucial role in on-demand streaming diffusion. In addition, the initial audience size and online buzz are significant factors. On-demand streaming fluctuation is highly correlated with online buzz fluctuation.To stimulate audience attention and interpersonal diffusion, it is worthwhile to invest in promotion near update days. Strong pre-launch promotion is also a good marketing tool to improve overall performance. It is not advisable for online video providers to promote several popular TV series on the same update day. Inter-period accumulation is a feasible forecasting tool to predict the future trend of the on-demand streaming amount. The buzz in public
Modeling Periodic Impulsive Effects on Online TV Series Diffusion
Fang, Qiwen; Wang, Xi
2016-01-01
Background Online broadcasting substantially affects the production, distribution, and profit of TV series. In addition, online word-of-mouth significantly affects the diffusion of TV series. Because on-demand streaming rates are the most important factor that influences the earnings of online video suppliers, streaming statistics and forecasting trends are valuable. In this paper, we investigate the effects of periodic impulsive stimulation and pre-launch promotion on on-demand streaming dynamics. We consider imbalanced audience feverish distribution using an impulsive susceptible-infected-removed(SIR)-like model. In addition, we perform a correlation analysis of online buzz volume based on Baidu Index data. Methods We propose a PI-SIR model to evolve audience dynamics and translate them into on-demand streaming fluctuations, which can be observed and comprehended by online video suppliers. Six South Korean TV series datasets are used to test the model. We develop a coarse-to-fine two-step fitting scheme to estimate the model parameters, first by fitting inter-period accumulation and then by fitting inner-period feverish distribution. Results We find that audience members display similar viewing habits. That is, they seek new episodes every update day but fade away. This outcome means that impulsive intensity plays a crucial role in on-demand streaming diffusion. In addition, the initial audience size and online buzz are significant factors. On-demand streaming fluctuation is highly correlated with online buzz fluctuation. Conclusion To stimulate audience attention and interpersonal diffusion, it is worthwhile to invest in promotion near update days. Strong pre-launch promotion is also a good marketing tool to improve overall performance. It is not advisable for online video providers to promote several popular TV series on the same update day. Inter-period accumulation is a feasible forecasting tool to predict the future trend of the on-demand streaming amount
Optimization of recurrent neural networks for time series modeling
DEFF Research Database (Denmark)
Pedersen, Morten With
1997-01-01
The present thesis is about optimization of recurrent neural networks applied to time series modeling. In particular is considered fully recurrent networks working from only a single external input, one layer of nonlinear hidden units and a li near output unit applied to prediction of discrete time...... series. The overall objective s are to improve training by application of second-order methods and to improve generalization ability by architecture optimization accomplished by pruning. The major topics covered in the thesis are: 1. The problem of training recurrent networks is analyzed from a numerical...... of solution obtained as well as computation time required. 3. A theoretical definition of the generalization error for recurrent networks is provided. This definition justifies a commonly adopted approach for estimating generalization ability. 4. The viability of pruning recurrent networks by the Optimal...
Modeling and Forecasting of Water Demand in Isfahan Using Underlying Trend Concept and Time Series
Directory of Open Access Journals (Sweden)
H. Sadeghi
2016-02-01
Full Text Available Introduction: Accurate water demand modeling for the city is very important for forecasting and policies adoption related to water resources management. Thus, for future requirements of water estimation, forecasting and modeling, it is important to utilize models with little errors. Water has a special place among the basic human needs, because it not hampers human life. The importance of the issue of water management in the extraction and consumption, it is necessary as a basic need. Municipal water applications is include a variety of water demand for domestic, public, industrial and commercial. Predicting the impact of urban water demand in better planning of water resources in arid and semiarid regions are faced with water restrictions. Materials and Methods: One of the most important factors affecting the changing technological advances in production and demand functions, we must pay special attention to the layout pattern. Technology development is concerned not only technically, but also other aspects such as personal, non-economic factors (population, geographical and social factors can be analyzed. Model examined in this study, a regression model is composed of a series of structural components over time allows changed invisible accidentally. Explanatory variables technology (both crystalline and amorphous in a model according to which the material is said to be better, but because of the lack of measured variables over time can not be entered in the template. Model examined in this study, a regression model is composed of a series of structural component invisible accidentally changed over time allows. In this study, structural time series (STSM and ARMA time series models have been used to model and estimate the water demand in Isfahan. Moreover, in order to find the efficient procedure, both models have been compared to each other. The desired data in this research include water consumption in Isfahan, water price and the monthly pay
Models for waste life cycle assessment: Review of technical assumptions
DEFF Research Database (Denmark)
Gentil, Emmanuel; Damgaard, Anders; Hauschild, Michael Zwicky
2010-01-01
A number of waste life cycle assessment (LCA) models have been gradually developed since the early 1990s, in a number of countries, usually independently from each other. Large discrepancies in results have been observed among different waste LCA models, although it has also been shown that results...... from different LCA studies can be consistent. This paper is an attempt to identify, review and analyse methodologies and technical assumptions used in various parts of selected waste LCA models. Several criteria were identified, which could have significant impacts on the results......, such as the functional unit, system boundaries, waste composition and energy modelling. The modelling assumptions of waste management processes, ranging from collection, transportation, intermediate facilities, recycling, thermal treatment, biological treatment, and landfilling, are obviously critical when comparing...
Technical change in forest sector models: the global forest products model approach
Joseph Buongiorno; Sushuai Zhu
2015-01-01
Technical change is developing rapidly in some parts of the forest sector, especially in the pulp and paper industry where wood fiber is being substituted by waste paper. In forest sector models, the processing of wood and other input into products is frequently represented by activity analysis (inputâoutput). In this context, technical change translates in changes...
Modeling interdependent socio-technical networks: The smart grid—an agent-based modeling approach
Worm, D.; Langley, D.J.; Becker, J.
2014-01-01
The aim of this paper is to improve scientific modeling of interdependent socio-technical networks. In these networks the interplay between technical or infrastructural elements on the one hand and social and behavioral aspects on the other hand, plays an important role. Examples include electricity
A Feature Fusion Based Forecasting Model for Financial Time Series
Guo, Zhiqiang; Wang, Huaiqing; Liu, Quan; Yang, Jie
2014-01-01
Predicting the stock market has become an increasingly interesting research area for both researchers and investors, and many prediction models have been proposed. In these models, feature selection techniques are used to pre-process the raw data and remove noise. In this paper, a prediction model is constructed to forecast stock market behavior with the aid of independent component analysis, canonical correlation analysis, and a support vector machine. First, two types of features are extracted from the historical closing prices and 39 technical variables obtained by independent component analysis. Second, a canonical correlation analysis method is utilized to combine the two types of features and extract intrinsic features to improve the performance of the prediction model. Finally, a support vector machine is applied to forecast the next day's closing price. The proposed model is applied to the Shanghai stock market index and the Dow Jones index, and experimental results show that the proposed model performs better in the area of prediction than other two similar models. PMID:24971455
Hybrid perturbation methods based on statistical time series models
San-Juan, Juan Félix; San-Martín, Montserrat; Pérez, Iván; López, Rosario
2016-04-01
In this work we present a new methodology for orbit propagation, the hybrid perturbation theory, based on the combination of an integration method and a prediction technique. The former, which can be a numerical, analytical or semianalytical theory, generates an initial approximation that contains some inaccuracies derived from the fact that, in order to simplify the expressions and subsequent computations, not all the involved forces are taken into account and only low-order terms are considered, not to mention the fact that mathematical models of perturbations not always reproduce physical phenomena with absolute precision. The prediction technique, which can be based on either statistical time series models or computational intelligence methods, is aimed at modelling and reproducing missing dynamics in the previously integrated approximation. This combination results in the precision improvement of conventional numerical, analytical and semianalytical theories for determining the position and velocity of any artificial satellite or space debris object. In order to validate this methodology, we present a family of three hybrid orbit propagators formed by the combination of three different orders of approximation of an analytical theory and a statistical time series model, and analyse their capability to process the effect produced by the flattening of the Earth. The three considered analytical components are the integration of the Kepler problem, a first-order and a second-order analytical theories, whereas the prediction technique is the same in the three cases, namely an additive Holt-Winters method.
On the maximum-entropy/autoregressive modeling of time series
Chao, B. F.
1984-01-01
The autoregressive (AR) model of a random process is interpreted in the light of the Prony's relation which relates a complex conjugate pair of poles of the AR process in the z-plane (or the z domain) on the one hand, to the complex frequency of one complex harmonic function in the time domain on the other. Thus the AR model of a time series is one that models the time series as a linear combination of complex harmonic functions, which include pure sinusoids and real exponentials as special cases. An AR model is completely determined by its z-domain pole configuration. The maximum-entropy/autogressive (ME/AR) spectrum, defined on the unit circle of the z-plane (or the frequency domain), is nothing but a convenient, but ambiguous visual representation. It is asserted that the position and shape of a spectral peak is determined by the corresponding complex frequency, and the height of the spectral peak contains little information about the complex amplitude of the complex harmonic functions.
Modeling financial time series with S-plus
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...
Clustering Multivariate Time Series Using Hidden Markov Models
Directory of Open Access Journals (Sweden)
Shima Ghassempour
2014-03-01
Full Text Available In this paper we describe an algorithm for clustering multivariate time series with variables taking both categorical and continuous values. Time series of this type are frequent in health care, where they represent the health trajectories of individuals. The problem is challenging because categorical variables make it difficult to define a meaningful distance between trajectories. We propose an approach based on Hidden Markov Models (HMMs, where we first map each trajectory into an HMM, then define a suitable distance between HMMs and finally proceed to cluster the HMMs with a method based on a distance matrix. We test our approach on a simulated, but realistic, data set of 1,255 trajectories of individuals of age 45 and over, on a synthetic validation set with known clustering structure, and on a smaller set of 268 trajectories extracted from the longitudinal Health and Retirement Survey. The proposed method can be implemented quite simply using standard packages in R and Matlab and may be a good candidate for solving the difficult problem of clustering multivariate time series with categorical variables using tools that do not require advanced statistic knowledge, and therefore are accessible to a wide range of researchers.
Assimilation of LAI time-series in crop production models
Kooistra, Lammert; Rijk, Bert; Nannes, Louis
2014-05-01
Agriculture is worldwide a large consumer of freshwater, nutrients and land. Spatial explicit agricultural management activities (e.g., fertilization, irrigation) could significantly improve efficiency in resource use. In previous studies and operational applications, remote sensing has shown to be a powerful method for spatio-temporal monitoring of actual crop status. As a next step, yield forecasting by assimilating remote sensing based plant variables in crop production models would improve agricultural decision support both at the farm and field level. In this study we investigated the potential of remote sensing based Leaf Area Index (LAI) time-series assimilated in the crop production model LINTUL to improve yield forecasting at field level. The effect of assimilation method and amount of assimilated observations was evaluated. The LINTUL-3 crop production model was calibrated and validated for a potato crop on two experimental fields in the south of the Netherlands. A range of data sources (e.g., in-situ soil moisture and weather sensors, destructive crop measurements) was used for calibration of the model for the experimental field in 2010. LAI from cropscan field radiometer measurements and actual LAI measured with the LAI-2000 instrument were used as input for the LAI time-series. The LAI time-series were assimilated in the LINTUL model and validated for a second experimental field on which potatoes were grown in 2011. Yield in 2011 was simulated with an R2 of 0.82 when compared with field measured yield. Furthermore, we analysed the potential of assimilation of LAI into the LINTUL-3 model through the 'updating' assimilation technique. The deviation between measured and simulated yield decreased from 9371 kg/ha to 8729 kg/ha when assimilating weekly LAI measurements in the LINTUL model over the season of 2011. LINTUL-3 furthermore shows the main growth reducing factors, which are useful for farm decision support. The combination of crop models and sensor
Empirical intrinsic geometry for nonlinear modeling and time series filtering.
Talmon, Ronen; Coifman, Ronald R
2013-07-30
In this paper, we present a method for time series analysis based on empirical intrinsic geometry (EIG). EIG enables one to reveal the low-dimensional parametric manifold as well as to infer the underlying dynamics of high-dimensional time series. By incorporating concepts of information geometry, this method extends existing geometric analysis tools to support stochastic settings and parametrizes the geometry of empirical distributions. However, the statistical models are not required as priors; hence, EIG may be applied to a wide range of real signals without existing definitive models. We show that the inferred model is noise-resilient and invariant under different observation and instrumental modalities. In addition, we show that it can be extended efficiently to newly acquired measurements in a sequential manner. These two advantages enable us to revisit the Bayesian approach and incorporate empirical dynamics and intrinsic geometry into a nonlinear filtering framework. We show applications to nonlinear and non-Gaussian tracking problems as well as to acoustic signal localization.
Hepburn, Larry; Shin, Masako
This document, one of eight in a multi-cultural competency-based vocational/technical curricula series, is on food service. This program is designed to run 24 weeks and cover 15 instructional areas: orientation, sanitation, management/planning, preparing food for cooking, preparing beverages, cooking eggs, cooking meat, cooking vegetables,…
Hepburn, Larry; Shin, Masako
This document, one of eight in a multi-cultural competency-based vocational/technical curricula series, is on automotive mechanics. This program is designed to run 36 weeks and cover 10 instructional areas: the engine; drive trains--rear ends/drive shafts/manual transmission; carburetor; emission; ignition/tune-up; charging and starting;…
Empirical investigation on modeling solar radiation series with ARMA–GARCH models
International Nuclear Information System (INIS)
Sun, Huaiwei; Yan, Dong; Zhao, Na; Zhou, Jianzhong
2015-01-01
Highlights: • Apply 6 ARMA–GARCH(-M) models to model and forecast solar radiation. • The ARMA–GARCH(-M) models produce more accurate radiation forecasting than conventional methods. • Show that ARMA–GARCH-M models are more effective for forecasting solar radiation mean and volatility. • The ARMA–EGARCH-M is robust and the ARMA–sGARCH-M is very competitive. - Abstract: Simulation of radiation is one of the most important issues in solar utilization. Time series models are useful tools in the estimation and forecasting of solar radiation series and their changes. In this paper, the effectiveness of autoregressive moving average (ARMA) models with various generalized autoregressive conditional heteroskedasticity (GARCH) processes, namely ARMA–GARCH models are evaluated for their effectiveness in radiation series. Six different GARCH approaches, which contain three different ARMA–GARCH models and corresponded GARCH in mean (ARMA–GARCH-M) models, are applied in radiation data sets from two representative climate stations in China. Multiple evaluation metrics of modeling sufficiency are used for evaluating the performances of models. The results show that the ARMA–GARCH(-M) models are effective in radiation series estimation. Both in fitting and prediction of radiation series, the ARMA–GARCH(-M) models show better modeling sufficiency than traditional models, while ARMA–EGARCH-M models are robustness in two sites and the ARMA–sGARCH-M models appear very competitive. Comparisons of statistical diagnostics and model performance clearly show that the ARMA–GARCH-M models make the mean radiation equations become more sufficient. It is recommended the ARMA–GARCH(-M) models to be the preferred method to use in the modeling of solar radiation series
Directory of Open Access Journals (Sweden)
Kansuporn eSriyudthsak
2016-05-01
Full Text Available The high-throughput acquisition of metabolome data is greatly anticipated for the complete understanding of cellular metabolism in living organisms. A variety of analytical technologies have been developed to acquire large-scale metabolic profiles under different biological or environmental conditions. Time series data are useful for predicting the most likely metabolic pathways because they provide important information regarding the accumulation of metabolites, which implies causal relationships in the metabolic reaction network. Considerable effort has been undertaken to utilize these data for constructing a mathematical model merging system properties and quantitatively characterizing a whole metabolic system in toto. However, there are technical difficulties between benchmarking the provision and utilization of data. Although hundreds of metabolites can be measured, which provide information on the metabolic reaction system, simultaneous measurement of thousands of metabolites is still challenging. In addition, it is nontrivial to logically predict the dynamic behaviors of unmeasurable metabolite concentrations without sufficient information on the metabolic reaction network. Yet, consolidating the advantages of advancements in both metabolomics and mathematical modeling remain to be accomplished. This review outlines the conceptual basis of and recent advances in technologies in both the research fields. It also highlights the potential for constructing a large-scale mathematical model by estimating model parameters from time series metabolome data in order to comprehensively understand metabolism at the systems level.
Sriyudthsak, Kansuporn; Shiraishi, Fumihide; Hirai, Masami Yokota
2016-01-01
The high-throughput acquisition of metabolome data is greatly anticipated for the complete understanding of cellular metabolism in living organisms. A variety of analytical technologies have been developed to acquire large-scale metabolic profiles under different biological or environmental conditions. Time series data are useful for predicting the most likely metabolic pathways because they provide important information regarding the accumulation of metabolites, which implies causal relationships in the metabolic reaction network. Considerable effort has been undertaken to utilize these data for constructing a mathematical model merging system properties and quantitatively characterizing a whole metabolic system in toto. However, there are technical difficulties between benchmarking the provision and utilization of data. Although, hundreds of metabolites can be measured, which provide information on the metabolic reaction system, simultaneous measurement of thousands of metabolites is still challenging. In addition, it is nontrivial to logically predict the dynamic behaviors of unmeasurable metabolite concentrations without sufficient information on the metabolic reaction network. Yet, consolidating the advantages of advancements in both metabolomics and mathematical modeling remain to be accomplished. This review outlines the conceptual basis of and recent advances in technologies in both the research fields. It also highlights the potential for constructing a large-scale mathematical model by estimating model parameters from time series metabolome data in order to comprehensively understand metabolism at the systems level.
Incorporating Satellite Time-Series Data into Modeling
Gregg, Watson
2008-01-01
In situ time series observations have provided a multi-decadal view of long-term changes in ocean biology. These observations are sufficiently reliable to enable discernment of even relatively small changes, and provide continuous information on a host of variables. Their key drawback is their limited domain. Satellite observations from ocean color sensors do not suffer the drawback of domain, and simultaneously view the global oceans. This attribute lends credence to their use in global and regional model validation and data assimilation. We focus on these applications using the NASA Ocean Biogeochemical Model. The enhancement of the satellite data using data assimilation is featured and the limitation of tongterm satellite data sets is also discussed.
International Nuclear Information System (INIS)
2015-11-01
The demands on nuclear fuel have recently been increasing, and include transient regimes, higher discharge burnup and longer fuel cycles. This has resulted in an increase of loads on fuel and core internals. In order to satisfy these demands while ensuring compliance with safety criteria, new national and international programmes have been launched and advanced modelling codes are being developed. The Fukushima Daiichi accident has particularly demonstrated the need for adequate analysis of all aspects of fuel performance to prevent a failure and also to predict fuel behaviour were an accident to occur.This publication presents the Proceedings of the Technical Meeting on Modelling of Water Cooled Fuel Including Design Basis and Severe Accidents, which was hosted by the Nuclear Power Institute of China (NPIC) in Chengdu, China, following the recommendation made in 2013 at the IAEA Technical Working Group on Fuel Performance and Technology. This recommendation was in agreement with IAEA mid-term initiatives, linked to the post-Fukushima IAEA Nuclear Safety Action Plan, as well as the forthcoming Coordinated Research Project (CRP) on Fuel Modelling in Accident Conditions. At the technical meeting in Chengdu, major areas and physical phenomena, as well as types of code and experiment to be studied and used in the CRP, were discussed. The technical meeting provided a forum for international experts to review the state of the art of code development for modelling fuel performance of nuclear fuel for water cooled reactors with regard to steady state and transient conditions, and for design basis and early phases of severe accidents, including experimental support for code validation. A round table discussion focused on the needs and perspectives on fuel modelling in accident conditions. This meeting was the ninth in a series of IAEA meetings, which reflects Member States’ continuing interest in nuclear fuel issues. The previous meetings were held in 1980 (jointly with
Self-organising mixture autoregressive model for non-stationary time series modelling.
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.
Technical note: Bayesian calibration of dynamic ruminant nutrition models.
Reed, K F; Arhonditsis, G B; France, J; Kebreab, E
2016-08-01
Mechanistic models of ruminant digestion and metabolism have advanced our understanding of the processes underlying ruminant animal physiology. Deterministic modeling practices ignore the inherent variation within and among individual animals and thus have no way to assess how sources of error influence model outputs. We introduce Bayesian calibration of mathematical models to address the need for robust mechanistic modeling tools that can accommodate error analysis by remaining within the bounds of data-based parameter estimation. For the purpose of prediction, the Bayesian approach generates a posterior predictive distribution that represents the current estimate of the value of the response variable, taking into account both the uncertainty about the parameters and model residual variability. Predictions are expressed as probability distributions, thereby conveying significantly more information than point estimates in regard to uncertainty. Our study illustrates some of the technical advantages of Bayesian calibration and discusses the future perspectives in the context of animal nutrition modeling. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.
Xing, X.; Yuan, Z.; Chen, L. F.; Yu, X. Y.; Xiao, L.
2018-04-01
The stability control is one of the major technical difficulties in the field of highway subgrade construction engineering. Building deformation model is a crucial step for InSAR time series deformation monitoring. Most of the InSAR deformation models for deformation monitoring are pure empirical mathematical models, without considering the physical mechanism of the monitored object. In this study, we take rheology into consideration, inducing rheological parameters into traditional InSAR deformation models. To assess the feasibility and accuracy for our new model, both simulation and real deformation data over Lungui highway (a typical highway built on soft clay subgrade in Guangdong province, China) are investigated with TerraSAR-X satellite imagery. In order to solve the unknows of the non-linear rheological model, three algorithms: Gauss-Newton (GN), Levenberg-Marquarat (LM), and Genetic Algorithm (GA), are utilized and compared to estimate the unknown parameters. Considering both the calculation efficiency and accuracy, GA is chosen as the final choice for the new model in our case study. Preliminary real data experiment is conducted with use of 17 TerraSAR-X Stripmap images (with a 3-m resolution). With the new deformation model and GA aforementioned, the unknown rheological parameters over all the high coherence points are obtained and the LOS deformation (the low-pass component) sequences are generated.
Modelling Socio-Technical Aspects of Organisational Security
DEFF Research Database (Denmark)
Ivanova, Marieta Georgieva
Identification of threats to organisations and risk assessment often take into consideration the pure technical aspects, overlooking the vulnerabilities originating from attacks on a social level, for example social engineering, and abstracting away the physical infrastructure. However, attacks...... would close this gap, however, it would also result in complicating the formal treatment and automatic identification of attacks. This dissertation shows that applying a system modelling approach to sociotechnical systems can be used for identifying attacks on organisations, which exploit various levels...... process calculus, we develop a formal analytical approach that generates attack trees from the model. The overall goal of the framework is to predict, prioritise and minimise the vulnerabilities in organisations by prohibiting the overall attack or at least increasing the difficulty and cost of fulfilling...
Time series modelling and forecasting of emergency department overcrowding.
Kadri, Farid; Harrou, Fouzi; Chaabane, Sondès; Tahon, Christian
2014-09-01
Efficient management of patient flow (demand) in emergency departments (EDs) has become an urgent issue for many hospital administrations. Today, more and more attention is being paid to hospital management systems to optimally manage patient flow and to improve management strategies, efficiency and safety in such establishments. To this end, EDs require significant human and material resources, but unfortunately these are limited. Within such a framework, the ability to accurately forecast demand in emergency departments has considerable implications for hospitals to improve resource allocation and strategic planning. The aim of this study was to develop models for forecasting daily attendances at the hospital emergency department in Lille, France. The study demonstrates how time-series analysis can be used to forecast, at least in the short term, demand for emergency services in a hospital emergency department. The forecasts were based on daily patient attendances at the paediatric emergency department in Lille regional hospital centre, France, from January 2012 to December 2012. An autoregressive integrated moving average (ARIMA) method was applied separately to each of the two GEMSA categories and total patient attendances. Time-series analysis was shown to provide a useful, readily available tool for forecasting emergency department demand.
Modeling technical change in climate analysis: evidence from agricultural crop damages.
Ahmed, Adeel; Devadason, Evelyn S; Al-Amin, Abul Quasem
2017-05-01
This study accounts for the Hicks neutral technical change in a calibrated model of climate analysis, to identify the optimum level of technical change for addressing climate changes. It demonstrates the reduction to crop damages, the costs to technical change, and the net gains for the adoption of technical change for a climate-sensitive Pakistan economy. The calibrated model assesses the net gains of technical change for the overall economy and at the agriculture-specific level. The study finds that the gains of technical change are overwhelmingly higher than the costs across the agriculture subsectors. The gains and costs following technical change differ substantially for different crops. More importantly, the study finds a cost-effective optimal level of technical change that potentially reduces crop damages to a minimum possible level. The study therefore contends that the climate policy for Pakistan should consider the role of technical change in addressing climate impacts on the agriculture sector.
Crowd Sourcing for Challenging Technical Problems and Business Model
Davis, Jeffrey R.; Richard, Elizabeth
2011-01-01
Crowd sourcing may be defined as the act of outsourcing tasks that are traditionally performed by an employee or contractor to an undefined, generally large group of people or community (a crowd) in the form of an open call. The open call may be issued by an organization wishing to find a solution to a particular problem or complete a task, or by an open innovation service provider on behalf of that organization. In 2008, the Space Life Sciences Directorate (SLSD), with the support of Wyle Integrated Science and Engineering, established and implemented pilot projects in open innovation (crowd sourcing) to determine if these new internet-based platforms could indeed find solutions to difficult technical challenges. These unsolved technical problems were converted to problem statements, also called "Challenges" or "Technical Needs" by the various open innovation service providers, and were then posted externally to seek solutions. In addition, an open call was issued internally to NASA employees Agency wide (10 Field Centers and NASA HQ) using an open innovation service provider crowd sourcing platform to post NASA challenges from each Center for the others to propose solutions). From 2008 to 2010, the SLSD issued 34 challenges, 14 externally and 20 internally. The 14 external problems or challenges were posted through three different vendors: InnoCentive, Yet2.com and TopCoder. The 20 internal challenges were conducted using the InnoCentive crowd sourcing platform designed for internal use by an organization. This platform was customized for NASA use and promoted as NASA@Work. The results were significant. Of the seven InnoCentive external challenges, two full and five partial awards were made in complex technical areas such as predicting solar flares and long-duration food packaging. Similarly, the TopCoder challenge yielded an optimization algorithm for designing a lunar medical kit. The Yet2.com challenges yielded many new industry and academic contacts in bone
Optimal model-free prediction from multivariate time series
Runge, Jakob; Donner, Reik V.; Kurths, Jürgen
2015-05-01
Forecasting a time series from multivariate predictors constitutes a challenging problem, especially using model-free approaches. Most techniques, such as nearest-neighbor prediction, quickly suffer from the curse of dimensionality and overfitting for more than a few predictors which has limited their application mostly to the univariate case. Therefore, selection strategies are needed that harness the available information as efficiently as possible. Since often the right combination of predictors matters, ideally all subsets of possible predictors should be tested for their predictive power, but the exponentially growing number of combinations makes such an approach computationally prohibitive. Here a prediction scheme that overcomes this strong limitation is introduced utilizing a causal preselection step which drastically reduces the number of possible predictors to the most predictive set of causal drivers making a globally optimal search scheme tractable. The information-theoretic optimality is derived and practical selection criteria are discussed. As demonstrated for multivariate nonlinear stochastic delay processes, the optimal scheme can even be less computationally expensive than commonly used suboptimal schemes like forward selection. The method suggests a general framework to apply the optimal model-free approach to select variables and subsequently fit a model to further improve a prediction or learn statistical dependencies. The performance of this framework is illustrated on a climatological index of El Niño Southern Oscillation.
Modelling Technical and Economic Parameters in Selection of Manufacturing Devices
Directory of Open Access Journals (Sweden)
Naqib Daneshjo
2017-11-01
Full Text Available Sustainable science and technology development is also conditioned by continuous development of means of production which have a key role in structure of each production system. Mechanical nature of the means of production is complemented by controlling and electronic devices in context of intelligent industry. A selection of production machines for a technological process or technological project has so far been practically resolved, often only intuitively. With regard to increasing intelligence, the number of variable parameters that have to be considered when choosing a production device is also increasing. It is necessary to use computing techniques and decision making methods according to heuristic methods and more precise methodological procedures during the selection. The authors present an innovative model for optimization of technical and economic parameters in the selection of manufacturing devices for industry 4.0.
High-order fuzzy time-series based on multi-period adaptation model for forecasting stock markets
Chen, Tai-Liang; Cheng, Ching-Hsue; Teoh, Hia-Jong
2008-02-01
Stock investors usually make their short-term investment decisions according to recent stock information such as the late market news, technical analysis reports, and price fluctuations. To reflect these short-term factors which impact stock price, this paper proposes a comprehensive fuzzy time-series, which factors linear relationships between recent periods of stock prices and fuzzy logical relationships (nonlinear relationships) mined from time-series into forecasting processes. In empirical analysis, the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) and HSI (Heng Seng Index) are employed as experimental datasets, and four recent fuzzy time-series models, Chen’s (1996), Yu’s (2005), Cheng’s (2006) and Chen’s (2007), are used as comparison models. Besides, to compare with conventional statistic method, the method of least squares is utilized to estimate the auto-regressive models of the testing periods within the databases. From analysis results, the performance comparisons indicate that the multi-period adaptation model, proposed in this paper, can effectively improve the forecasting performance of conventional fuzzy time-series models which only factor fuzzy logical relationships in forecasting processes. From the empirical study, the traditional statistic method and the proposed model both reveal that stock price patterns in the Taiwan stock and Hong Kong stock markets are short-term.
Some aspects of technical models within bridge management system
Directory of Open Access Journals (Sweden)
Grković Slobodan
2015-01-01
Full Text Available Bridge Management System (BMS represents a rational and systematic approach to organizing and conducting of all activities related to bridge maintenance. Main goal of BMS is helping the bridge owner to make an optimal decision with respect to bridge maintenance budget, whether it is dedicated to one bridge or to group of bridges, by securing that the decision is made on the basis of Life-Cycle Cost (LCC estimates. The structure of BMS is based on condition rating and Bridge Database (BD, Deterioration model (DM, Cost Model for evaluation of costs and Optimization Model for choosing the most rational maintenance strategy. Relationship between bridge condition rating and DM with maintenance costs within BMS is very important. Predictions regarding future intensity and rate of bridge deterioration depends on multitude of factors and it is a consequence of several simultaneous actions and deterioration processes (DP which need to be included into DMs. DMs are mostly based on modeling of physical and chemical actions and processes or they are based on statistical analysis of large number of data regarding the condition of existing bridges, or on artificial intelligence models, etc. Stochastic models based on Markov processes are applied within more advanced contemporary BMS. Due to social and economic circumstances and lack of financial resources over the last two decades in Republic of Serbia bridge maintenance was neglected and creation of BD was discontinued. The paper deliberates some aspects of technical models within BMS, with DMs being pointed out, development of BD within bridge maintenance in Republic of Serbia and it gives an overview of sophisticated BMS and current advances in this field.
An SVM model with hybrid kernels for hydrological time series
Wang, C.; Wang, H.; Zhao, X.; Xie, Q.
2017-12-01
Support Vector Machine (SVM) models have been widely applied to the forecast of climate/weather and its impact on other environmental variables such as hydrologic response to climate/weather. When using SVM, the choice of the kernel function plays the key role. Conventional SVM models mostly use one single type of kernel function, e.g., radial basis kernel function. Provided that there are several featured kernel functions available, each having its own advantages and drawbacks, a combination of these kernel functions may give more flexibility and robustness to SVM approach, making it suitable for a wide range of application scenarios. This paper presents such a linear combination of radial basis kernel and polynomial kernel for the forecast of monthly flowrate in two gaging stations using SVM approach. The results indicate significant improvement in the accuracy of predicted series compared to the approach with either individual kernel function, thus demonstrating the feasibility and advantages of such hybrid kernel approach for SVM applications.
Promoting Creative Thinking Ability Using Contextual Learning Model in Technical Drawing Achievement
Mursid, R.
2018-02-01
The purpose of this study is to determine whether there is influence; the differences in the results between students that learn drawing techniques taught by the Contextual Innovative Model (CIM) and taught by Direct Instructional Model (DIM), the differences in achievement among students of technical drawing that have High Creative Thinking Ability (HCTA) with Low Creative Thinking Ability (LCTA), and the interaction between the learning model with the ability to think creatively to the achievement technical drawing. Quasi-experimental research method. Results of research appoint that: the achievement of students that learned technical drawing by using CIM is higher than the students that learned technical drawing by using DIM, the achievement of students of technical drawings HCTA is higher than the achievement of students who have technical drawing LCTA, and there are interactions between the use of learning models and creative thinking abilities in influencing student achievement technical drawing.
Exploring the Benefits of Teacher-Modeling Strategies Integrated into Career and Technical Education
Cathers, Thomas J., Sr.
2013-01-01
This case study examined how career and technical education classes function using multiple instructional modeling strategies integrated into vocational and technical training environments. Seven New Jersey public school technical teachers received an introductory overview of the investigation and participated by responding to 10 open-end…
Thin stillage fractionation using ultrafiltration: resistance in series model.
Arora, Amit; Dien, Bruce S; Belyea, Ronald L; Wang, Ping; Singh, Vijay; Tumbleson, M E; Rausch, Kent D
2009-02-01
The corn based dry grind process is the most widely used method in the US for fuel ethanol production. Fermentation of corn to ethanol produces whole stillage after ethanol is removed by distillation. It is centrifuged to separate thin stillage from wet grains. Thin stillage contains 5-10% solids. To concentrate solids of thin stillage, it requires evaporation of large amounts of water and maintenance of evaporators. Evaporator maintenance requires excess evaporator capacity at the facility, increasing capital expenses, requiring plant slowdowns or shut downs and results in revenue losses. Membrane filtration is one method that could lead to improved value of thin stillage and may offer an alternative to evaporation. Fractionation of thin stillage using ultrafiltration was conducted to evaluate membranes as an alternative to evaporators in the ethanol industry. Two regenerated cellulose membranes with molecular weight cut offs of 10 and 100 kDa were evaluated. Total solids (suspended and soluble) contents recovered through membrane separation process were similar to those from commercial evaporators. Permeate flux decline of thin stillage using a resistance in series model was determined. Each of the four components of total resistance was evaluated experimentally. Effects of operating variables such as transmembrane pressure and temperature on permeate flux rate and resistances were determined and optimum conditions for maximum flux rates were evaluated. Model equations were developed to evaluate the resistance components that are responsible for fouling and to predict total flux decline with respect to time. Modeling results were in agreement with experimental results (R(2) > 0.98).
Neural network modeling of nonlinear systems based on Volterra series extension of a linear model
Soloway, Donald I.; Bialasiewicz, Jan T.
1992-01-01
A Volterra series approach was applied to the identification of nonlinear systems which are described by a neural network model. A procedure is outlined by which a mathematical model can be developed from experimental data obtained from the network structure. Applications of the results to the control of robotic systems are discussed.
Modelling transport energy demand: A socio-technical approach
International Nuclear Information System (INIS)
Anable, Jillian; Brand, Christian; Tran, Martino; Eyre, Nick
2012-01-01
Despite an emerging consensus that societal energy consumption and related emissions are not only influenced by technical efficiency but also by lifestyles and socio-cultural factors, few attempts have been made to operationalise these insights in models of energy demand. This paper addresses that gap by presenting a scenario exercise using an integrated suite of sectoral and whole systems models to explore potential energy pathways in the UK transport sector. Techno-economic driven scenarios are contrasted with one in which social change is strongly influenced by concerns about energy use, the environment and well-being. The ‘what if’ Lifestyle scenario reveals a future in which distance travelled by car is reduced by 74% by 2050 and final energy demand from transport is halved compared to the reference case. Despite the more rapid uptake of electric vehicles and the larger share of electricity in final energy demand, it shows a future where electricity decarbonisation could be delayed. The paper illustrates the key trade-off between the more aggressive pursuit of purely technological fixes and demand reduction in the transport sector and concludes there are strong arguments for pursuing both demand and supply side solutions in the pursuit of emissions reduction and energy security.
Application of stochastic frontier approach model to assess technical ...
African Journals Online (AJOL)
Since almost all the arable land is under cultivation, future increase in maize production will heavily depend on technical efficiency and yield improvement rather than expansion in area under production. The main objective of this study was to determine the technical efficiency of smallholder maize production in Kenya.
Formal Modelling and Analysis of Socio-Technical Systems
Probst, Christian W.; Kammüller, Florian; Rydhof Hansen, René; Probst, Christian W.; Hankin, Chris; Rydhof Hansen, René
2015-01-01
Attacks on systems and organisations increasingly exploit human actors, for example through social engineering. This non-technical aspect of attacks complicates their formal treatment and automatic identification. Formalisation of human behaviour is difficult at best, and attacks on socio-technical
González Sotelo, V; Maculé, F; Minguell, J; Bergé, R; Franco, C; Sala-Blanch, X
2017-12-01
Total knee arthroplasty (TKA) is an operation with moderate to severe postoperative pain. The Fast-Track models employ local infiltration techniques with anaesthetics at high volumes (100-150ml). We proposed a genicular nerve block with low volume of local anaesthetic. The aim of our study is to evaluate the periarticular distribution of these blocks in a fresh cadaver model and to describe the technique in a preliminary group of patients submitted to TKA. In the anatomical phase, 4 genicular nerves (superior medial, superior lateral, inferior medial and inferior lateral) were blocked with 4ml of local anaesthetic with iodinated contrast and methylene blue in each (16ml in total). It was performed on a fresh cadaver and the distribution of the injected medium was evaluated by means of a CT-scan and coronal anatomical sections on both knees. The clinical phase included 12 patients scheduled for TKA. Ultrasound-guided block of the 4 genicular nerves was performed preoperatively and their clinical efficacy evaluated by assessing pain after the reversal of the spinal block and at 12h after the block. Pain was measured using the numerical scale and the need for rescue analgesia was evaluated. A wide periarticular distribution of contrast was observed by CT-scan, which was later evaluated in the coronal sections. The distribution followed the joint capsule without entering the joint, both in the femur and in the tibia. The pain after the reversal of the subarachnoid block was 2±1, requiring rescue analgesia in 42% of the patients. At 12h, the pain according to the numerical scale was 4±1, 33% required rescue analgesia. The administration of 4ml of local anaesthetic at the level of the 4 genicular nerves of the knee produces a wide periarticular distribution. Our preliminary data in a series of 12 patients undergoing TKA seems to be clinically effective. Nevertheless, extensive case series and comparative studies with local infiltration techniques with anaesthetics are
Czech Academy of Sciences Publication Activity Database
Štěpánek, P.; Zahradníček, P.; Huth, Radan
2011-01-01
Roč. 115, 1-2 (2011), s. 87-98 ISSN 0324-6329 R&D Projects: GA ČR GA205/08/1619 Institutional research plan: CEZ:AV0Z30420517 Keywords : data quality control * filling missing values * interpolation techniques * climatological time series Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 0.364, year: 2011 http://www.met.hu/en/ismeret-tar/kiadvanyok/idojaras/index.php?id=34
Kennedy, Curtis E; Turley, James P
2011-10-24
Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of candidate features; 9
time series modeling of daily abandoned calls in a call centre
African Journals Online (AJOL)
DJFLEX
Models for evaluating and predicting the short periodic time series in daily ... Ugwuowo (2006) proposed asymmetric angular- linear multivariate regression models, ..... Using the parameter estimates in Table 3, the fitted Fourier series model is ..... For the SARIMA model with the stochastic component also being white noise, ...
Modeling Financial Time Series Based on a Market Microstructure Model with Leverage Effect
Yanhui Xi; Hui Peng; Yemei Qin
2016-01-01
The basic market microstructure model specifies that the price/return innovation and the volatility innovation are independent Gaussian white noise processes. However, the financial leverage effect has been found to be statistically significant in many financial time series. In this paper, a novel market microstructure model with leverage effects is proposed. The model specification assumed a negative correlation in the errors between the price/return innovation and the volatility innovation....
vector bilinear autoregressive time series model and its superiority
African Journals Online (AJOL)
KEYWORDS: Linear time series, Autoregressive process, Autocorrelation function, Partial autocorrelation function,. Vector time .... important result on matrix algebra with respect to the spectral ..... application to covariance analysis of super-.
Energy Technology Data Exchange (ETDEWEB)
Underdal, Arild
1997-12-31
This report discusses in non-technical terms the overall architecture of a model that will be designed to enable the user to (1) explore systematically the political feasibility of alternative policy options and (2) to determine the set of politically feasible solutions in the global climate change negotiations. 25 refs., 2 figs., 1 tab.
The Exponential Model for the Spectrum of a Time Series: Extensions and Applications
DEFF Research Database (Denmark)
Proietti, Tommaso; Luati, Alessandra
The exponential model for the spectrum of a time series and its fractional extensions are based on the Fourier series expansion of the logarithm of the spectral density. The coefficients of the expansion form the cepstrum of the time series. After deriving the cepstrum of important classes of time...
Rotation in the Dynamic Factor Modeling of Multivariate Stationary Time Series.
Molenaar, Peter C. M.; Nesselroade, John R.
2001-01-01
Proposes a special rotation procedure for the exploratory dynamic factor model for stationary multivariate time series. The rotation procedure applies separately to each univariate component series of a q-variate latent factor series and transforms such a component, initially represented as white noise, into a univariate moving-average.…
Rotation in the dynamic factor modeling of multivariate stationary time series.
Molenaar, P.C.M.; Nesselroade, J.R.
2001-01-01
A special rotation procedure is proposed for the exploratory dynamic factor model for stationary multivariate time series. The rotation procedure applies separately to each univariate component series of a q-variate latent factor series and transforms such a component, initially represented as white
Formal modelling and analysis of socio-technical systems
DEFF Research Database (Denmark)
Probst, Christian W.; Kammüller, Florian; Hansen, Rene Rydhof
2016-01-01
systems are still mostly identified through brainstorming of experts. In this work we discuss several approaches to formalising socio-technical systems and their analysis. Starting from a flow logic-based analysis of the insider threat, we discuss how to include the socio aspects explicitly, and show......Attacks on systems and organisations increasingly exploit human actors, for example through social engineering. This non-technical aspect of attacks complicates their formal treatment and automatic identification. Formalisation of human behaviour is difficult at best, and attacks on socio-technical...... a formalisation that proves properties of this formalisation. On the formal side, our work closes the gap between formal and informal approaches to socio-technical systems. On the informal side, we show how to steal a birthday cake from a bakery by social engineering....
New Models for Forecasting Enrollments: Fuzzy Time Series and Neural Network Approaches.
Song, Qiang; Chissom, Brad S.
Since university enrollment forecasting is very important, many different methods and models have been proposed by researchers. Two new methods for enrollment forecasting are introduced: (1) the fuzzy time series model; and (2) the artificial neural networks model. Fuzzy time series has been proposed to deal with forecasting problems within a…
A Sandwich-Type Standard Error Estimator of SEM Models with Multivariate Time Series
Zhang, Guangjian; Chow, Sy-Miin; Ong, Anthony D.
2011-01-01
Structural equation models are increasingly used as a modeling tool for multivariate time series data in the social and behavioral sciences. Standard error estimators of SEM models, originally developed for independent data, require modifications to accommodate the fact that time series data are inherently dependent. In this article, we extend a…
Time series modeling and forecasting using memetic algorithms for regime-switching models.
Bergmeir, Christoph; Triguero, Isaac; Molina, Daniel; Aznarte, José Luis; Benitez, José Manuel
2012-11-01
In this brief, we present a novel model fitting procedure for the neuro-coefficient smooth transition autoregressive model (NCSTAR), as presented by Medeiros and Veiga. The model is endowed with a statistically founded iterative building procedure and can be interpreted in terms of fuzzy rule-based systems. The interpretability of the generated models and a mathematically sound building procedure are two very important properties of forecasting models. The model fitting procedure employed by the original NCSTAR is a combination of initial parameter estimation by a grid search procedure with a traditional local search algorithm. We propose a different fitting procedure, using a memetic algorithm, in order to obtain more accurate models. An empirical evaluation of the method is performed, applying it to various real-world time series originating from three forecasting competitions. The results indicate that we can significantly enhance the accuracy of the models, making them competitive to models commonly used in the field.
Using Disks as Models for Proofs of Series
Somchaipeng, Tongta; Kruatong, Tussatrin; Panijpan, Bhinyo
2012-01-01
Exploring and deriving proofs of closed-form expressions for series can be fun for students. However, for some students, a physical representation of such problems is more meaningful. Various approaches have been designed to help students visualize squares of sums and sums of squares; these approaches may be arithmetic-algebraic or combinatorial…
multivariate time series modeling of selected childhood diseases
African Journals Online (AJOL)
2016-06-17
Jun 17, 2016 ... KEYWORDS: Multivariate Approach, Pre-whitening, Vector Time Series, .... Alternatively, the process may be written in mean adjusted form as .... The AIC criterion asymptotically over estimates the order with positive probability, whereas the BIC and HQC criteria ... has the same asymptotic distribution as Ǫ.
A prediction method based on wavelet transform and multiple models fusion for chaotic time series
International Nuclear Information System (INIS)
Zhongda, Tian; Shujiang, Li; Yanhong, Wang; Yi, Sha
2017-01-01
In order to improve the prediction accuracy of chaotic time series, a prediction method based on wavelet transform and multiple models fusion is proposed. The chaotic time series is decomposed and reconstructed by wavelet transform, and approximate components and detail components are obtained. According to different characteristics of each component, least squares support vector machine (LSSVM) is used as predictive model for approximation components. At the same time, an improved free search algorithm is utilized for predictive model parameters optimization. Auto regressive integrated moving average model (ARIMA) is used as predictive model for detail components. The multiple prediction model predictive values are fusion by Gauss–Markov algorithm, the error variance of predicted results after fusion is less than the single model, the prediction accuracy is improved. The simulation results are compared through two typical chaotic time series include Lorenz time series and Mackey–Glass time series. The simulation results show that the prediction method in this paper has a better prediction.
Evolution of Black-Box Models Based on Volterra Series
Directory of Open Access Journals (Sweden)
Daniel D. Silveira
2015-01-01
Full Text Available This paper presents a historical review of the many behavioral models actually used to model radio frequency power amplifiers and a new classification of these behavioral models. It also discusses the evolution of these models, from a single polynomial to multirate Volterra models, presenting equations and estimation methods. New trends in RF power amplifier behavioral modeling are suggested.
2014-03-01
This study resulted in the development of the GASCAP model (the Greenhouse Gas Assessment : Spreadsheet for Transportation Capital Projects). This spreadsheet model provides a user-friendly interface for determining the greenhouse gas (GHG) emissions...
Predicting long-term catchment nutrient export: the use of nonlinear time series models
Valent, Peter; Howden, Nicholas J. K.; Szolgay, Jan; Komornikova, Magda
2010-05-01
After the Second World War the nitrate concentrations in European water bodies changed significantly as the result of increased nitrogen fertilizer use and changes in land use. However, in the last decades, as a consequence of the implementation of nitrate-reducing measures in Europe, the nitrate concentrations in water bodies slowly decrease. This causes that the mean and variance of the observed time series also changes with time (nonstationarity and heteroscedascity). In order to detect changes and properly describe the behaviour of such time series by time series analysis, linear models (such as autoregressive (AR), moving average (MA) and autoregressive moving average models (ARMA)), are no more suitable. Time series with sudden changes in statistical characteristics can cause various problems in the calibration of traditional water quality models and thus give biased predictions. Proper statistical analysis of these non-stationary and heteroscedastic time series with the aim of detecting and subsequently explaining the variations in their statistical characteristics requires the use of nonlinear time series models. This information can be then used to improve the model building and calibration of conceptual water quality model or to select right calibration periods in order to produce reliable predictions. The objective of this contribution is to analyze two long time series of nitrate concentrations of the rivers Ouse and Stour with advanced nonlinear statistical modelling techniques and compare their performance with traditional linear models of the ARMA class in order to identify changes in the time series characteristics. The time series were analysed with nonlinear models with multiple regimes represented by self-exciting threshold autoregressive (SETAR) and Markov-switching models (MSW). The analysis showed that, based on the value of residual sum of squares (RSS) in both datasets, SETAR and MSW models described the time-series better than models of the
Memos with Personality: A Model from British Technical Colleges.
Yee, Carole
1986-01-01
Notes that while American technical writing texts stress brevity and directness as important characteristics of business correspondence, British texts stress qualities of personality and courtesy, especially in the memo. Shows how to incorporate personality into correspondence, thereby building cooperation among colleagues. (FL)
Evaluation of nonlinearity and validity of nonlinear modeling for complex time series.
Suzuki, Tomoya; Ikeguchi, Tohru; Suzuki, Masuo
2007-10-01
Even if an original time series exhibits nonlinearity, it is not always effective to approximate the time series by a nonlinear model because such nonlinear models have high complexity from the viewpoint of information criteria. Therefore, we propose two measures to evaluate both the nonlinearity of a time series and validity of nonlinear modeling applied to it by nonlinear predictability and information criteria. Through numerical simulations, we confirm that the proposed measures effectively detect the nonlinearity of an observed time series and evaluate the validity of the nonlinear model. The measures are also robust against observational noises. We also analyze some real time series: the difference of the number of chickenpox and measles patients, the number of sunspots, five Japanese vowels, and the chaotic laser. We can confirm that the nonlinear model is effective for the Japanese vowel /a/, the difference of the number of measles patients, and the chaotic laser.
Competence Model and Modern Trends of Development of the Russian Institute of Technical Customer
Directory of Open Access Journals (Sweden)
Mishlanova Marina
2017-01-01
Full Text Available Article considers modern maintenance and development of the management actor by the investment-construction projects of the technical customer. Urgent problems of the formation of Institute of the technical customer establishment are allocated. Elementary competence model is presented: based competences of technical customer, model of the primary competence, example of the operational level of the model. Analysis of the development of the Institute of the technical customer was performed: compliance with current realities of investment-construction activities, improvement of contractual relations, compliance with international standards, state participation, creation of the single technical customer. Necessity of development of competence models for the urgent justification of professional standards is assessed. The possibility of modeling of the competencies and functions of technical customer in approach to the FIDIC-model was revealed. Possibility of usage of the competence model of the technical customer on the stage of building in terms of public-private partnership. Results show the direction for further researches.
Visser, H.; Molenaar, J.
1995-05-01
The detection of trends in climatological data has become central to the discussion on climate change due to the enhanced greenhouse effect. To prove detection, a method is needed (i) to make inferences on significant rises or declines in trends, (ii) to take into account natural variability in climate series, and (iii) to compare output from GCMs with the trends in observed climate data. To meet these requirements, flexible mathematical tools are needed. A structural time series model is proposed with which a stochastic trend, a deterministic trend, and regression coefficients can be estimated simultaneously. The stochastic trend component is described using the class of ARIMA models. The regression component is assumed to be linear. However, the regression coefficients corresponding with the explanatory variables may be time dependent to validate this assumption. The mathematical technique used to estimate this trend-regression model is the Kaiman filter. The main features of the filter are discussed.Examples of trend estimation are given using annual mean temperatures at a single station in the Netherlands (1706-1990) and annual mean temperatures at Northern Hemisphere land stations (1851-1990). The inclusion of explanatory variables is shown by regressing the latter temperature series on four variables: Southern Oscillation index (SOI), volcanic dust index (VDI), sunspot numbers (SSN), and a simulated temperature signal, induced by increasing greenhouse gases (GHG). In all analyses, the influence of SSN on global temperatures is found to be negligible. The correlations between temperatures and SOI and VDI appear to be negative. For SOI, this correlation is significant, but for VDI it is not, probably because of a lack of volcanic eruptions during the sample period. The relation between temperatures and GHG is positive, which is in agreement with the hypothesis of a warming climate because of increasing levels of greenhouse gases. The prediction performance of
Modeling Interdependent Socio-technical Networks via ABM Smart Grid Case
Worm, D.T.H.; Langley, D.J.; Becker, J.M.
2013-01-01
The objective of this paper is to improve scientific modeling of interdependent socio-technical networks. In these networks the interplay between technical or infrastructural elements on the one hand and social and behavioral aspects on the other hand, is of importance. Examples include electricity
Quality Concerns in Technical Education in India: A Quantifiable Quality Enabled Model
Gambhir, Victor; Wadhwa, N. C.; Grover, Sandeep
2016-01-01
Purpose: The paper aims to discuss current Technical Education scenarios in India. It proposes modelling the factors affecting quality in a technical institute and then applying a suitable technique for assessment, comparison and ranking. Design/methodology/approach: The paper chose graph theoretic approach for quantification of quality-enabled…
Fitting ARMA Time Series by Structural Equation Models.
van Buuren, Stef
1997-01-01
This paper outlines how the stationary ARMA (p,q) model (G. Box and G. Jenkins, 1976) can be specified as a structural equation model. Maximum likelihood estimates for the parameters in the ARMA model can be obtained by software for fitting structural equation models. The method is applied to three problem types. (SLD)
Extracting the relevant delays in time series modelling
DEFF Research Database (Denmark)
Goutte, Cyril
1997-01-01
selection, and more precisely stepwise forward selection. The method is compared to other forward selection schemes, as well as to a nonparametric tests aimed at estimating the embedding dimension of time series. The final application extends these results to the efficient estimation of FIR filters on some......In this contribution, we suggest a convenient way to use generalisation error to extract the relevant delays from a time-varying process, i.e. the delays that lead to the best prediction performance. We design a generalisation-based algorithm that takes its inspiration from traditional variable...
Alternative Approaches to Technical Efficiency Estimation in the Stochastic Frontier Model
Acquah, H. de-Graft; Onumah, E. E.
2014-01-01
Estimating the stochastic frontier model and calculating technical efficiency of decision making units are of great importance in applied production economic works. This paper estimates technical efficiency from the stochastic frontier model using Jondrow, and Battese and Coelli approaches. In order to compare alternative methods, simulated data with sample sizes of 60 and 200 are generated from stochastic frontier model commonly applied to agricultural firms. Simulated data is employed to co...
Time Series Modelling of Syphilis Incidence in China from 2005 to 2012.
Zhang, Xingyu; Zhang, Tao; Pei, Jiao; Liu, Yuanyuan; Li, Xiaosong; Medrano-Gracia, Pau
2016-01-01
The infection rate of syphilis in China has increased dramatically in recent decades, becoming a serious public health concern. Early prediction of syphilis is therefore of great importance for heath planning and management. In this paper, we analyzed surveillance time series data for primary, secondary, tertiary, congenital and latent syphilis in mainland China from 2005 to 2012. Seasonality and long-term trend were explored with decomposition methods. Autoregressive integrated moving average (ARIMA) was used to fit a univariate time series model of syphilis incidence. A separate multi-variable time series for each syphilis type was also tested using an autoregressive integrated moving average model with exogenous variables (ARIMAX). The syphilis incidence rates have increased three-fold from 2005 to 2012. All syphilis time series showed strong seasonality and increasing long-term trend. Both ARIMA and ARIMAX models fitted and estimated syphilis incidence well. All univariate time series showed highest goodness-of-fit results with the ARIMA(0,0,1)×(0,1,1) model. Time series analysis was an effective tool for modelling the historical and future incidence of syphilis in China. The ARIMAX model showed superior performance than the ARIMA model for the modelling of syphilis incidence. Time series correlations existed between the models for primary, secondary, tertiary, congenital and latent syphilis.
Time Series Modelling of Syphilis Incidence in China from 2005 to 2012
Zhang, Xingyu; Zhang, Tao; Pei, Jiao; Liu, Yuanyuan; Li, Xiaosong; Medrano-Gracia, Pau
2016-01-01
Background The infection rate of syphilis in China has increased dramatically in recent decades, becoming a serious public health concern. Early prediction of syphilis is therefore of great importance for heath planning and management. Methods In this paper, we analyzed surveillance time series data for primary, secondary, tertiary, congenital and latent syphilis in mainland China from 2005 to 2012. Seasonality and long-term trend were explored with decomposition methods. Autoregressive integrated moving average (ARIMA) was used to fit a univariate time series model of syphilis incidence. A separate multi-variable time series for each syphilis type was also tested using an autoregressive integrated moving average model with exogenous variables (ARIMAX). Results The syphilis incidence rates have increased three-fold from 2005 to 2012. All syphilis time series showed strong seasonality and increasing long-term trend. Both ARIMA and ARIMAX models fitted and estimated syphilis incidence well. All univariate time series showed highest goodness-of-fit results with the ARIMA(0,0,1)×(0,1,1) model. Conclusion Time series analysis was an effective tool for modelling the historical and future incidence of syphilis in China. The ARIMAX model showed superior performance than the ARIMA model for the modelling of syphilis incidence. Time series correlations existed between the models for primary, secondary, tertiary, congenital and latent syphilis. PMID:26901682
Modeling time-series data from microbial communities.
Ridenhour, Benjamin J; Brooker, Sarah L; Williams, Janet E; Van Leuven, James T; Miller, Aaron W; Dearing, M Denise; Remien, Christopher H
2017-11-01
As sequencing technologies have advanced, the amount of information regarding the composition of bacterial communities from various environments (for example, skin or soil) has grown exponentially. To date, most work has focused on cataloging taxa present in samples and determining whether the distribution of taxa shifts with exogenous covariates. However, important questions regarding how taxa interact with each other and their environment remain open thus preventing in-depth ecological understanding of microbiomes. Time-series data from 16S rDNA amplicon sequencing are becoming more common within microbial ecology, but methods to infer ecological interactions from these longitudinal data are limited. We address this gap by presenting a method of analysis using Poisson regression fit with an elastic-net penalty that (1) takes advantage of the fact that the data are time series; (2) constrains estimates to allow for the possibility of many more interactions than data; and (3) is scalable enough to handle data consisting of thousands of taxa. We test the method on gut microbiome data from white-throated woodrats (Neotoma albigula) that were fed varying amounts of the plant secondary compound oxalate over a period of 22 days to estimate interactions between OTUs and their environment.
Parametric, nonparametric and parametric modelling of a chaotic circuit time series
Timmer, J.; Rust, H.; Horbelt, W.; Voss, H. U.
2000-09-01
The determination of a differential equation underlying a measured time series is a frequently arising task in nonlinear time series analysis. In the validation of a proposed model one often faces the dilemma that it is hard to decide whether possible discrepancies between the time series and model output are caused by an inappropriate model or by bad estimates of parameters in a correct type of model, or both. We propose a combination of parametric modelling based on Bock's multiple shooting algorithm and nonparametric modelling based on optimal transformations as a strategy to test proposed models and if rejected suggest and test new ones. We exemplify this strategy on an experimental time series from a chaotic circuit where we obtain an extremely accurate reconstruction of the observed attractor.
Modeling technical efficiency of inshore fishery using data envelopment analysis
Rahman, Rahayu; Zahid, Zalina; Khairi, Siti Shaliza Mohd; Hussin, Siti Aida Sheikh
2016-10-01
Fishery industry contributes significantly to the economy of Malaysia. This study utilized Data Envelopment Analysis application in estimating the technical efficiency of fishery in Terengganu, a state on the eastern coast of Peninsular Malaysia, based on multiple output, i.e. total fish landing and income of fishermen with six inputs, i.e. engine power, vessel size, number of trips, number of workers, cost and operation distance. The data were collected by survey conducted between November and December 2014. The decision making units (DMUs) involved 100 fishermen from 10 fishery areas. The result showed that the technical efficiency in Season I (dry season) and Season II (rainy season) were 90.2% and 66.7% respectively. About 27% of the fishermen were rated to be efficient during Season I, meanwhile only 13% of the fishermen achieved full efficiency 100% during Season II. The results also found out that there was a significance difference in the efficiency performance between the fishery areas.
International Nuclear Information System (INIS)
Saegusa, Hiromitsu; Takeuchi, Shinji; Maekawa, Keisuke; Osawa, Hideaki; Semba, Takeshi
2011-01-01
It is important for site characterization projects to manage the decision-making process with transparency and traceability and to transfer the technical know-how accumulated during the research and development to the implementing phase and to future generations. The modeling for a geological environment is to be used to synthesize investigation results. Evaluation of the impact of uncertainties in the model is important to identify and prioritize key issues for further investigations. Therefore, a plan for site characterization should be made based on the results of the modeling. The aim of this study is to support for the planning of initial surface-based site characterization based on the technical know-how accumulated from the Mizunami Underground Research Laboratory Project and the Horonobe Underground Research Laboratory Project. These projects are broad scientific studies of the deep geological environment that are a basis for research and development for the geological disposal of high-level radioactive wastes. In this study, the work-flow of the groundwater flow modeling, which is one of the geological environment models, and is to be used for setting the area for the geological environment modeling and for groundwater flow characterization, and the related decision-making process using literature data have been summarized. (author)
Task technical plan: DWPF air permit/dispersion modeling
International Nuclear Information System (INIS)
Lambert, D.P.
1993-01-01
This Task Technical Plan summarizes work required to project the benzene emissions from the Late Wash Facility (LWF) as well as update the benzene, mercury, and NO x emissions from the remainder of the Defense Waste Processing Facility (DWPF). These calculations will reflect (1) the addition of the LWF and (2) the replacement of formic acid with nitric acid in the melter preparation process. The completed calculations will be used to assist DWPF in applying for the LWF Air Quality Permit
A generalized exponential time series regression model for electricity prices
DEFF Research Database (Denmark)
Haldrup, Niels; Knapik, Oskar; Proietti, Tomasso
on the estimated model, the best linear predictor is constructed. Our modeling approach provides good fit within sample and outperforms competing benchmark predictors in terms of forecasting accuracy. We also find that building separate models for each hour of the day and averaging the forecasts is a better...
Analysis and evaluation of the ASTEC model basis. Relevant experiments. Technical report
International Nuclear Information System (INIS)
Koppers, V.; Koch, M.K.
2015-12-01
The present report is a Technical Report within the research project ''ASMO'', funded by the German Federal Ministry of Economics and Technology (BMWi 1501433) and projected at the Reactor Simulation and Safety Group, Chair of Energy Systems and Energy Economics (LEE) at the Ruhr-Universitaet Bochum (RUB). The project deals with the analysis of the model basis of the Accident Source Term Evaluation Code (ASTEC). This report focuses on the containment part of ASTEC (CPA) and presents the simulation results of the experiment TH20.7. The experimental series TH20 was performed in the test vessel THAI (Thermal-hydraulics, Aerosols, Iodine) to investigate the erosion of a helium layer by a blower generated air jet. Helium is used as a substitute for hydrogen. In the experiment TH20.7 a light-gas layer is established and eroded by a momentum driven jet. The simulation of momentum driven jets is challenging for CPA because there is no model to simulate the kinetic momentum transfer. Subject of this report is the analysis of the capability of the code with the current model basis to model momentum driven phenomena. The jet is modelled using virtual ventilation systems, so called FAN-Systems. The FAN-Systems are adapted to the erosion velocity. The simulation results are compared to the experimental results and a basic calculation using FAN-Systems without any adjustments. For further improvement, different variation calculations are performed. At first, the vertical nodalization is refined. Subsequently, the resistance coefficients are adjusted to support the jet flow pattern and the number of the FAN-Systems is reduced. The analysis shows that the simulation of a momentum driven light-gas layer erosion is possible using adjusted FAN-Systems. A fine selected vertical nodalization and adaption of the resistance coefficients improves the simulation results.
Modeling sports highlights using a time-series clustering framework and model interpretation
Radhakrishnan, Regunathan; Otsuka, Isao; Xiong, Ziyou; Divakaran, Ajay
2005-01-01
In our past work on sports highlights extraction, we have shown the utility of detecting audience reaction using an audio classification framework. The audio classes in the framework were chosen based on intuition. In this paper, we present a systematic way of identifying the key audio classes for sports highlights extraction using a time series clustering framework. We treat the low-level audio features as a time series and model the highlight segments as "unusual" events in a background of an "usual" process. The set of audio classes to characterize the sports domain is then identified by analyzing the consistent patterns in each of the clusters output from the time series clustering framework. The distribution of features from the training data so obtained for each of the key audio classes, is parameterized by a Minimum Description Length Gaussian Mixture Model (MDL-GMM). We also interpret the meaning of each of the mixture components of the MDL-GMM for the key audio class (the "highlight" class) that is correlated with highlight moments. Our results show that the "highlight" class is a mixture of audience cheering and commentator's excited speech. Furthermore, we show that the precision-recall performance for highlights extraction based on this "highlight" class is better than that of our previous approach which uses only audience cheering as the key highlight class.
Energy Technology Data Exchange (ETDEWEB)
Noun, R. J.
1983-06-01
The SERI Wind Energy Program manages the areas or innovative research, wind systems analysis, and environmental compatibility for the U.S. Department of Energy. Since 1978, SERI wind program staff have conducted in-house aerodynamic and engineering analyses of novel concepts for wind energy conversion and have managed over 20 subcontracts to determine technical feasibility; the most promising of these concepts is the passive blade cyclic pitch control project. In the area of systems analysis, the SERI program has analyzed the impact of intermittent generation on the reliability of electric utility systems using standard utility planning models. SERI has also conducted methodology assessments. Environmental issues related to television interference and acoustic noise from large wind turbines have been addressed. SERI has identified the causes, effects, and potential control of acoustic noise emissions from large wind turbines.
Time Series ARIMA Models of Undergraduate Grade Point Average.
Rogers, Bruce G.
The Auto-Regressive Integrated Moving Average (ARIMA) Models, often referred to as Box-Jenkins models, are regression methods for analyzing sequential dependent observations with large amounts of data. The Box-Jenkins approach, a three-stage procedure consisting of identification, estimation and diagnosis, was used to select the most appropriate…
SEM based CARMA time series modeling for arbitrary N
Oud, J.H.L.; Völkle, M.C.; Driver, C.C.
2018-01-01
This article explains in detail the state space specification and estimation of first and higher-order autoregressive moving-average models in continuous time (CARMA) in an extended structural equation modeling (SEM) context for N = 1 as well as N > 1. To illustrate the approach, simulations will be
Modeling Large Time Series for Efficient Approximate Query Processing
DEFF Research Database (Denmark)
Perera, Kasun S; Hahmann, Martin; Lehner, Wolfgang
2015-01-01
query statistics derived from experiments and when running the system. Our approach can also reduce communication load by exchanging models instead of data. To allow seamless integration of model-based querying into traditional data warehouses, we introduce a SQL compatible query terminology. Our...
Modelling Changes in the Unconditional Variance of Long Stock Return Series
DEFF Research Database (Denmark)
Amado, Cristina; Teräsvirta, Timo
In this paper we develop a testing and modelling procedure for describing the long-term volatility movements over very long return series. For the purpose, we assume that volatility is multiplicatively decomposed into a conditional and an unconditional component as in Amado and Teräsvirta (2011...... show that the long-memory property in volatility may be explained by ignored changes in the unconditional variance of the long series. Finally, based on a formal statistical test we find evidence of the superiority of volatility forecast accuracy of the new model over the GJR-GARCH model at all...... horizons for a subset of the long return series....
Modelling changes in the unconditional variance of long stock return series
DEFF Research Database (Denmark)
Amado, Cristina; Teräsvirta, Timo
2014-01-01
In this paper we develop a testing and modelling procedure for describing the long-term volatility movements over very long daily return series. For this purpose we assume that volatility is multiplicatively decomposed into a conditional and an unconditional component as in Amado and Teräsvirta...... that the apparent long memory property in volatility may be interpreted as changes in the unconditional variance of the long series. Finally, based on a formal statistical test we find evidence of the superiority of volatility forecasting accuracy of the new model over the GJR-GARCH model at all horizons for eight...... subsets of the long return series....
Mixed Portmanteau Test for Diagnostic Checking of Time Series Models
Directory of Open Access Journals (Sweden)
Sohail Chand
2014-01-01
Full Text Available Model criticism is an important stage of model building and thus goodness of fit tests provides a set of tools for diagnostic checking of the fitted model. Several tests are suggested in literature for diagnostic checking. These tests use autocorrelation or partial autocorrelation in the residuals to criticize the adequacy of fitted model. The main idea underlying these portmanteau tests is to identify if there is any dependence structure which is yet unexplained by the fitted model. In this paper, we suggest mixed portmanteau tests based on autocorrelation and partial autocorrelation functions of the residuals. We derived the asymptotic distribution of the mixture test and studied its size and power using Monte Carlo simulations.
Technical review of the dispersion and dose models used in the MILDOS computer program
International Nuclear Information System (INIS)
Horst, T.W.; Soldat, J.K.; Bander, T.J.
1982-05-01
The MILDOS computer code is used to estimate impacts of radioactive emissions from uranium milling facilities. This report reviews the technical basis of the models used in the MILDOS computer code. The models were compared with state-of-the-art predictions, taking into account the intended uses of the MILDOS code. Several suggested modifications are presented and the technical basis for those changes are given
SEM Based CARMA Time Series Modeling for Arbitrary N.
Oud, Johan H L; Voelkle, Manuel C; Driver, Charles C
2018-01-01
This article explains in detail the state space specification and estimation of first and higher-order autoregressive moving-average models in continuous time (CARMA) in an extended structural equation modeling (SEM) context for N = 1 as well as N > 1. To illustrate the approach, simulations will be presented in which a single panel model (T = 41 time points) is estimated for a sample of N = 1,000 individuals as well as for samples of N = 100 and N = 50 individuals, followed by estimating 100 separate models for each of the one-hundred N = 1 cases in the N = 100 sample. Furthermore, we will demonstrate how to test the difference between the full panel model and each N = 1 model by means of a subject-group-reproducibility test. Finally, the proposed analyses will be applied in an empirical example, in which the relationships between mood at work and mood at home are studied in a sample of N = 55 women. All analyses are carried out by ctsem, an R-package for continuous time modeling, interfacing to OpenMx.
Time series modelling of the Kobe-Osaka earthquake recordings
Directory of Open Access Journals (Sweden)
N. Singh
2002-01-01
generated by an earthquake. With a view of comparing these two types of waveforms, Singh (1992 developed a technique for identifying a model in time domain. Fortunately this technique has been found useful in modelling the recordings of the killer earthquake occurred in the Kobe-Osaka region of Japan at 5.46 am on 17 January, 1995. The aim of the present study is to show how well the method for identifying a model (developed by Singh (1992 can be used for describing the vibrations of the above mentioned earthquake recorded at Charters Towers in Queensland, Australia.
Applying Time Series Analysis Model to Temperature Data in Greenhouses
Directory of Open Access Journals (Sweden)
Abdelhafid Hasni
2011-03-01
Full Text Available The objective of the research is to find an appropriate Seasonal Auto-Regressive Integrated Moving Average (SARIMA Model for fitting the inside air temperature (Tin of a naturally ventilated greenhouse under Mediterranean conditions by considering the minimum of Akaike Information Criterion (AIC. The results of fitting were as follows: the best SARIMA Model for fitting air temperature of greenhouse is SARIMA (1,0,0 (1,0,224.
Prahutama, Alan; Suparti; Wahyu Utami, Tiani
2018-03-01
Regression analysis is an analysis to model the relationship between response variables and predictor variables. The parametric approach to the regression model is very strict with the assumption, but nonparametric regression model isn’t need assumption of model. Time series data is the data of a variable that is observed based on a certain time, so if the time series data wanted to be modeled by regression, then we should determined the response and predictor variables first. Determination of the response variable in time series is variable in t-th (yt), while the predictor variable is a significant lag. In nonparametric regression modeling, one developing approach is to use the Fourier series approach. One of the advantages of nonparametric regression approach using Fourier series is able to overcome data having trigonometric distribution. In modeling using Fourier series needs parameter of K. To determine the number of K can be used Generalized Cross Validation method. In inflation modeling for the transportation sector, communication and financial services using Fourier series yields an optimal K of 120 parameters with R-square 99%. Whereas if it was modeled by multiple linear regression yield R-square 90%.
Liu, Zitao; Hauskrecht, Milos
2017-11-01
Building of an accurate predictive model of clinical time series for a patient is critical for understanding of the patient condition, its dynamics, and optimal patient management. Unfortunately, this process is not straightforward. First, patient-specific variations are typically large and population-based models derived or learned from many different patients are often unable to support accurate predictions for each individual patient. Moreover, time series observed for one patient at any point in time may be too short and insufficient to learn a high-quality patient-specific model just from the patient's own data. To address these problems we propose, develop and experiment with a new adaptive forecasting framework for building multivariate clinical time series models for a patient and for supporting patient-specific predictions. The framework relies on the adaptive model switching approach that at any point in time selects the most promising time series model out of the pool of many possible models, and consequently, combines advantages of the population, patient-specific and short-term individualized predictive models. We demonstrate that the adaptive model switching framework is very promising approach to support personalized time series prediction, and that it is able to outperform predictions based on pure population and patient-specific models, as well as, other patient-specific model adaptation strategies.
A neuro-fuzzy computing technique for modeling hydrological time series
Nayak, P. C.; Sudheer, K. P.; Rangan, D. M.; Ramasastri, K. S.
2004-05-01
Intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are proven to be efficient when applied individually to a variety of problems. Recently there has been a growing interest in combining both these approaches, and as a result, neuro-fuzzy computing techniques have evolved. This approach has been tested and evaluated in the field of signal processing and related areas, but researchers have only begun evaluating the potential of this neuro-fuzzy hybrid approach in hydrologic modeling studies. This paper presents the application of an adaptive neuro fuzzy inference system (ANFIS) to hydrologic time series modeling, and is illustrated by an application to model the river flow of Baitarani River in Orissa state, India. An introduction to the ANFIS modeling approach is also presented. The advantage of the method is that it does not require the model structure to be known a priori, in contrast to most of the time series modeling techniques. The results showed that the ANFIS forecasted flow series preserves the statistical properties of the original flow series. The model showed good performance in terms of various statistical indices. The results are highly promising, and a comparative analysis suggests that the proposed modeling approach outperforms ANNs and other traditional time series models in terms of computational speed, forecast errors, efficiency, peak flow estimation etc. It was observed that the ANFIS model preserves the potential of the ANN approach fully, and eases the model building process.
International Nuclear Information System (INIS)
Seguela, Patrick
2001-01-01
This research thesis addresses the problem of knowledge acquisition and structuring from technical texts, and the use of this knowledge in the development of models. The author presents the Cameleon method which aims at extracting binary lexical relationships from technical texts by identifying linguistic markers. The relevance of this method is assessed in the case of four different corpuses: a written technical corpus, an oral technical corpus, a corpus of texts of instructions, and a corpus of academic texts. The author reports the development of a model of representation of knowledge of a specific field by using lexical relationships. The method is then applied to develop a model used in document search within a knowledge management system [fr
Modeling activity patterns of wildlife using time-series analysis.
Zhang, Jindong; Hull, Vanessa; Ouyang, Zhiyun; He, Liang; Connor, Thomas; Yang, Hongbo; Huang, Jinyan; Zhou, Shiqiang; Zhang, Zejun; Zhou, Caiquan; Zhang, Hemin; Liu, Jianguo
2017-04-01
The study of wildlife activity patterns is an effective approach to understanding fundamental ecological and evolutionary processes. However, traditional statistical approaches used to conduct quantitative analysis have thus far had limited success in revealing underlying mechanisms driving activity patterns. Here, we combine wavelet analysis, a type of frequency-based time-series analysis, with high-resolution activity data from accelerometers embedded in GPS collars to explore the effects of internal states (e.g., pregnancy) and external factors (e.g., seasonal dynamics of resources and weather) on activity patterns of the endangered giant panda ( Ailuropoda melanoleuca ). Giant pandas exhibited higher frequency cycles during the winter when resources (e.g., water and forage) were relatively poor, as well as during spring, which includes the giant panda's mating season. During the summer and autumn when resources were abundant, pandas exhibited a regular activity pattern with activity peaks every 24 hr. A pregnant individual showed distinct differences in her activity pattern from other giant pandas for several months following parturition. These results indicate that animals adjust activity cycles to adapt to seasonal variation of the resources and unique physiological periods. Wavelet coherency analysis also verified the synchronization of giant panda activity level with air temperature and solar radiation at the 24-hr band. Our study also shows that wavelet analysis is an effective tool for analyzing high-resolution activity pattern data and its relationship to internal and external states, an approach that has the potential to inform wildlife conservation and management across species.
Model of a synthetic wind speed time series generator
DEFF Research Database (Denmark)
Negra, N.B.; Holmstrøm, O.; Bak-Jensen, B.
2008-01-01
is described and some statistical issues (seasonal characteristics, autocorrelation functions, average values and distribution functions) are used for verification. The output of the model has been designed as input for sequential Monte Carlo simulation; however, it is expected that it can be used for other...... of the main elements to consider for this purpose is the model of the wind speed that is usually required as input. Wind speed measurements may represent a solution for this problem, but, for techniques such as sequential Monte Carlo simulation, they have to be long enough in order to describe a wide range...
TIME SERIES MODELS OF THREE SETS OF RXTE OBSERVATIONS OF 4U 1543–47
International Nuclear Information System (INIS)
Koen, C.
2013-01-01
The X-ray nova 4U 1543–47 was in a different physical state (low/hard, high/soft, and very high) during the acquisition of each of the three time series analyzed in this paper. Standard time series models of the autoregressive moving average (ARMA) family are fitted to these series. The low/hard data can be adequately modeled by a simple low-order model with fixed coefficients, once the slowly varying mean count rate has been accounted for. The high/soft series requires a higher order model, or an ARMA model with variable coefficients. The very high state is characterized by a succession of 'dips', with roughly equal depths. These seem to appear independently of one another. The underlying stochastic series can again be modeled by an ARMA form, or roughly as the sum of an ARMA series and white noise. The structuring of each model in terms of short-lived aperiodic and 'quasi-periodic' components is discussed.
Multiband Prediction Model for Financial Time Series with Multivariate Empirical Mode Decomposition
Directory of Open Access Journals (Sweden)
Md. Rabiul Islam
2012-01-01
Full Text Available This paper presents a subband approach to financial time series prediction. Multivariate empirical mode decomposition (MEMD is employed here for multiband representation of multichannel financial time series together. Autoregressive moving average (ARMA model is used in prediction of individual subband of any time series data. Then all the predicted subband signals are summed up to obtain the overall prediction. The ARMA model works better for stationary signal. With multiband representation, each subband becomes a band-limited (narrow band signal and hence better prediction is achieved. The performance of the proposed MEMD-ARMA model is compared with classical EMD, discrete wavelet transform (DWT, and with full band ARMA model in terms of signal-to-noise ratio (SNR and mean square error (MSE between the original and predicted time series. The simulation results show that the MEMD-ARMA-based method performs better than the other methods.
Mathematical model of thyristor inverter including a series-parallel resonant circuit
Luft, M.; Szychta, E.
2008-01-01
The article presents a mathematical model of thyristor inverter including a series-parallel resonant circuit with the aid of state variable method. Maple procedures are used to compute current and voltage waveforms in the inverter.
Mathematical Model of Thyristor Inverter Including a Series-parallel Resonant Circuit
Miroslaw Luft; Elzbieta Szychta
2008-01-01
The article presents a mathematical model of thyristor inverter including a series-parallel resonant circuit with theaid of state variable method. Maple procedures are used to compute current and voltage waveforms in the inverter.
Mathematical Model of Thyristor Inverter Including a Series-parallel Resonant Circuit
Directory of Open Access Journals (Sweden)
Miroslaw Luft
2008-01-01
Full Text Available The article presents a mathematical model of thyristor inverter including a series-parallel resonant circuit with theaid of state variable method. Maple procedures are used to compute current and voltage waveforms in the inverter.
Danish wind turbines: Technical-economic feasibility of commercial models
International Nuclear Information System (INIS)
Falchetta, M.
1992-01-01
This feasibility study examines the principal technical and economic (investment-manufacturing-installation-operation unit costs, supply and demand) characteristics of wind turbines being commercialized in Denmark. The general configuration of the 150 to 450 kW range machines currently being manufactured can be described as that of a three bladed fibreglass rotor, of from 24 to 35 meters in diameter, and mounted on a tower of from 29 to 41 meters in height. The electrical system consists of one asynchronous generator or a two generator system with a power ratio of 1 to 5 between the two generators. The cost analysis reveals that the Danish wind turbines are competitively priced, with per kWh costs varying from $0. 0675 to $0. 040 for operating wind speeds ranging from 5 to 7 m/sec, and that their overall design and performance characteristics make them suitable for Italian site conditions
Linear models for multivariate, time series, and spatial data
Christensen, Ronald
1991-01-01
This is a companion volume to Plane Answers to Complex Questions: The Theory 0/ Linear Models. It consists of six additional chapters written in the same spirit as the last six chapters of the earlier book. Brief introductions are given to topics related to linear model theory. No attempt is made to give a comprehensive treatment of the topics. Such an effort would be futile. Each chapter is on a topic so broad that an in depth discussion would require a book-Iength treatment. People need to impose structure on the world in order to understand it. There is a limit to the number of unrelated facts that anyone can remem ber. If ideas can be put within a broad, sophisticatedly simple structure, not only are they easier to remember but often new insights become avail able. In fact, sophisticatedly simple models of the world may be the only ones that work. I have often heard Arnold Zellner say that, to the best of his knowledge, this is true in econometrics. The process of modeling is fundamental to understand...
Monitoring Poisson time series using multi-process models
DEFF Research Database (Denmark)
Engebjerg, Malene Dahl Skov; Lundbye-Christensen, Søren; Kjær, Birgitte B.
aspects of health resource management may also be addressed. In this paper we center on the detection of outbreaks of infectious diseases. This is achieved by a multi-process Poisson state space model taking autocorrelation and overdispersion into account, which has been applied to a data set concerning...
International Nuclear Information System (INIS)
Wallace, Adam N.; Pacheco, Rafael A.; Tomasian, Anderanik; Hsi, Andy C.; Long, Jeremiah; Chang, Randy O.; Jennings, Jack W.
2016-01-01
BackgroundA novel coaxial biopsy system powered by a handheld drill has recently been introduced for percutaneous bone biopsy. This technical note describes our initial experience performing fluoroscopy-guided vertebral body biopsies with this system, compares the yield of drill-assisted biopsy specimens with those obtained using a manual technique, and assesses the histologic adequacy of specimens obtained with drill assistance.MethodsMedical records of all single-level, fluoroscopy-guided vertebral body biopsies were reviewed. Procedural complications were documented according to the Society of Interventional Radiology classification. The total length of bone core obtained from drill-assisted biopsies was compared with that of matched manual biopsies. Pathology reports were reviewed to determine the histologic adequacy of specimens obtained with drill assistance.ResultsTwenty eight drill-assisted percutaneous vertebral body biopsies met study inclusion criteria. No acute complications were reported. Of the 86 % (24/28) of patients with clinical follow-up, no delayed complications were reported (median follow-up, 28 weeks; range 5–115 weeks). The median total length of bone core obtained from drill-assisted biopsies was 28 mm (range 8–120 mm). This was longer than that obtained from manual biopsies (median, 20 mm; range 5–45 mm; P = 0.03). Crush artifact was present in 11 % (3/28) of drill-assisted biopsy specimens, which in one case (3.6 %; 1/28) precluded definitive diagnosis.ConclusionsA drill-assisted, coaxial biopsy system can be used to safely obtain vertebral body core specimens under fluoroscopic guidance. The higher bone core yield obtained with drill assistance may be offset by the presence of crush artifact
Energy Technology Data Exchange (ETDEWEB)
Wallace, Adam N., E-mail: wallacea@mir.wustl.edu; Pacheco, Rafael A., E-mail: pachecor@mir.wustl.edu; Tomasian, Anderanik, E-mail: tomasiana@mir.wustl.edu [Washington University School of Medicine, Mallinckrodt Institute of Radiology (United States); Hsi, Andy C., E-mail: hsia@path.wustl.edu [Washington University School of Medicine, Division of Anatomic Pathology, Department of Pathology & Immunology (United States); Long, Jeremiah, E-mail: longj@mir.wustl.edu [Washington University School of Medicine, Mallinckrodt Institute of Radiology (United States); Chang, Randy O., E-mail: changr@wusm.wustl.edu [Washington University School of Medicine (United States); Jennings, Jack W., E-mail: jenningsj@mir.wustl.edu [Washington University School of Medicine, Mallinckrodt Institute of Radiology (United States)
2016-02-15
BackgroundA novel coaxial biopsy system powered by a handheld drill has recently been introduced for percutaneous bone biopsy. This technical note describes our initial experience performing fluoroscopy-guided vertebral body biopsies with this system, compares the yield of drill-assisted biopsy specimens with those obtained using a manual technique, and assesses the histologic adequacy of specimens obtained with drill assistance.MethodsMedical records of all single-level, fluoroscopy-guided vertebral body biopsies were reviewed. Procedural complications were documented according to the Society of Interventional Radiology classification. The total length of bone core obtained from drill-assisted biopsies was compared with that of matched manual biopsies. Pathology reports were reviewed to determine the histologic adequacy of specimens obtained with drill assistance.ResultsTwenty eight drill-assisted percutaneous vertebral body biopsies met study inclusion criteria. No acute complications were reported. Of the 86 % (24/28) of patients with clinical follow-up, no delayed complications were reported (median follow-up, 28 weeks; range 5–115 weeks). The median total length of bone core obtained from drill-assisted biopsies was 28 mm (range 8–120 mm). This was longer than that obtained from manual biopsies (median, 20 mm; range 5–45 mm; P = 0.03). Crush artifact was present in 11 % (3/28) of drill-assisted biopsy specimens, which in one case (3.6 %; 1/28) precluded definitive diagnosis.ConclusionsA drill-assisted, coaxial biopsy system can be used to safely obtain vertebral body core specimens under fluoroscopic guidance. The higher bone core yield obtained with drill assistance may be offset by the presence of crush artifact.
Modeling Financial Time Series Based on a Market Microstructure Model with Leverage Effect
Directory of Open Access Journals (Sweden)
Yanhui Xi
2016-01-01
Full Text Available The basic market microstructure model specifies that the price/return innovation and the volatility innovation are independent Gaussian white noise processes. However, the financial leverage effect has been found to be statistically significant in many financial time series. In this paper, a novel market microstructure model with leverage effects is proposed. The model specification assumed a negative correlation in the errors between the price/return innovation and the volatility innovation. With the new representations, a theoretical explanation of leverage effect is provided. Simulated data and daily stock market indices (Shanghai composite index, Shenzhen component index, and Standard and Poor’s 500 Composite index via Bayesian Markov Chain Monte Carlo (MCMC method are used to estimate the leverage market microstructure model. The results verify the effectiveness of the model and its estimation approach proposed in the paper and also indicate that the stock markets have strong leverage effects. Compared with the classical leverage stochastic volatility (SV model in terms of DIC (Deviance Information Criterion, the leverage market microstructure model fits the data better.
Spatially adaptive mixture modeling for analysis of FMRI time series.
Vincent, Thomas; Risser, Laurent; Ciuciu, Philippe
2010-04-01
Within-subject analysis in fMRI essentially addresses two problems, the detection of brain regions eliciting evoked activity and the estimation of the underlying dynamics. In Makni et aL, 2005 and Makni et aL, 2008, a detection-estimation framework has been proposed to tackle these problems jointly, since they are connected to one another. In the Bayesian formalism, detection is achieved by modeling activating and nonactivating voxels through independent mixture models (IMM) within each region while hemodynamic response estimation is performed at a regional scale in a nonparametric way. Instead of IMMs, in this paper we take advantage of spatial mixture models (SMM) for their nonlinear spatial regularizing properties. The proposed method is unsupervised and spatially adaptive in the sense that the amount of spatial correlation is automatically tuned from the data and this setting automatically varies across brain regions. In addition, the level of regularization is specific to each experimental condition since both the signal-to-noise ratio and the activation pattern may vary across stimulus types in a given brain region. These aspects require the precise estimation of multiple partition functions of underlying Ising fields. This is addressed efficiently using first path sampling for a small subset of fields and then using a recently developed fast extrapolation technique for the large remaining set. Simulation results emphasize that detection relying on supervised SMM outperforms its IMM counterpart and that unsupervised spatial mixture models achieve similar results without any hand-tuning of the correlation parameter. On real datasets, the gain is illustrated in a localizer fMRI experiment: brain activations appear more spatially resolved using SMM in comparison with classical general linear model (GLM)-based approaches, while estimating a specific parcel-based HRF shape. Our approach therefore validates the treatment of unsmoothed fMRI data without fixed GLM
Bayesian Modelling of fMRI Time Series
DEFF Research Database (Denmark)
Højen-Sørensen, Pedro; Hansen, Lars Kai; Rasmussen, Carl Edward
2000-01-01
We present a Hidden Markov Model (HMM) for inferring the hidden psychological state (or neural activity) during single trial fMRI activation experiments with blocked task paradigms. Inference is based on Bayesian methodology, using a combination of analytical and a variety of Markov Chain Monte...... Carlo (MCMC) sampling techniques. The advantage of this method is that detection of short time learning effects between repeated trials is possible since inference is based only on single trial experiments....
Cost model validation: a technical and cultural approach
Hihn, J.; Rosenberg, L.; Roust, K.; Warfield, K.
2001-01-01
This paper summarizes how JPL's parametric mission cost model (PMCM) has been validated using both formal statistical methods and a variety of peer and management reviews in order to establish organizational acceptance of the cost model estimates.
Energy flow models for the estimation of technical losses in distribution network
International Nuclear Information System (INIS)
Au, Mau Teng; Tan, Chin Hooi
2013-01-01
This paper presents energy flow models developed to estimate technical losses in distribution network. Energy flow models applied in this paper is based on input energy and peak demand of distribution network, feeder length and peak demand, transformer loading capacity, and load factor. Two case studies, an urban distribution network and a rural distribution network are used to illustrate application of the energy flow models. Results on technical losses obtained for the two distribution networks are consistent and comparable to network of similar types and characteristics. Hence, the energy flow models are suitable for practical application.
International Nuclear Information System (INIS)
1995-05-01
This technical memorandum provides information that can be used to make decisions concerning the disposition of four inactive tank systems that have been designated Batch 1, Series 1, by the Inactive Tanks Remediation Program team. The Batch I, Series 1, tanks are 3001-B, 3004-B, 3013, and T-30. The report offers viable alternatives for tank system disposition. The Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) requires a Federal Facility Agreement (FFA) for federal facilities placed on the National Priorities List. The Oak Ridge Reservation was placed on that list on December 21, 1989, and the agreement was signed in November 1991 by DOE's Oak Ridge Operations Office, the US Environmental Protection Agency-Region IV, and the Tennessee Department of Environment and Conservation. The effective date of the FFA is January 1, 1992. One objective of the FFA is to ensure that inactive liquid low-level radioactive waste tank systems are evaluated and, if appropriate, remediated through the CERCLA process. The Inactive Tanks Remediation Program and the Gunite and Associated Tanks Project (GAAT) are the two efforts that will meet this FFA objective. This memorandum addresses tank systems within the Inactive Tanks Remediation Program. Separate CERCLA documentation addresses the tank systems within the GAAT Project
A MODEL FOR INTEGRATED SOFTWARE TO IMPROVE COMMUNICATION POLICY IN DENTAL TECHNICAL LABS
Directory of Open Access Journals (Sweden)
Minko M. Milev
2017-06-01
Full Text Available Introduction: Integrated marketing communications (IMC are all kinds of communications between organisations and customers, partners, other organisations and society. Aim: To develop and present an integrated software model, which can improve the effectiveness of communications in dental technical services. Material and Methods: The model of integrated software is based on recommendations of a total of 700 respondents (students of dental technology, dental physicians, dental technicians and patients of dental technical laboratories in Northeastern Bulgaria. Results and Discussion: We present the benefits of future integrated software to improve the communication policy in the dental technical laboratory that meets the needs of fast cooperation and well-built communicative network between dental physicians, dental technicians, patients and students. Conclusion: The use of integrated communications could be a powerful unified approach to improving the communication policy between all players at the market of dental technical services.
MODELLING BIOPHYSICAL PARAMETERS OF MAIZE USING LANDSAT 8 TIME SERIES
Directory of Open Access Journals (Sweden)
T. Dahms
2016-06-01
Full Text Available Open and free access to multi-frequent high-resolution data (e.g. Sentinel – 2 will fortify agricultural applications based on satellite data. The temporal and spatial resolution of these remote sensing datasets directly affects the applicability of remote sensing methods, for instance a robust retrieving of biophysical parameters over the entire growing season with very high geometric resolution. In this study we use machine learning methods to predict biophysical parameters, namely the fraction of absorbed photosynthetic radiation (FPAR, the leaf area index (LAI and the chlorophyll content, from high resolution remote sensing. 30 Landsat 8 OLI scenes were available in our study region in Mecklenburg-Western Pomerania, Germany. In-situ data were weekly to bi-weekly collected on 18 maize plots throughout the summer season 2015. The study aims at an optimized prediction of biophysical parameters and the identification of the best explaining spectral bands and vegetation indices. For this purpose, we used the entire in-situ dataset from 24.03.2015 to 15.10.2015. Random forest and conditional inference forests were used because of their explicit strong exploratory and predictive character. Variable importance measures allowed for analysing the relation between the biophysical parameters with respect to the spectral response, and the performance of the two approaches over the plant stock evolvement. Classical random forest regression outreached the performance of conditional inference forests, in particular when modelling the biophysical parameters over the entire growing period. For example, modelling biophysical parameters of maize for the entire vegetation period using random forests yielded: FPAR: R² = 0.85; RMSE = 0.11; LAI: R² = 0.64; RMSE = 0.9 and chlorophyll content (SPAD: R² = 0.80; RMSE=4.9. Our results demonstrate the great potential in using machine-learning methods for the interpretation of long-term multi-frequent remote sensing
Modelling Biophysical Parameters of Maize Using Landsat 8 Time Series
Dahms, Thorsten; Seissiger, Sylvia; Conrad, Christopher; Borg, Erik
2016-06-01
Open and free access to multi-frequent high-resolution data (e.g. Sentinel - 2) will fortify agricultural applications based on satellite data. The temporal and spatial resolution of these remote sensing datasets directly affects the applicability of remote sensing methods, for instance a robust retrieving of biophysical parameters over the entire growing season with very high geometric resolution. In this study we use machine learning methods to predict biophysical parameters, namely the fraction of absorbed photosynthetic radiation (FPAR), the leaf area index (LAI) and the chlorophyll content, from high resolution remote sensing. 30 Landsat 8 OLI scenes were available in our study region in Mecklenburg-Western Pomerania, Germany. In-situ data were weekly to bi-weekly collected on 18 maize plots throughout the summer season 2015. The study aims at an optimized prediction of biophysical parameters and the identification of the best explaining spectral bands and vegetation indices. For this purpose, we used the entire in-situ dataset from 24.03.2015 to 15.10.2015. Random forest and conditional inference forests were used because of their explicit strong exploratory and predictive character. Variable importance measures allowed for analysing the relation between the biophysical parameters with respect to the spectral response, and the performance of the two approaches over the plant stock evolvement. Classical random forest regression outreached the performance of conditional inference forests, in particular when modelling the biophysical parameters over the entire growing period. For example, modelling biophysical parameters of maize for the entire vegetation period using random forests yielded: FPAR: R² = 0.85; RMSE = 0.11; LAI: R² = 0.64; RMSE = 0.9 and chlorophyll content (SPAD): R² = 0.80; RMSE=4.9. Our results demonstrate the great potential in using machine-learning methods for the interpretation of long-term multi-frequent remote sensing datasets to model
Sensitivity analysis of machine-learning models of hydrologic time series
O'Reilly, A. M.
2017-12-01
Sensitivity analysis traditionally has been applied to assessing model response to perturbations in model parameters, where the parameters are those model input variables adjusted during calibration. Unlike physics-based models where parameters represent real phenomena, the equivalent of parameters for machine-learning models are simply mathematical "knobs" that are automatically adjusted during training/testing/verification procedures. Thus the challenge of extracting knowledge of hydrologic system functionality from machine-learning models lies in their very nature, leading to the label "black box." Sensitivity analysis of the forcing-response behavior of machine-learning models, however, can provide understanding of how the physical phenomena represented by model inputs affect the physical phenomena represented by model outputs.As part of a previous study, hybrid spectral-decomposition artificial neural network (ANN) models were developed to simulate the observed behavior of hydrologic response contained in multidecadal datasets of lake water level, groundwater level, and spring flow. Model inputs used moving window averages (MWA) to represent various frequencies and frequency-band components of time series of rainfall and groundwater use. Using these forcing time series, the MWA-ANN models were trained to predict time series of lake water level, groundwater level, and spring flow at 51 sites in central Florida, USA. A time series of sensitivities for each MWA-ANN model was produced by perturbing forcing time-series and computing the change in response time-series per unit change in perturbation. Variations in forcing-response sensitivities are evident between types (lake, groundwater level, or spring), spatially (among sites of the same type), and temporally. Two generally common characteristics among sites are more uniform sensitivities to rainfall over time and notable increases in sensitivities to groundwater usage during significant drought periods.
An Illustration of Generalised Arma (garma) Time Series Modeling of Forest Area in Malaysia
Pillai, Thulasyammal Ramiah; Shitan, Mahendran
Forestry is the art and science of managing forests, tree plantations, and related natural resources. The main goal of forestry is to create and implement systems that allow forests to continue a sustainable provision of environmental supplies and services. Forest area is land under natural or planted stands of trees, whether productive or not. Forest area of Malaysia has been observed over the years and it can be modeled using time series models. A new class of GARMA models have been introduced in the time series literature to reveal some hidden features in time series data. For these models to be used widely in practice, we illustrate the fitting of GARMA (1, 1; 1, δ) model to the Annual Forest Area data of Malaysia which has been observed from 1987 to 2008. The estimation of the model was done using Hannan-Rissanen Algorithm, Whittle's Estimation and Maximum Likelihood Estimation.
Nonlinearity, Breaks, and Long-Range Dependence in Time-Series Models
DEFF Research Database (Denmark)
Hillebrand, Eric Tobias; Medeiros, Marcelo C.
We study the simultaneous occurrence of long memory and nonlinear effects, such as parameter changes and threshold effects, in ARMA time series models and apply our modeling framework to daily realized volatility. Asymptotic theory for parameter estimation is developed and two model building...
Assessing and improving the quality of modeling : a series of empirical studies about the UML
Lange, C.F.J.
2007-01-01
Assessing and Improving the Quality of Modeling A Series of Empirical Studies about the UML This thesis addresses the assessment and improvement of the quality of modeling in software engineering. In particular, we focus on the Unified Modeling Language (UML), which is the de facto standard in
Technical Work Plan for: Thermodynamic Databases for Chemical Modeling
International Nuclear Information System (INIS)
C.F. Jovecolon
2006-01-01
The objective of the work scope covered by this Technical Work Plan (TWP) is to correct and improve the Yucca Mountain Project (YMP) thermodynamic databases, to update their documentation, and to ensure reasonable consistency among them. In addition, the work scope will continue to generate database revisions, which are organized and named so as to be transparent to internal and external users and reviewers. Regarding consistency among databases, it is noted that aqueous speciation and mineral solubility data for a given system may differ according to how solubility was determined, and the method used for subsequent retrieval of thermodynamic parameter values from measured data. Of particular concern are the details of the determination of ''infinite dilution'' constants, which involve the use of specific methods for activity coefficient corrections. That is, equilibrium constants developed for a given system for one set of conditions may not be consistent with constants developed for other conditions, depending on the species considered in the chemical reactions and the methods used in the reported studies. Hence, there will be some differences (for example in log K values) between the Pitzer and ''B-dot'' database parameters for the same reactions or species
A Four-Stage Hybrid Model for Hydrological Time Series Forecasting
Di, Chongli; Yang, Xiaohua; Wang, Xiaochao
2014-01-01
Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of ‘denoising, decomposition and ensemble’. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models. PMID:25111782
A four-stage hybrid model for hydrological time series forecasting.
Di, Chongli; Yang, Xiaohua; Wang, Xiaochao
2014-01-01
Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of 'denoising, decomposition and ensemble'. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models.
Mesorad dose assessment model. Volume 1. Technical basis
International Nuclear Information System (INIS)
Scherpelz, R.I.; Bander, T.J.; Athey, G.F.; Ramsdell, J.V.
1986-03-01
MESORAD is a dose assessment model for emergency response applications. Using release data for as many as 50 radionuclides, the model calculates: (1) external doses resulting from exposure to radiation emitted by radionuclides contained in elevated or deposited material; (2) internal dose commitment resulting from inhalation; and (3) total whole-body doses. External doses from airborne material are calculated using semi-infinite and finite cloud approximations. At each stage in model execution, the appropriate approximation is selected after considering the cloud dimensions. Atmospheric processes are represented in MESORAD by a combination of Lagrangian puff and Gaussian plume dispersion models, a source depletion (deposition velocity) dry deposition model, and a wet deposition model using washout coefficients based on precipitation rates
Moeeni, Hamid; Bonakdari, Hossein; Fatemi, Seyed Ehsan
2017-04-01
Because time series stationarization has a key role in stochastic modeling results, three methods are analyzed in this study. The methods are seasonal differencing, seasonal standardization and spectral analysis to eliminate the periodic effect on time series stationarity. First, six time series including 4 streamflow series and 2 water temperature series are stationarized. The stochastic term for these series obtained with ARIMA is subsequently modeled. For the analysis, 9228 models are introduced. It is observed that seasonal standardization and spectral analysis eliminate the periodic term completely, while seasonal differencing maintains seasonal correlation structures. The obtained results indicate that all three methods present acceptable performance overall. However, model accuracy in monthly streamflow prediction is higher with seasonal differencing than with the other two methods. Another advantage of seasonal differencing over the other methods is that the monthly streamflow is never estimated as negative. Standardization is the best method for predicting monthly water temperature although it is quite similar to seasonal differencing, while spectral analysis performed the weakest in all cases. It is concluded that for each monthly seasonal series, seasonal differencing is the best stationarization method in terms of periodic effect elimination. Moreover, the monthly water temperature is predicted with more accuracy than monthly streamflow. The criteria of the average stochastic term divided by the amplitude of the periodic term obtained for monthly streamflow and monthly water temperature were 0.19 and 0.30, 0.21 and 0.13, and 0.07 and 0.04 respectively. As a result, the periodic term is more dominant than the stochastic term for water temperature in the monthly water temperature series compared to streamflow series.
Chitale, Rohan; Ghobrial, George M; Lobel, Darlene; Harrop, James
2013-10-01
The learning and development of technical skills are paramount for neurosurgical trainees. External influences and a need for maximizing efficiency and proficiency have encouraged advancements in simulator-based learning models. To confirm the importance of establishing an educational curriculum for teaching minimally invasive techniques of pedicle screw placement using a computer-enhanced physical model of percutaneous pedicle screw placement with simultaneous didactic and technical components. A 2-hour educational curriculum was created to educate neurosurgical residents on anatomy, pathophysiology, and technical aspects associated with image-guided pedicle screw placement. Predidactic and postdidactic practical and written scores were analyzed and compared. Scores were calculated for each participant on the basis of the optimal pedicle screw starting point and trajectory for both fluoroscopy and computed tomographic navigation. Eight trainees participated in this module. Average mean scores on the written didactic test improved from 78% to 100%. The technical component scores for fluoroscopic guidance improved from 58.8 to 52.9. Technical score for computed tomography-navigated guidance also improved from 28.3 to 26.6. Didactic and technical quantitative scores with a simulator-based educational curriculum improved objectively measured resident performance. A minimally invasive spine simulation model and curriculum may serve a valuable function in the education of neurosurgical residents and outcomes for patients.
Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox
DEFF Research Database (Denmark)
Nonejad, Nima
This paper details Particle Markov chain Monte Carlo techniques for analysis of unobserved component time series models using several economic data sets. PMCMC combines the particle filter with the Metropolis-Hastings algorithm. Overall PMCMC provides a very compelling, computationally fast...... and efficient framework for estimation. These advantages are used to for instance estimate stochastic volatility models with leverage effect or with Student-t distributed errors. We also model changing time series characteristics of the US inflation rate by considering a heteroskedastic ARFIMA model where...
Hybrid model for forecasting time series with trend, seasonal and salendar variation patterns
Suhartono; Rahayu, S. P.; Prastyo, D. D.; Wijayanti, D. G. P.; Juliyanto
2017-09-01
Most of the monthly time series data in economics and business in Indonesia and other Moslem countries not only contain trend and seasonal, but also affected by two types of calendar variation effects, i.e. the effect of the number of working days or trading and holiday effects. The purpose of this research is to develop a hybrid model or a combination of several forecasting models to predict time series that contain trend, seasonal and calendar variation patterns. This hybrid model is a combination of classical models (namely time series regression and ARIMA model) and/or modern methods (artificial intelligence method, i.e. Artificial Neural Networks). A simulation study was used to show that the proposed procedure for building the hybrid model could work well for forecasting time series with trend, seasonal and calendar variation patterns. Furthermore, the proposed hybrid model is applied for forecasting real data, i.e. monthly data about inflow and outflow of currency at Bank Indonesia. The results show that the hybrid model tend to provide more accurate forecasts than individual forecasting models. Moreover, this result is also in line with the third results of the M3 competition, i.e. the hybrid model on average provides a more accurate forecast than the individual model.
Road safety forecasts in five European countries using structural time series models.
Antoniou, Constantinos; Papadimitriou, Eleonora; Yannis, George
2014-01-01
Modeling road safety development is a complex task and needs to consider both the quantifiable impact of specific parameters as well as the underlying trends that cannot always be measured or observed. The objective of this research is to apply structural time series models for obtaining reliable medium- to long-term forecasts of road traffic fatality risk using data from 5 countries with different characteristics from all over Europe (Cyprus, Greece, Hungary, Norway, and Switzerland). Two structural time series models are considered: (1) the local linear trend model and the (2) latent risk time series model. Furthermore, a structured decision tree for the selection of the applicable model for each situation (developed within the Road Safety Data, Collection, Transfer and Analysis [DaCoTA] research project, cofunded by the European Commission) is outlined. First, the fatality and exposure data that are used for the development of the models are presented and explored. Then, the modeling process is presented, including the model selection process, introduction of intervention variables, and development of mobility scenarios. The forecasts using the developed models appear to be realistic and within acceptable confidence intervals. The proposed methodology is proved to be very efficient for handling different cases of data availability and quality, providing an appropriate alternative from the family of structural time series models in each country. A concluding section providing perspectives and directions for future research is presented.
Technical note: Equivalent genomic models with a residual polygenic effect.
Liu, Z; Goddard, M E; Hayes, B J; Reinhardt, F; Reents, R
2016-03-01
Routine genomic evaluations in animal breeding are usually based on either a BLUP with genomic relationship matrix (GBLUP) or single nucleotide polymorphism (SNP) BLUP model. For a multi-step genomic evaluation, these 2 alternative genomic models were proven to give equivalent predictions for genomic reference animals. The model equivalence was verified also for young genotyped animals without phenotypes. Due to incomplete linkage disequilibrium of SNP markers to genes or causal mutations responsible for genetic inheritance of quantitative traits, SNP markers cannot explain all the genetic variance. A residual polygenic effect is normally fitted in the genomic model to account for the incomplete linkage disequilibrium. In this study, we start by showing the proof that the multi-step GBLUP and SNP BLUP models are equivalent for the reference animals, when they have a residual polygenic effect included. Second, the equivalence of both multi-step genomic models with a residual polygenic effect was also verified for young genotyped animals without phenotypes. Additionally, we derived formulas to convert genomic estimated breeding values of the GBLUP model to its components, direct genomic values and residual polygenic effect. Third, we made a proof that the equivalence of these 2 genomic models with a residual polygenic effect holds also for single-step genomic evaluation. Both the single-step GBLUP and SNP BLUP models lead to equal prediction for genotyped animals with phenotypes (e.g., reference animals), as well as for (young) genotyped animals without phenotypes. Finally, these 2 single-step genomic models with a residual polygenic effect were proven to be equivalent for estimation of SNP effects, too. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.
Developing and Validating the Socio-Technical Model in Ontology Engineering
Silalahi, Mesnan; Indra Sensuse, Dana; Giri Sucahyo, Yudho; Fadhilah Akmaliah, Izzah; Rahayu, Puji; Cahyaningsih, Elin
2018-03-01
This paper describes results from an attempt to develop a model in ontology engineering methodology and a way to validate the model. The approach to methodology in ontology engineering is from the point view of socio-technical system theory. Qualitative research synthesis is used to build the model using meta-ethnography. In order to ensure the objectivity of the measurement, inter-rater reliability method was applied using a multi-rater Fleiss Kappa. The results show the accordance of the research output with the diamond model in the socio-technical system theory by evidence of the interdependency of the four socio-technical variables namely people, technology, structure and task.
Technical know-how of site descriptive modeling for site characterization - 59089
International Nuclear Information System (INIS)
Saegusa, Hiromitsu; Onoe, Hironori; Doke, Ryosuke; Niizato, Tadafumi; Yasue, Ken-ichi
2012-01-01
The site descriptive model covering the current status of characteristics of geological environment and the site evolution model for estimation of the long-term evolution of site conditions are used to integrate multi-disciplinary investigation results. It is important to evaluate uncertainties in the models, to specify issues regarding the uncertainties and to prioritize the resolution of specified issues, for the planning of site characterization. There is a large quantity of technical know-how in the modeling process. It is important to record the technical know-how with transparency and traceability, since site characterization projects generally need long duration. The transfer of the technical know-how accumulated in the research and development (R and D) phase to the implementation phase is equally important. The aim of this study is to support the planning of initial surface-based site characterizations based on the technical know-how accumulated from the underground research laboratory projects. These projects are broad scientific studies of the deep geological environment and provide a technical basis for the geological disposal of high-level radioactive wastes. In this study, a comprehensive task flow from acquisition of existing data to planning of field investigations through the modeling has been specified. Specific task flow and decision-making process to perform the tasks have been specified. (authors)
Predictive Software Cost Model Study. Volume I. Final Technical Report.
1980-06-01
development phase to identify computer resources necessary to support computer programs after transfer of program manangement responsibility and system... classical model development with refinements specifically applicable to avionics systems. The refinements are the result of the Phase I literature search
Technical Note: How to use Winbugs to infer animal models
DEFF Research Database (Denmark)
Damgaard, Lars Holm
2007-01-01
This paper deals with Bayesian inferences of animal models using Gibbs sampling. First, we suggest a general and efficient method for updating additive genetic effects, in which the computational cost is independent of the pedigree depth and increases linearly only with the size of the pedigree....... Second, we show how this approach can be used to draw inferences from a wide range of animal models using the computer package Winbugs. Finally, we illustrate the approach in a simulation study, in which the data are generated and analyzed using Winbugs according to a linear model with i.i.d errors...... having Student's t distributions. In conclusion, Winbugs can be used to make inferences in small-sized, quantitative, genetic data sets applying a wide range of animal models that are not yet standard in the animal breeding literature...
Dispersive processes in models of regional radionuclide migration. Technical memorandum
International Nuclear Information System (INIS)
Evenson, D.E.; Dettinger, M.D.
1980-05-01
Three broad areas of concern in the development of aquifer scale transport models will be local scale diffusion and dispersion processes, regional scale dispersion processes, and numerical problems associated with the advection-dispersion equation. Local scale dispersion processes are fairly well understood and accessible to observation. These processes will generally be dominated in large scale systems by regional processes, or macro-dispersion. Macro-dispersion is primarily the result of large scale heterogeneities in aquifer properties. In addition, the effects of many modeling approximations are often included in the process. Because difficulties arise in parameterization of this large scale phenomenon, parameterization should be based on field measurements made at the same scale as the transport process of interest or else partially circumvented through the application of a probabilistic advection model. Other problems associated with numerical transport models include difficulties with conservation of mass, stability, numerical dissipation, overshoot, flexibility, and efficiency. We recommend the random-walk model formulation for Lawrence Livermore Laboratory's purposes as the most flexible, accurate and relatively efficient modeling approach that overcomes these difficulties
Technical note: A linear model for predicting δ13 Cprotein.
Pestle, William J; Hubbe, Mark; Smith, Erin K; Stevenson, Joseph M
2015-08-01
Development of a model for the prediction of δ(13) Cprotein from δ(13) Ccollagen and Δ(13) Cap-co . Model-generated values could, in turn, serve as "consumer" inputs for multisource mixture modeling of paleodiet. Linear regression analysis of previously published controlled diet data facilitated the development of a mathematical model for predicting δ(13) Cprotein (and an experimentally generated error term) from isotopic data routinely generated during the analysis of osseous remains (δ(13) Cco and Δ(13) Cap-co ). Regression analysis resulted in a two-term linear model (δ(13) Cprotein (%) = (0.78 × δ(13) Cco ) - (0.58× Δ(13) Cap-co ) - 4.7), possessing a high R-value of 0.93 (r(2) = 0.86, P analysis of human osseous remains. These predicted values are ideal for use in multisource mixture modeling of dietary protein source contribution. © 2015 Wiley Periodicals, Inc.
Travel Cost Inference from Sparse, Spatio-Temporally Correlated Time Series Using Markov Models
DEFF Research Database (Denmark)
Yang, Bin; Guo, Chenjuan; Jensen, Christian S.
2013-01-01
of such time series offers insight into the underlying system and enables prediction of system behavior. While the techniques presented in the paper apply more generally, we consider the case of transportation systems and aim to predict travel cost from GPS tracking data from probe vehicles. Specifically, each...... road segment has an associated travel-cost time series, which is derived from GPS data. We use spatio-temporal hidden Markov models (STHMM) to model correlations among different traffic time series. We provide algorithms that are able to learn the parameters of an STHMM while contending...... with the sparsity, spatio-temporal correlation, and heterogeneity of the time series. Using the resulting STHMM, near future travel costs in the transportation network, e.g., travel time or greenhouse gas emissions, can be inferred, enabling a variety of routing services, e.g., eco-routing. Empirical studies...
Adaptive time-variant models for fuzzy-time-series forecasting.
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.
Career and Technical Education (CTE) Student Success in Community Colleges: A Conceptual Model
Hirschy, Amy S.; Bremer, Christine D.; Castellano, Marisa
2011-01-01
Career and technical education (CTE) students pursuing occupational associate's degrees or certificates differ from students seeking academic majors at 2-year institutions in several ways. This article examines several theoretical models of student persistence and offers a conceptual model of student success focused on CTE students in community…
A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method
Jun-He Yang; Ching-Hsue Cheng; Chia-Pan Chan
2017-01-01
Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir's water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting m...
Applying ARIMA model for annual volume time series of the Magdalena River
Gloria Amaris; Humberto Ávila; Thomas Guerrero
2017-01-01
Context: Climate change effects, human interventions, and river characteristics are factors that increase the risk on the population and the water resources. However, negative impacts such as flooding, and river droughts may be previously identified using appropriate numerical tools. Objectives: The annual volume (Millions of m3/year) time series of the Magdalena River was analyzed by an ARIMA model, using the historical time series of the Calamar station (Instituto de Hidrología, Meteoro...
Characteristics of the LeRC/Hughes J-series 30-cm engineering model thruster
Collett, C. R.; Poeschel, R. L.; Kami, S.
1981-01-01
As a consequence of endurance and structural tests performed on 900-series engineering model thrusters (EMT), several modifications in design were found to be necessary for achieving performance goals. The modified thruster is known as the J-series EMT. The most important of the design modifications affect the accelerator grid, gimbal mount, cathode polepiece, and wiring harness. The paper discusses the design modifications incorporated, the condition(s) they corrected, and the characteristics of the modified thruster.
Ghobrial, George M; Anderson, Paul A; Chitale, Rohan; Campbell, Peter G; Lobel, Darlene A; Harrop, James
2013-10-01
In the era of surgical resident work hour restrictions, the traditional apprenticeship model may provide fewer hours for neurosurgical residents to hone technical skills. Spinal dura mater closure or repair is 1 skill that is infrequently encountered, and persistent cerebrospinal fluid leaks are a potential morbidity. To establish an educational curriculum to train residents in spinal dura mater closure with a novel durotomy repair model. The Congress of Neurological Surgeons has developed a simulation-based model for durotomy closure with the ongoing efforts of their simulation educational committee. The core curriculum consists of didactic training materials and a technical simulation model of dural repair for the lumbar spine. Didactic pretest scores ranged from 4/11 (36%) to 10/11 (91%). Posttest scores ranged from 8/11 (73%) to 11/11 (100%). Overall, didactic improvements were demonstrated by all participants, with a mean improvement between pre- and posttest scores of 1.17 (18.5%; P = .02). The technical component consisted of 11 durotomy closures by 6 participants, where 4 participants performed multiple durotomies. Mean time to closure of the durotomy ranged from 490 to 546 seconds in the first and second closures, respectively (P = .66), whereby the median leak rate improved from 14 to 7 (P = .34). There were also demonstrative technical improvements by all. Simulated spinal dura mater repair appears to be a potentially valuable tool in the education of neurosurgery residents. The combination of a didactic and technical assessment appears to be synergistic in terms of educational development.
Development of Simulink-Based SiC MOSFET Modeling Platform for Series Connected Devices
DEFF Research Database (Denmark)
Tsolaridis, Georgios; Ilves, Kalle; Reigosa, Paula Diaz
2016-01-01
A new MATLAB/Simulink-based modeling platform has been developed for SiC MOSFET power modules. The modeling platform describes the electrical behavior f a single 1.2 kV/ 350 A SiC MOSFET power module, as well as the series connection of two of them. A fast parameter initialization is followed...... by an optimization process to facilitate the extraction of the model’s parameters in a more automated way relying on a small number of experimental waveforms. Through extensive experimental work, it is shown that the model accurately predicts both static and dynamic performances. The series connection of two Si......C power modules has been investigated through the validation of the static and dynamic conditions. Thanks to the developed model, a better understanding of the challenges introduced by uneven voltage balance sharing among series connected devices is possible....
The application of time series models to cloud field morphology analysis
Chin, Roland T.; Jau, Jack Y. C.; Weinman, James A.
1987-01-01
A modeling method for the quantitative description of remotely sensed cloud field images is presented. A two-dimensional texture modeling scheme based on one-dimensional time series procedures is adopted for this purpose. The time series procedure used is the seasonal autoregressive, moving average (ARMA) process in Box and Jenkins. Cloud field properties such as directionality, clustering and cloud coverage can be retrieved by this method. It has been demonstrated that a cloud field image can be quantitatively defined by a small set of parameters and synthesized surrogates can be reconstructed from these model parameters. This method enables cloud climatology to be studied quantitatively.
International Nuclear Information System (INIS)
Dong, Xiangyuan; Guo, Shuqing
2008-01-01
In this paper, a novel image reconstruction method for electrical capacitance tomography (ECT) based on the combined series and parallel model is presented. A regularization technique is used to obtain a stabilized solution of the inverse problem. Also, the adaptive coefficient of the combined model is deduced by numerical optimization. Simulation results indicate that it can produce higher quality images when compared to the algorithm based on the parallel or series models for the cases tested in this paper. It provides a new algorithm for ECT application
Hierarchical Hidden Markov Models for Multivariate Integer-Valued Time-Series
DEFF Research Database (Denmark)
Catania, Leopoldo; Di Mari, Roberto
2018-01-01
We propose a new flexible dynamic model for multivariate nonnegative integer-valued time-series. Observations are assumed to depend on the realization of two additional unobserved integer-valued stochastic variables which control for the time-and cross-dependence of the data. An Expectation......-Maximization algorithm for maximum likelihood estimation of the model's parameters is derived. We provide conditional and unconditional (cross)-moments implied by the model, as well as the limiting distribution of the series. A Monte Carlo experiment investigates the finite sample properties of our estimation...
Biotrans functional and technical description. Report of VIEWLS WP5, modelling studies
International Nuclear Information System (INIS)
Van Tilburg, X.; Egging, R.; Londo, H.M.
2006-01-01
The overall objectives of this project are to provide structured and clear data on the availability and performance of biofuels and to identify the possibilities and strategies towards large scale sustainable production, use and trading of biofuels for the transport sector in Europe, including Central and Eastern European Countries (CEEC). The report supplements the two other reports in the work package: 'Biofuel and Bio-energy implementation scenarios - final report of VIEWLS WP5' (2005) and 'VIEWLS modelling and analysis, technical data for biofuel production chains' (2005). This document contains a functional and technical description of the BioTrans model, accompanied by a description of the system. Section 2 contains a conceptual and functional description of the biofuel model. Section 3 describes the optimisation method in technical terms, discussing aspects like the target function and constraints used. Finally, section 4 discusses the input and output requirements for the BioTrans system
Blaney-Morin-Nigeria (BMN) Evapotranspiration Model (A Technical ...
African Journals Online (AJOL)
Duru [1] presented a modified form of the Blaney-Morin potential evapotranspiration equation christened Blaney-Morin- Nigeria (BMN) Evapotranspiration (ET) model for use in Nigeria. In this work, Duru recognize the very wide variability of relative humidity in Nigeria and consequently the very important role this parameter ...
Technical note: River modelling to infer flood management framework
African Journals Online (AJOL)
River hydraulic models have successfully identified the weaknesses and areas for improvement with respect to flooding in the Sarawak River system, and can also be used to support decisions on flood management measures. Often, the big question is 'how'. This paper demonstrates a theoretical flood management ...
Model Adoption Exchange Payment System: Technical Specifications and User Instructions.
Ambrosino, Robert J.
This user's manual, designed to meet the needs of adoption exchange administrators and program managers for a formal tool to assist them in the overall management and operation of their program, presents the Model Adoption Exchange Payment System (MAEPS), which was developed to improve the delivery of adoption exchange services throughout the…
Efforts - Final technical report on task 4. Physical modelling calidation
DEFF Research Database (Denmark)
Andreasen, Jan Lasson; Olsson, David Dam; Christensen, T. W.
The present report is documentation for the work carried out in Task 4 at DTU Physical modelling-validation on the Brite/Euram project No. BE96-3340, contract No. BRPR-CT97-0398, with the title Enhanced Framework for forging design using reliable three-dimensional simulation (EFFORTS). The report...
Technical Note: Calibration and validation of geophysical observation models
Salama, M.S.; van der Velde, R.; van der Woerd, H.J.; Kromkamp, J.C.; Philippart, C.J.M.; Joseph, A.T.; O'Neill, P.E.; Lang, R.H.; Gish, T.; Werdell, P.J.; Su, Z.
2012-01-01
We present a method to calibrate and validate observational models that interrelate remotely sensed energy fluxes to geophysical variables of land and water surfaces. Coincident sets of remote sensing observation of visible and microwave radiations and geophysical data are assembled and subdivided
A new model for reliability optimization of series-parallel systems with non-homogeneous components
International Nuclear Information System (INIS)
Feizabadi, Mohammad; Jahromi, Abdolhamid Eshraghniaye
2017-01-01
In discussions related to reliability optimization using redundancy allocation, one of the structures that has attracted the attention of many researchers, is series-parallel structure. In models previously presented for reliability optimization of series-parallel systems, there is a restricting assumption based on which all components of a subsystem must be homogeneous. This constraint limits system designers in selecting components and prevents achieving higher levels of reliability. In this paper, a new model is proposed for reliability optimization of series-parallel systems, which makes possible the use of non-homogeneous components in each subsystem. As a result of this flexibility, the process of supplying system components will be easier. To solve the proposed model, since the redundancy allocation problem (RAP) belongs to the NP-hard class of optimization problems, a genetic algorithm (GA) is developed. The computational results of the designed GA are indicative of high performance of the proposed model in increasing system reliability and decreasing costs. - Highlights: • In this paper, a new model is proposed for reliability optimization of series-parallel systems. • In the previous models, there is a restricting assumption based on which all components of a subsystem must be homogeneous. • The presented model provides a possibility for the subsystems’ components to be non- homogeneous in the required conditions. • The computational results demonstrate the high performance of the proposed model in improving reliability and reducing costs.
Effect of calibration data series length on performance and optimal parameters of hydrological model
Directory of Open Access Journals (Sweden)
Chuan-zhe Li
2010-12-01
Full Text Available In order to assess the effects of calibration data series length on the performance and optimal parameter values of a hydrological model in ungauged or data-limited catchments (data are non-continuous and fragmental in some catchments, we used non-continuous calibration periods for more independent streamflow data for SIMHYD (simple hydrology model calibration. Nash-Sutcliffe efficiency and percentage water balance error were used as performance measures. The particle swarm optimization (PSO method was used to calibrate the rainfall-runoff models. Different lengths of data series ranging from one year to ten years, randomly sampled, were used to study the impact of calibration data series length. Fifty-five relatively unimpaired catchments located all over Australia with daily precipitation, potential evapotranspiration, and streamflow data were tested to obtain more general conclusions. The results show that longer calibration data series do not necessarily result in better model performance. In general, eight years of data are sufficient to obtain steady estimates of model performance and parameters for the SIMHYD model. It is also shown that most humid catchments require fewer calibration data to obtain a good performance and stable parameter values. The model performs better in humid and semi-humid catchments than in arid catchments. Our results may have useful and interesting implications for the efficiency of using limited observation data for hydrological model calibration in different climates.
Bayesian near-boundary analysis in basic macroeconomic time series models
M.D. de Pooter (Michiel); F. Ravazzolo (Francesco); R. Segers (René); H.K. van Dijk (Herman)
2008-01-01
textabstractSeveral lessons learnt from a Bayesian analysis of basic macroeconomic time series models are presented for the situation where some model parameters have substantial posterior probability near the boundary of the parameter region. This feature refers to near-instability within dynamic
Applying ARIMA model for annual volume time series of the Magdalena River
Directory of Open Access Journals (Sweden)
Gloria Amaris
2017-04-01
Conclusions: The simulated results obtained with the ARIMA model compared to the observed data showed a fairly good adjustment of the minimum and maximum magnitudes. This allows concluding that it is a good tool for estimating minimum and maximum volumes, even though this model is not capable of simulating the exact behaviour of an annual volume time series.
Berendrecht, W.L.; Heemink, A.W.; Geer, F.C. van; Gehrels, J.C.
2003-01-01
A state-space representation of the transfer function-noise (TFN) model allows the choice of a modeling (input) interval that is smaller than the measuring interval of the output variable. Since in geohydrological applications the interval of the available input series (precipitation excess) is
2013-10-31
... an association, business, labor union, etc.). DOT's complete Privacy Act Statement can be found in... supplemental type certificate (STC) change in the digital systems architecture in the Learjet Model 45 series... plus two crew members. The proposed Learjet Model 45 architecture is new and novel for commercial...
Technical documentation of HGSYSTEM/UF6 model
International Nuclear Information System (INIS)
Hanna, S.R.; Chang, J.C.; Zhang, J.X.
1996-01-01
MMES has been directed to upgrade the safety analyses for the gaseous diffusion plants at Paducah KY and Piketon OH. These will require assessment of consequences of accidental releases of UF 6 to the atmosphere at these plants. The HGSYSTEM model has been chosen as the basis for evaluating UF 6 releases; it includes dispersion algorithms for dense gases and treats the chemistry and thermodynamics of HF, a major product of the reaction of UF 6 with water vapor in air. Objective of this project was to incorporate additional capability into HGSYSTEM: UF 6 chemistry and thermodynamics, plume lift-off algorithms, and wet and dry deposition. The HGSYSTEM modules are discussed. The hybrid HGSYSTEM/UF 6 model has been evaluated in three ways
Technical Work Plan for: Additional Multiscale Thermohydrologic Modeling
International Nuclear Information System (INIS)
B. Kirstein
2006-01-01
The primary objective of Revision 04 of the MSTHM report is to provide TSPA with revised repository-wide MSTHM analyses that incorporate updated percolation flux distributions, revised hydrologic properties, updated IEDs, and information pertaining to the emplacement of transport, aging, and disposal (TAD) canisters. The updated design information is primarily related to the incorporation of TAD canisters, but also includes updates related to superseded IEDs describing emplacement drift cross-sectional geometry and layout. The intended use of the results of Revision 04 of the MSTHM report, as described in this TWP, is to predict the evolution of TH conditions (temperature, relative humidity, liquid-phase saturation, and liquid-phase flux) at specified locations within emplacement drifts and in the adjoining near-field host rock along all emplacement drifts throughout the repository. This information directly supports the TSPA for the nominal and seismic scenarios. The revised repository-wide analyses are required to incorporate updated parameters and design information and to extend those analyses out to 1,000,000 years. Note that the previous MSTHM analyses reported in Revision 03 of Multiscale Thermohydrologic Model (BSC 2005 [DIRS 173944]) only extend out to 20,000 years. The updated parameters are the percolation flux distributions, including incorporation of post-10,000-year distributions, and updated calibrated hydrologic property values for the host-rock units. The applied calibrated hydrologic properties will be an updated version of those available in Calibrated Properties Model (BSC 2004 [DIRS 169857]). These updated properties will be documented in an Appendix of Revision 03 of UZ Flow Models and Submodels (BSC 2004 [DIRS 169861]). The updated calibrated properties are applied because they represent the latest available information. The reasonableness of applying the updated calibrated' properties to the prediction of near-fieldin-drift TH conditions
The electricity portfolio simulation model (EPSim) technical description.
Energy Technology Data Exchange (ETDEWEB)
Drennen, Thomas E.; Klotz, Richard (Hobart and William Smith Colleges, Geneva, NY)
2005-09-01
Stakeholders often have competing interests when selecting or planning new power plants. The purpose of developing this preliminary Electricity Portfolio Simulation Model (EPSim) is to provide a first cut, dynamic methodology and approach to this problem, that can subsequently be refined and validated, that may help energy planners, policy makers, and energy students better understand the tradeoffs associated with competing electricity portfolios. EPSim allows the user to explore competing electricity portfolios annually from 2002 to 2025 in terms of five different criteria: cost, environmental impacts, energy dependence, health and safety, and sustainability. Four additional criteria (infrastructure vulnerability, service limitations, policy needs and science and technology needs) may be added in future versions of the model. Using an analytic hierarchy process (AHP) approach, users or groups of users apply weights to each of the criteria. The default energy assumptions of the model mimic Department of Energy's (DOE) electricity portfolio to 2025 (EIA, 2005). At any time, the user can compare alternative portfolios to this reference case portfolio.
Electronic resource management practical perspectives in a new technical services model
Elguindi, Anne
2012-01-01
A significant shift is taking place in libraries, with the purchase of e-resources accounting for the bulk of materials spending. Electronic Resource Management makes the case that technical services workflows need to make a corresponding shift toward e-centric models and highlights the increasing variety of e-formats that are forcing new developments in the field.Six chapters cover key topics, including: technical services models, both past and emerging; staffing and workflow in electronic resource management; implementation and transformation of electronic resource management systems; the ro
Kusev, Petko; van Schaik, Paul; Tsaneva-Atanasova, Krasimira; Juliusson, Asgeir; Chater, Nick
2018-01-01
When attempting to predict future events, people commonly rely on historical data. One psychological characteristic of judgmental forecasting of time series, established by research, is that when people make forecasts from series, they tend to underestimate future values for upward trends and overestimate them for downward ones, so-called trend-damping (modeled by anchoring on, and insufficient adjustment from, the average of recent time series values). Events in a time series can be experienced sequentially (dynamic mode), or they can also be retrospectively viewed simultaneously (static mode), not experienced individually in real time. In one experiment, we studied the influence of presentation mode (dynamic and static) on two sorts of judgment: (a) predictions of the next event (forecast) and (b) estimation of the average value of all the events in the presented series (average estimation). Participants' responses in dynamic mode were anchored on more recent events than in static mode for all types of judgment but with different consequences; hence, dynamic presentation improved prediction accuracy, but not estimation. These results are not anticipated by existing theoretical accounts; we develop and present an agent-based model-the adaptive anchoring model (ADAM)-to account for the difference between processing sequences of dynamically and statically presented stimuli (visually presented data). ADAM captures how variation in presentation mode produces variation in responses (and the accuracy of these responses) in both forecasting and judgment tasks. ADAM's model predictions for the forecasting and judgment tasks fit better with the response data than a linear-regression time series model. Moreover, ADAM outperformed autoregressive-integrated-moving-average (ARIMA) and exponential-smoothing models, while neither of these models accounts for people's responses on the average estimation task. Copyright © 2017 The Authors. Cognitive Science published by Wiley
New insights into soil temperature time series modeling: linear or nonlinear?
Bonakdari, Hossein; Moeeni, Hamid; Ebtehaj, Isa; Zeynoddin, Mohammad; Mahoammadian, Abdolmajid; Gharabaghi, Bahram
2018-03-01
Soil temperature (ST) is an important dynamic parameter, whose prediction is a major research topic in various fields including agriculture because ST has a critical role in hydrological processes at the soil surface. In this study, a new linear methodology is proposed based on stochastic methods for modeling daily soil temperature (DST). With this approach, the ST series components are determined to carry out modeling and spectral analysis. The results of this process are compared with two linear methods based on seasonal standardization and seasonal differencing in terms of four DST series. The series used in this study were measured at two stations, Champaign and Springfield, at depths of 10 and 20 cm. The results indicate that in all ST series reviewed, the periodic term is the most robust among all components. According to a comparison of the three methods applied to analyze the various series components, it appears that spectral analysis combined with stochastic methods outperformed the seasonal standardization and seasonal differencing methods. In addition to comparing the proposed methodology with linear methods, the ST modeling results were compared with the two nonlinear methods in two forms: considering hydrological variables (HV) as input variables and DST modeling as a time series. In a previous study at the mentioned sites, Kim and Singh Theor Appl Climatol 118:465-479, (2014) applied the popular Multilayer Perceptron (MLP) neural network and Adaptive Neuro-Fuzzy Inference System (ANFIS) nonlinear methods and considered HV as input variables. The comparison results signify that the relative error projected in estimating DST by the proposed methodology was about 6%, while this value with MLP and ANFIS was over 15%. Moreover, MLP and ANFIS models were employed for DST time series modeling. Due to these models' relatively inferior performance to the proposed methodology, two hybrid models were implemented: the weights and membership function of MLP and
A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress
Directory of Open Access Journals (Sweden)
Ching-Hsue Cheng
2018-01-01
Full Text Available The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i the proposed model is different from the previous models lacking the concept of time series; (ii the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies.
A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress
2018-01-01
The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i) the proposed model is different from the previous models lacking the concept of time series; (ii) the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii) the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies. PMID:29765399
A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress.
Cheng, Ching-Hsue; Chan, Chia-Pang; Yang, Jun-He
2018-01-01
The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i) the proposed model is different from the previous models lacking the concept of time series; (ii) the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii) the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies.
Nonlinear Prediction Model for Hydrologic Time Series Based on Wavelet Decomposition
Kwon, H.; Khalil, A.; Brown, C.; Lall, U.; Ahn, H.; Moon, Y.
2005-12-01
Traditionally forecasting and characterizations of hydrologic systems is performed utilizing many techniques. Stochastic linear methods such as AR and ARIMA and nonlinear ones such as statistical learning theory based tools have been extensively used. The common difficulty to all methods is the determination of sufficient and necessary information and predictors for a successful prediction. Relationships between hydrologic variables are often highly nonlinear and interrelated across the temporal scale. A new hybrid approach is proposed for the simulation of hydrologic time series combining both the wavelet transform and the nonlinear model. The present model employs some merits of wavelet transform and nonlinear time series model. The Wavelet Transform is adopted to decompose a hydrologic nonlinear process into a set of mono-component signals, which are simulated by nonlinear model. The hybrid methodology is formulated in a manner to improve the accuracy of a long term forecasting. The proposed hybrid model yields much better results in terms of capturing and reproducing the time-frequency properties of the system at hand. Prediction results are promising when compared to traditional univariate time series models. An application of the plausibility of the proposed methodology is provided and the results conclude that wavelet based time series model can be utilized for simulating and forecasting of hydrologic variable reasonably well. This will ultimately serve the purpose of integrated water resources planning and management.
Model-based Clustering of Categorical Time Series with Multinomial Logit Classification
Frühwirth-Schnatter, Sylvia; Pamminger, Christoph; Winter-Ebmer, Rudolf; Weber, Andrea
2010-09-01
A common problem in many areas of applied statistics is to identify groups of similar time series in a panel of time series. However, distance-based clustering methods cannot easily be extended to time series data, where an appropriate distance-measure is rather difficult to define, particularly for discrete-valued time series. Markov chain clustering, proposed by Pamminger and Frühwirth-Schnatter [6], is an approach for clustering discrete-valued time series obtained by observing a categorical variable with several states. This model-based clustering method is based on finite mixtures of first-order time-homogeneous Markov chain models. In order to further explain group membership we present an extension to the approach of Pamminger and Frühwirth-Schnatter [6] by formulating a probabilistic model for the latent group indicators within the Bayesian classification rule by using a multinomial logit model. The parameters are estimated for a fixed number of clusters within a Bayesian framework using an Markov chain Monte Carlo (MCMC) sampling scheme representing a (full) Gibbs-type sampler which involves only draws from standard distributions. Finally, an application to a panel of Austrian wage mobility data is presented which leads to an interesting segmentation of the Austrian labour market.
Improved time series prediction with a new method for selection of model parameters
International Nuclear Information System (INIS)
Jade, A M; Jayaraman, V K; Kulkarni, B D
2006-01-01
A new method for model selection in prediction of time series is proposed. Apart from the conventional criterion of minimizing RMS error, the method also minimizes the error on the distribution of singularities, evaluated through the local Hoelder estimates and its probability density spectrum. Predictions of two simulated and one real time series have been done using kernel principal component regression (KPCR) and model parameters of KPCR have been selected employing the proposed as well as the conventional method. Results obtained demonstrate that the proposed method takes into account the sharp changes in a time series and improves the generalization capability of the KPCR model for better prediction of the unseen test data. (letter to the editor)
Nonlinear time series modeling and forecasting the seismic data of the Hindu Kush region
Khan, Muhammad Yousaf; Mittnik, Stefan
2018-01-01
In this study, we extended the application of linear and nonlinear time models in the field of earthquake seismology and examined the out-of-sample forecast accuracy of linear Autoregressive (AR), Autoregressive Conditional Duration (ACD), Self-Exciting Threshold Autoregressive (SETAR), Threshold Autoregressive (TAR), Logistic Smooth Transition Autoregressive (LSTAR), Additive Autoregressive (AAR), and Artificial Neural Network (ANN) models for seismic data of the Hindu Kush region. We also extended the previous studies by using Vector Autoregressive (VAR) and Threshold Vector Autoregressive (TVAR) models and compared their forecasting accuracy with linear AR model. Unlike previous studies that typically consider the threshold model specifications by using internal threshold variable, we specified these models with external transition variables and compared their out-of-sample forecasting performance with the linear benchmark AR model. The modeling results show that time series models used in the present study are capable of capturing the dynamic structure present in the seismic data. The point forecast results indicate that the AR model generally outperforms the nonlinear models. However, in some cases, threshold models with external threshold variables specification produce more accurate forecasts, indicating that specification of threshold time series models is of crucial importance. For raw seismic data, the ACD model does not show an improved out-of-sample forecasting performance over the linear AR model. The results indicate that the AR model is the best forecasting device to model and forecast the raw seismic data of the Hindu Kush region.
Competency model for the project managers of technical projects
Duncan, William R.
1992-05-01
Traditional job description techniques were developed to support compensation decisions for hourly wage earners in a manufacturing environment. Their resultant focus on activities performed on the job works well in this environment where the ability to perform the activity adequately is objectively verifiable by testing and observation. Although many organizations have adapted these techniques for salaried employees and service environments, the focus on activities performed has never been satisfactory. For example, stating that a project manager `prepares regular project status reports' tells us little about what to look for in a potential project manager or how to determine if a practicing project manager is ready for additional responsibilities. The concept of a `competency model' has been developed within the last decade to address this shortcoming. Competency models focus on what skills are needed to perform the tasks defined by the job description. For example, a project manager must be able to communicate well both orally and in writing in order to `prepare regular project status reports.'
Cointegration and Error Correction Modelling in Time-Series Analysis: A Brief Introduction
Directory of Open Access Journals (Sweden)
Helmut Thome
2015-07-01
Full Text Available Criminological research is often based on time-series data showing some type of trend movement. Trending time-series may correlate strongly even in cases where no causal relationship exists (spurious causality. To avoid this problem researchers often apply some technique of detrending their data, such as by differencing the series. This approach, however, may bring up another problem: that of spurious non-causality. Both problems can, in principle, be avoided if the series under investigation are “difference-stationary” (if the trend movements are stochastic and “cointegrated” (if the stochastically changing trendmovements in different variables correspond to each other. The article gives a brief introduction to key instruments and interpretative tools applied in cointegration modelling.
Bayesian dynamic modeling of time series of dengue disease case counts.
Martínez-Bello, Daniel Adyro; López-Quílez, Antonio; Torres-Prieto, Alexander
2017-07-01
The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model's short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful
THE MODEL OF LIFELONG EDUCATION IN A TECHNICAL UNIVERSITY AS A MULTILEVEL EDUCATIONAL COMPLEX
Directory of Open Access Journals (Sweden)
Svetlana V. Sergeyeva
2016-06-01
Full Text Available Introduction: the current leading trend of the educational development is characterised by its continuity. Institutions of higher education as multi-level educational complexes nurture favourable conditions for realisation of the strategy of lifelong education. Today a technical university offering training of future engineers is facing a topic issue of creating a multilevel educational complex. Materials and Methods: this paper is put together on the basis of modern Russian and foreign scientific literature about lifelong education. The authors used theoretical methods of scientific research: systemstructural analysis, synthesis, modeling, analysis and generalisations of concepts. Results: the paper presents a model of lifelong education developed by authors for a technical university as a multilevel educational complex. It is realised through a set of principles: multi-level and continuity, integration, conformity and quality, mobility, anticipation, openness, social partnership and feedback. In accordance with the purpose, objectives and principles, the content part of the model is formed. The syllabi following the described model are run in accordance with the training levels undertaken by a technical university as a multilevel educational complex. All syllabi are based on the gradual nature of their implementation. In this regard, the authors highlight three phases: diagnostic, constructive and transformative, assessing. Discussion and Conclusions: the expected result of the created model of lifelong education development in a technical university as a multilevel educational complex is presented by a graduate trained for effective professional activity, competitive, prepared and sought-after at the regional labour market.
A multivariate time series approach to modeling and forecasting demand in the emergency department.
Jones, Spencer S; Evans, R Scott; Allen, Todd L; Thomas, Alun; Haug, Peter J; Welch, Shari J; Snow, Gregory L
2009-02-01
The goals of this investigation were to study the temporal relationships between the demands for key resources in the emergency department (ED) and the inpatient hospital, and to develop multivariate forecasting models. Hourly data were collected from three diverse hospitals for the year 2006. Descriptive analysis and model fitting were carried out using graphical and multivariate time series methods. Multivariate models were compared to a univariate benchmark model in terms of their ability to provide out-of-sample forecasts of ED census and the demands for diagnostic resources. Descriptive analyses revealed little temporal interaction between the demand for inpatient resources and the demand for ED resources at the facilities considered. Multivariate models provided more accurate forecasts of ED census and of the demands for diagnostic resources. Our results suggest that multivariate time series models can be used to reliably forecast ED patient census; however, forecasts of the demands for diagnostic resources were not sufficiently reliable to be useful in the clinical setting.
Zeng, Nianyin; Wang, Zidong; Li, Yurong; Du, Min; Cao, Jie; Liu, Xiaohui
2013-12-01
In this paper, the expectation maximization (EM) algorithm is applied to the modeling of the nano-gold immunochromatographic assay (nano-GICA) via available time series of the measured signal intensities of the test and control lines. The model for the nano-GICA is developed as the stochastic dynamic model that consists of a first-order autoregressive stochastic dynamic process and a noisy measurement. By using the EM algorithm, the model parameters, the actual signal intensities of the test and control lines, as well as the noise intensity can be identified simultaneously. Three different time series data sets concerning the target concentrations are employed to demonstrate the effectiveness of the introduced algorithm. Several indices are also proposed to evaluate the inferred models. It is shown that the model fits the data very well.
A time series modeling approach in risk appraisal of violent and sexual recidivism.
Bani-Yaghoub, Majid; Fedoroff, J Paul; Curry, Susan; Amundsen, David E
2010-10-01
For over half a century, various clinical and actuarial methods have been employed to assess the likelihood of violent recidivism. Yet there is a need for new methods that can improve the accuracy of recidivism predictions. This study proposes a new time series modeling approach that generates high levels of predictive accuracy over short and long periods of time. The proposed approach outperformed two widely used actuarial instruments (i.e., the Violence Risk Appraisal Guide and the Sex Offender Risk Appraisal Guide). Furthermore, analysis of temporal risk variations based on specific time series models can add valuable information into risk assessment and management of violent offenders.
The influence of noise on nonlinear time series detection based on Volterra-Wiener-Korenberg model
Energy Technology Data Exchange (ETDEWEB)
Lei Min [State Key Laboratory of Vibration, Shock and Noise, Shanghai Jiao Tong University, Shanghai 200030 (China)], E-mail: leimin@sjtu.edu.cn; Meng Guang [State Key Laboratory of Vibration, Shock and Noise, Shanghai Jiao Tong University, Shanghai 200030 (China)
2008-04-15
This paper studies the influence of noises on Volterra-Wiener-Korenberg (VWK) nonlinear test model. Our numerical results reveal that different types of noises lead to different behavior of VWK model detection. For dynamic noise, it is difficult to distinguish chaos from nonchaotic but nonlinear determinism. For time series, measure noise has no impact on chaos determinism detection. This paper also discusses various behavior of VWK model detection with surrogate data for different noises.
Forecasting electricity spot-prices using linear univariate time-series models
International Nuclear Information System (INIS)
Cuaresma, Jesus Crespo; Hlouskova, Jaroslava; Kossmeier, Stephan; Obersteiner, Michael
2004-01-01
This paper studies the forecasting abilities of a battery of univariate models on hourly electricity spot prices, using data from the Leipzig Power Exchange. The specifications studied include autoregressive models, autoregressive-moving average models and unobserved component models. The results show that specifications, where each hour of the day is modelled separately present uniformly better forecasting properties than specifications for the whole time-series, and that the inclusion of simple probabilistic processes for the arrival of extreme price events can lead to improvements in the forecasting abilities of univariate models for electricity spot prices. (Author)
Application of the Laplace transform method for computational modelling of radioactive decay series
Energy Technology Data Exchange (ETDEWEB)
Oliveira, Deise L.; Damasceno, Ralf M.; Barros, Ricardo C. [Univ. do Estado do Rio de Janeiro (IME/UERJ) (Brazil). Programa de Pos-graduacao em Ciencias Computacionais
2012-03-15
It is well known that when spent fuel is removed from the core, it is still composed of considerable amount of radioactive elements with significant half-lives. Most actinides, in particular plutonium, fall into this category, and have to be safely disposed of. One solution is to store the long-lived spent fuel as it is, by encasing and burying it deep underground in a stable geological formation. This implies estimating the transmutation of these radioactive elements with time. Therefore, we describe in this paper the application of the Laplace transform technique in matrix formulation to analytically solve initial value problems that mathematically model radioactive decay series. Given the initial amount of each type of radioactive isotopes in the decay series, the computer code generates the amount at a given time of interest, or may plot a graph of the evolution in time of the amount of each type of isotopes in the series. This computer code, that we refer to as the LTRad{sub L} code, where L is the number of types of isotopes belonging to the series, was developed using the Scilab free platform for numerical computation and can model one segment or the entire chain of any of the three radioactive series existing on Earth today. Numerical results are given to typical model problems to illustrate the computer code efficiency and accuracy. (orig.)
Application of the Laplace transform method for computational modelling of radioactive decay series
International Nuclear Information System (INIS)
Oliveira, Deise L.; Damasceno, Ralf M.; Barros, Ricardo C.
2012-01-01
It is well known that when spent fuel is removed from the core, it is still composed of considerable amount of radioactive elements with significant half-lives. Most actinides, in particular plutonium, fall into this category, and have to be safely disposed of. One solution is to store the long-lived spent fuel as it is, by encasing and burying it deep underground in a stable geological formation. This implies estimating the transmutation of these radioactive elements with time. Therefore, we describe in this paper the application of the Laplace transform technique in matrix formulation to analytically solve initial value problems that mathematically model radioactive decay series. Given the initial amount of each type of radioactive isotopes in the decay series, the computer code generates the amount at a given time of interest, or may plot a graph of the evolution in time of the amount of each type of isotopes in the series. This computer code, that we refer to as the LTRad L code, where L is the number of types of isotopes belonging to the series, was developed using the Scilab free platform for numerical computation and can model one segment or the entire chain of any of the three radioactive series existing on Earth today. Numerical results are given to typical model problems to illustrate the computer code efficiency and accuracy. (orig.)
Directory of Open Access Journals (Sweden)
Yolanda Navarro-Abal
2012-12-01
Full Text Available Television fiction series sometimes generate an unreal vision of life, especially among young people, becoming a mirror in which they can see themselves reflected. The series become models of values, attitudes, skills and behaviours that tend to be imitated by some viewers. The aim of this study was to analyze the conflict management behavioural styles presented by the main characters of television fiction series. Thus, we evaluated the association between these styles and the age and sex of the main characters, as well as the nationality and genre of the fiction series. 16 fiction series were assessed by selecting two characters of both sexes from each series. We adapted the Rahim Organizational Conflict Inventory-II for observing and recording the data. The results show that there is no direct association between the conflict management behavioural styles presented in the drama series and the sex of the main characters. However, associations were found between these styles and the age of the characters and the genre of the fiction series.
Directory of Open Access Journals (Sweden)
Rui Xue
2015-01-01
Full Text Available Although bus passenger demand prediction has attracted increased attention during recent years, limited research has been conducted in the context of short-term passenger demand forecasting. This paper proposes an interactive multiple model (IMM filter algorithm-based model to predict short-term passenger demand. After aggregated in 15 min interval, passenger demand data collected from a busy bus route over four months were used to generate time series. Considering that passenger demand exhibits various characteristics in different time scales, three time series were developed, named weekly, daily, and 15 min time series. After the correlation, periodicity, and stationarity analyses, time series models were constructed. Particularly, the heteroscedasticity of time series was explored to achieve better prediction performance. Finally, IMM filter algorithm was applied to combine individual forecasting models with dynamically predicted passenger demand for next interval. Different error indices were adopted for the analyses of individual and hybrid models. The performance comparison indicates that hybrid model forecasts are superior to individual ones in accuracy. Findings of this study are of theoretical and practical significance in bus scheduling.
Technical progress in INPRO activities on modelling and innovation
International Nuclear Information System (INIS)
Villalibre, P.; Haas, E.; Khartabil, H.; Kim, S.; Korinny, A.; Usanov, V. and others
2010-01-01
Among the 31 Members of the 'International Project on Innovative Nuclear Reactors and Fuel Cycles' (INPRO) in April 2010, more than 20 are cooperating in the implementation of Collaborative Projects (CPs) related to the four substantive Programme Areas of the INPRO Action Plan. The purpose of the CPs is to contribute to the achievement of goals established in the programmatic areas. This paper presents progress status of several CPs dealing with Modelling and Innovation: - CP on Environmental Impact Benchmarking applicable to Nuclear Energy Systems under Normal Operation (ENV): A benchmark of codes and methods for determining radiation released during normal operation of nuclear facilities is performed by using established source term, release scenario, and target group (humans). - CP on Proliferation Resistance: Acquisition/Diversion Pathway Analysis (PRADA): 'PRADA' addresses the identification and analyses of high level pathways for the acquisition of weapons usable material, and makes recommendations for evaluating the multiplicity and robustness of barriers against proliferation. PRADA develops a case study based on DUPIC fuel cycle. The outcome from the project will support the assessment methodologies developed at GIF and INPRO. - CP on Global Architecture of Nuclear Energy Systems based on Thermal and Fast Reactors including Closed Fuel Cycle (GAINS): GAINS objective is to develop a methodological platform for assessing future nuclear energy systems taking into account the sustainable development, and to validate the results through sample analyses. High and moderate scenarios of nuclear energy demand and supply during the Century (5000 and 2500 GWe respectively in the year 2100) are being analysed using homogeneous and heterogeneous considerations. A non-geographic approach grouping the countries according to their planned use of nuclear energy has being established. - CP on Investigations of the 233 U/Th Fuel Cycle (ThFC): ThFC objective is to explore FC
Time-series modeling: applications to long-term finfish monitoring data
International Nuclear Information System (INIS)
Bireley, L.E.
1985-01-01
The growing concern and awareness that developed during the 1970's over the effects that industry had on the environment caused the electric utility industry in particular to develop monitoring programs. These programs generate long-term series of data that are not very amenable to classical normal-theory statistical analysis. The monitoring data collected from three finfish programs (impingement, trawl and seine) at the Millstone Nuclear Power Station were typical of such series and thus were used to develop methodology that used the full extent of the information in the series. The basis of the methodology was classic Box-Jenkins time-series modeling; however, the models also included deterministic components that involved flow, season and time as predictor variables. Time entered into the models as harmonic regression terms. Of the 32 models fitted to finfish catch data, 19 were found to account for more than 70% of the historical variation. The models were than used to forecast finfish catches a year in advance and comparisons were made to actual data. Usually the confidence intervals associated with the forecasts encompassed most of the observed data. The technique can provide the basis for intervention analysis in future impact assessments
Docherty, Gary
1976-01-01
One way to bring the accounting office into the classroom is to conduct the class as a "company." Such a class is aimed at developing students' technical and managerial skills, as well as their career awareness and career goals. Performance goals, a course description, and overall objectives of the course are given and might serve as a model.…
Agent-Based Modeling and Analysis of Socio-Technical Systems
Sharpanskykh, O.
2011-01-01
Socio-technical systems are characterized by high structural and behavioral complexities, which impede understanding and modeling of such systems. In particular, reciprocal relations between diverse local system processes that determine global system dynamics are not well understood. In this article
Asabere, Nana; Togo, Gilbert; Acakpovi, Amevi; Torby, Wisdom; Ampadu, Kwame
2017-01-01
Information and Communication Technologies (ICT) has changed the way we communicate and carry out certain daily activities. Globally, ICT has become an essential means for disseminating information. Using Accra Technical University in Ghana as a case study, this paper proposes an ICT model called Awareness Incentives Demand and Support (AIDS). Our…
A reference model and technical framework for mobile social software for learning
De Jong, Tim; Specht, Marcus; Koper, Rob
2008-01-01
De Jong,T., Specht, M., & Koper, R. (2008). A reference model and technical framework for mobile social software for learning. In I. A. Sánchez & P. Isaías (Eds.), Proceedings of the IADIS Mobile Learning Conference 2008 (pp. 206-210). April, 11-13, 2008, Carvoeiro, Portugal.
Applicability of Socio-Technical Model (STM in Working System of Modern Organizations
Directory of Open Access Journals (Sweden)
Rosmaini Tasmin
2011-10-01
Full Text Available Knowledge has been identified as one of the most important resources in organization that contributes to competitive advantages. Organizations around the world realize and put into practice an approach that bases on technological and sociological aspects to fill-up the gaps in their workplaces. The Socio-Technical Model (STM is an established organizational model introduced by Trist since 1960s at Tavistock Institute, London. It relates two most common components exist in all organizations, namely social systems (human and technological systems (information technology, machinery and equipment in organizations over many decades. This paper reviews the socio-technical model from various perspectives of its developmental stages and ideas written by researchers. Therefore, several literature reviews on socio-technical model have been compiled and discussed to justify whether its basic argument matches with required practices in Techno-Social environments. Through a socio-technical perspective on Knowledge Management, this paper highlights the interplay between social systems and technological system. It also suggests that management and leadership play critical roles in establishing the techno-social perspective for the effective assimilation of Knowledge Management practices.
Time-series modeling of long-term weight self-monitoring data.
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.
Markov Chain Modelling for Short-Term NDVI Time Series Forecasting
Directory of Open Access Journals (Sweden)
Stepčenko Artūrs
2016-12-01
Full Text Available In this paper, the NDVI time series forecasting model has been developed based on the use of discrete time, continuous state Markov chain of suitable order. The normalised difference vegetation index (NDVI is an indicator that describes the amount of chlorophyll (the green mass and shows the relative density and health of vegetation; therefore, it is an important variable for vegetation forecasting. A Markov chain is a stochastic process that consists of a state space. This stochastic process undergoes transitions from one state to another in the state space with some probabilities. A Markov chain forecast model is flexible in accommodating various forecast assumptions and structures. The present paper discusses the considerations and techniques in building a Markov chain forecast model at each step. Continuous state Markov chain model is analytically described. Finally, the application of the proposed Markov chain model is illustrated with reference to a set of NDVI time series data.
Time-series models on somatic cell score improve detection of matistis
DEFF Research Database (Denmark)
Norberg, E; Korsgaard, I R; Sloth, K H M N
2008-01-01
In-line detection of mastitis using frequent milk sampling was studied in 241 cows in a Danish research herd. Somatic cell scores obtained at a daily basis were analyzed using a mixture of four time-series models. Probabilities were assigned to each model for the observations to belong to a normal...... "steady-state" development, change in "level", change of "slope" or "outlier". Mastitis was indicated from the sum of probabilities for the "level" and "slope" models. Time-series models were based on the Kalman filter. Reference data was obtained from veterinary assessment of health status combined...... with bacteriological findings. At a sensitivity of 90% the corresponding specificity was 68%, which increased to 83% using a one-step back smoothing. It is concluded that mixture models based on Kalman filters are efficient in handling in-line sensor data for detection of mastitis and may be useful for similar...
Generation of Natural Runoff Monthly Series at Ungauged Sites Using a Regional Regressive Model
Directory of Open Access Journals (Sweden)
Dario Pumo
2016-05-01
Full Text Available Many hydrologic applications require reliable estimates of runoff in river basins to face the widespread lack of data, both in time and in space. A regional method for the reconstruction of monthly runoff series is here developed and applied to Sicily (Italy. A simple modeling structure is adopted, consisting of a regression-based rainfall–runoff model with four model parameters, calibrated through a two-step procedure. Monthly runoff estimates are based on precipitation, temperature, and exploiting the autocorrelation with runoff at the previous month. Model parameters are assessed by specific regional equations as a function of easily measurable physical and climate basin descriptors. The first calibration step is aimed at the identification of a set of parameters optimizing model performances at the level of single basin. Such “optimal” sets are used at the second step, part of a regional regression analysis, to establish the regional equations for model parameters assessment as a function of basin attributes. All the gauged watersheds across the region have been analyzed, selecting 53 basins for model calibration and using the other six basins exclusively for validation. Performances, quantitatively evaluated by different statistical indexes, demonstrate relevant model ability in reproducing the observed hydrological time-series at both the monthly and coarser time resolutions. The methodology, which is easily transferable to other arid and semi-arid areas, provides a reliable tool for filling/reconstructing runoff time series at any gauged or ungauged basin of a region.
Time series modeling by a regression approach based on a latent process.
Chamroukhi, Faicel; Samé, Allou; Govaert, Gérard; Aknin, Patrice
2009-01-01
Time series are used in many domains including finance, engineering, economics and bioinformatics generally to represent the change of a measurement over time. Modeling techniques may then be used to give a synthetic representation of such data. A new approach for time series modeling is proposed in this paper. It consists of a regression model incorporating a discrete hidden logistic process allowing for activating smoothly or abruptly different polynomial regression models. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. The M step of the EM algorithm uses a multi-class Iterative Reweighted Least-Squares (IRLS) algorithm to estimate the hidden process parameters. To evaluate the proposed approach, an experimental study on simulated data and real world data was performed using two alternative approaches: a heteroskedastic piecewise regression model using a global optimization algorithm based on dynamic programming, and a Hidden Markov Regression Model whose parameters are estimated by the Baum-Welch algorithm. Finally, in the context of the remote monitoring of components of the French railway infrastructure, and more particularly the switch mechanism, the proposed approach has been applied to modeling and classifying time series representing the condition measurements acquired during switch operations.
Transfer function modeling of the monthly accumulated rainfall series over the Iberian Peninsula
Energy Technology Data Exchange (ETDEWEB)
Mateos, Vidal L.; Garcia, Jose A.; Serrano, Antonio; De la Cruz Gallego, Maria [Departamento de Fisica, Universidad de Extremadura, Badajoz (Spain)
2002-10-01
In order to improve the results given by Autoregressive Moving-Average (ARMA) modeling for the monthly accumulated rainfall series taken at 19 observatories of the Iberian Peninsula, a Discrete Linear Transfer Function Noise (DLTFN) model was applied taking the local pressure series (LP), North Atlantic sea level pressure series (SLP) and North Atlantic sea surface temperature (SST) as input variables, and the rainfall series as the output series. In all cases, the performance of the DLTFN models, measured by the explained variance of the rainfall series, is better than the performance given by the ARMA modeling. The best performance is given by the models that take the local pressure as the input variable, followed by the sea level pressure models and the sea surface temperature models. Geographically speaking, the models fitted to those observatories located in the west of the Iberian Peninsula work better than those on the north and east of the Peninsula. Also, it was found that there is a region located between 0 N and 20 N, which shows the highest cross-correlation between SST and the peninsula rainfalls. This region moves to the west and northwest off the Peninsula when the SLP series are used. [Spanish] Con el objeto de mejorar los resultados porporcionados por los modelos Autorregresivo Media Movil (ARMA) ajustados a las precipitaciones mensuales acumuladas registradas en 19 observatorios de la Peninsula Iberica se han usado modelos de funcion de transferencia (DLTFN) en los que se han empleado como variable independiente la presion local (LP), la presion a nivel del mar (SLP) o la temperatura de agua del mar (SST) en el Atlantico Norte. En todos los casos analizados, los resultados obtenidos con los modelos DLTFN, medidos mediante la varianza explicada por el modelo, han sido mejores que los resultados proporcionados por los modelos ARMA. Los mejores resultados han sido dados por aquellos modelos que usan la presion local como variable de entrada, seguidos
DEFF Research Database (Denmark)
Kamel, S.; Jurado, F.; Chen, Zhe
2015-01-01
This paper presents an implicit modeling of Static Synchronous Series Compensator (SSSC) in Newton–Raphson load flow method. The algorithm of load flow is based on the revised current injection formulation. The developed model of SSSC is depended on the current injection approach. In this model...... will be in the mismatches vector. Finally, this modeling solves the problem that happens when the SSSC is only connected between two areas. Numerical examples on the WSCC 9-bus, IEEE 30-bus system, and IEEE 118-bus system are used to illustrate the feasibility of the developed SSSC model and performance of the Newton–Raphson...
Studies in astronomical time series analysis. I - Modeling random processes in the time domain
Scargle, J. D.
1981-01-01
Several random process models in the time domain are defined and discussed. Attention is given to the moving average model, the autoregressive model, and relationships between and combinations of these models. Consideration is then given to methods for investigating pulse structure, procedures of model construction, computational methods, and numerical experiments. A FORTRAN algorithm of time series analysis has been developed which is relatively stable numerically. Results of test cases are given to study the effect of adding noise and of different distributions for the pulse amplitudes. A preliminary analysis of the light curve of the quasar 3C 272 is considered as an example.
Data on copula modeling of mixed discrete and continuous neural time series.
Hu, Meng; Li, Mingyao; Li, Wu; Liang, Hualou
2016-06-01
Copula is an important tool for modeling neural dependence. Recent work on copula has been expanded to jointly model mixed time series in neuroscience ("Hu et al., 2016, Joint Analysis of Spikes and Local Field Potentials using Copula" [1]). Here we present further data for joint analysis of spike and local field potential (LFP) with copula modeling. In particular, the details of different model orders and the influence of possible spike contamination in LFP data from the same and different electrode recordings are presented. To further facilitate the use of our copula model for the analysis of mixed data, we provide the Matlab codes, together with example data.
Modeling the impact of forecast-based regime switches on macroeconomic time series
K. Bel (Koen); R. Paap (Richard)
2013-01-01
textabstractForecasts of key macroeconomic variables may lead to policy changes of governments, central banks and other economic agents. Policy changes in turn lead to structural changes in macroeconomic time series models. To describe this phenomenon we introduce a logistic smooth transition
76 FR 36390 - Airworthiness Directives; The Boeing Company Model 747SP Series Airplanes
2011-06-22
... power control modules (PCM). This proposed AD was prompted by a report of a rudder hard-over event on a... rudder PCM manifold, which could result in a hard-over of the rudder surface leading to an increase in... of a Model 747-400 series airplane of a lower rudder hard-over event caused by a lower rudder PCM...
Using the mean approach in pooling cross-section and time series data for regression modelling
International Nuclear Information System (INIS)
Nuamah, N.N.N.N.
1989-12-01
The mean approach is one of the methods for pooling cross section and time series data for mathematical-statistical modelling. Though a simple approach, its results are sometimes paradoxical in nature. However, researchers still continue using it for its simplicity. Here, the paper investigates the nature and source of such unwanted phenomena. (author). 7 refs
A robust interrupted time series model for analyzing complex health care intervention data
Cruz, Maricela
2017-08-29
Current health policy calls for greater use of evidence-based care delivery services to improve patient quality and safety outcomes. Care delivery is complex, with interacting and interdependent components that challenge traditional statistical analytic techniques, in particular, when modeling a time series of outcomes data that might be
Molenaar, P.C.M.
1987-01-01
Outlines a frequency domain analysis of the dynamic factor model and proposes a solution to the problem of constructing a causal filter of lagged factor loadings. The method is illustrated with applications to simulated and real multivariate time series. The latter applications involve topographic
ShapeSelectForest: a new r package for modeling landsat time series
Mary Meyer; Xiyue Liao; Gretchen Moisen; Elizabeth Freeman
2015-01-01
We present a new R package called ShapeSelectForest recently posted to the Comprehensive R Archival Network. The package was developed to fit nonparametric shape-restricted regression splines to time series of Landsat imagery for the purpose of modeling, mapping, and monitoring annual forest disturbance dynamics over nearly three decades. For each pixel and spectral...
Barry T. Wilson; Joseph F. Knight; Ronald E. McRoberts
2018-01-01
Imagery from the Landsat Program has been used frequently as a source of auxiliary data for modeling land cover, as well as a variety of attributes associated with tree cover. With ready access to all scenes in the archive since 2008 due to the USGS Landsat Data Policy, new approaches to deriving such auxiliary data from dense Landsat time series are required. Several...
A Model for Mental Health Programming in Schools and Communities: Introduction to the Mini-Series.
Nastasi, Bonnie K.
1998-01-01
Describes conceptual framework for mini-series on mental health programming. Model includes five components considered to be critical for comprehensive and effective programming. Components include: action research, ecological perspective of program development, collaborative/participatory approach, prevention to treatment via mental health…
76 FR 6584 - Airworthiness Directives; Bombardier, Inc. Model DHC-8-400 Series Airplanes
2011-02-07
.... Model DHC-8-400 Series Airplanes AGENCY: Federal Aviation Administration (FAA), DOT. ACTION: Notice of... area on the rib at Yw-42.000 to ensure adequate electrical bonding, installing spiral wrap on certain cable assemblies where existing spiral wrap does not extend 4 inches past the tie mounts, applying a...
A robust interrupted time series model for analyzing complex health care intervention data
Cruz, Maricela; Bender, Miriam; Ombao, Hernando
2017-01-01
Current health policy calls for greater use of evidence-based care delivery services to improve patient quality and safety outcomes. Care delivery is complex, with interacting and interdependent components that challenge traditional statistical analytic techniques, in particular, when modeling a time series of outcomes data that might be
Magill, Molly; Apodaca, Timothy R; Borsari, Brian; Gaume, Jacques; Hoadley, Ariel; Gordon, Rebecca E F; Tonigan, J Scott; Moyers, Theresa
2018-02-01
In the present meta-analysis, we test the technical and relational hypotheses of Motivational Interviewing (MI) efficacy. We also propose an a priori conditional process model where heterogeneity of technical path effect sizes should be explained by interpersonal/relational (i.e., empathy, MI Spirit) and intrapersonal (i.e., client treatment seeking status) moderators. A systematic review identified k = 58 reports, describing 36 primary studies and 40 effect sizes (N = 3,025 participants). Statistical methods calculated the inverse variance-weighted pooled correlation coefficient for the therapist to client and the client to outcome paths across multiple target behaviors (i.e., alcohol use, other drug use, other behavior change). Therapist MI-consistent skills were correlated with more client change talk (r = .55, p technical hypothesis was supported. Specifically, proportion MI consistency was related to higher proportion change talk (r = .11, p = .004) and higher proportion change talk was related to reductions in risk behavior at follow up (r = -.16, p technical hypothesis path effect sizes was partially explained by inter- and intrapersonal moderators. This meta-analysis provides additional support for the technical hypothesis of MI efficacy; future research on the relational hypothesis should occur in the field rather than in the context of clinical trials. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
Min, Ari; Park, Chang Gi; Scott, Linda D
2016-05-23
Data envelopment analysis (DEA) is an advantageous non-parametric technique for evaluating relative efficiency of performance. This article describes use of DEA to estimate technical efficiency of nursing care and demonstrates the benefits of using multilevel modeling to identify characteristics of efficient facilities in the second stage of analysis. Data were drawn from LTCFocUS.org, a secondary database including nursing home data from the Online Survey Certification and Reporting System and Minimum Data Set. In this example, 2,267 non-hospital-based nursing homes were evaluated. Use of DEA with nurse staffing levels as inputs and quality of care as outputs allowed estimation of the relative technical efficiency of nursing care in these facilities. In the second stage, multilevel modeling was applied to identify organizational factors contributing to technical efficiency. Use of multilevel modeling avoided biased estimation of findings for nested data and provided comprehensive information on differences in technical efficiency among counties and states. © The Author(s) 2016.
Evaluation of the autoregression time-series model for analysis of a noisy signal
International Nuclear Information System (INIS)
Allen, J.W.
1977-01-01
The autoregression (AR) time-series model of a continuous noisy signal was statistically evaluated to determine quantitatively the uncertainties of the model order, the model parameters, and the model's power spectral density (PSD). The result of such a statistical evaluation enables an experimenter to decide whether an AR model can adequately represent a continuous noisy signal and be consistent with the signal's frequency spectrum, and whether it can be used for on-line monitoring. Although evaluations of other types of signals have been reported in the literature, no direct reference has been found to AR model's uncertainties for continuous noisy signals; yet the evaluation is necessary to decide the usefulness of AR models of typical reactor signals (e.g., neutron detector output or thermocouple output) and the potential of AR models for on-line monitoring applications. AR and other time-series models for noisy data representation are being investigated by others since such models require fewer parameters than the traditional PSD model. For this study, the AR model was selected for its simplicity and conduciveness to uncertainty analysis, and controlled laboratory bench signals were used for continuous noisy data. (author)
Modeling pollen time series using seasonal-trend decomposition procedure based on LOESS smoothing.
Rojo, Jesús; Rivero, Rosario; Romero-Morte, Jorge; Fernández-González, Federico; Pérez-Badia, Rosa
2017-02-01
Analysis of airborne pollen concentrations provides valuable information on plant phenology and is thus a useful tool in agriculture-for predicting harvests in crops such as the olive and for deciding when to apply phytosanitary treatments-as well as in medicine and the environmental sciences. Variations in airborne pollen concentrations, moreover, are indicators of changing plant life cycles. By modeling pollen time series, we can not only identify the variables influencing pollen levels but also predict future pollen concentrations. In this study, airborne pollen time series were modeled using a seasonal-trend decomposition procedure based on LOcally wEighted Scatterplot Smoothing (LOESS) smoothing (STL). The data series-daily Poaceae pollen concentrations over the period 2006-2014-was broken up into seasonal and residual (stochastic) components. The seasonal component was compared with data on Poaceae flowering phenology obtained by field sampling. Residuals were fitted to a model generated from daily temperature and rainfall values, and daily pollen concentrations, using partial least squares regression (PLSR). This method was then applied to predict daily pollen concentrations for 2014 (independent validation data) using results for the seasonal component of the time series and estimates of the residual component for the period 2006-2013. Correlation between predicted and observed values was r = 0.79 (correlation coefficient) for the pre-peak period (i.e., the period prior to the peak pollen concentration) and r = 0.63 for the post-peak period. Separate analysis of each of the components of the pollen data series enables the sources of variability to be identified more accurately than by analysis of the original non-decomposed data series, and for this reason, this procedure has proved to be a suitable technique for analyzing the main environmental factors influencing airborne pollen concentrations.
On determining the prediction limits of mathematical models for time series
International Nuclear Information System (INIS)
Peluso, E.; Gelfusa, M.; Lungaroni, M.; Talebzadeh, S.; Gaudio, P.; Murari, A.; Contributors, JET
2016-01-01
Prediction is one of the main objectives of scientific analysis and it refers to both modelling and forecasting. The determination of the limits of predictability is an important issue of both theoretical and practical relevance. In the case of modelling time series, reached a certain level in performance in either modelling or prediction, it is often important to assess whether all the information available in the data has been exploited or whether there are still margins for improvement of the tools being developed. In this paper, an information theoretic approach is proposed to address this issue and quantify the quality of the models and/or predictions. The excellent properties of the proposed indicator have been proved with the help of a systematic series of numerical tests and a concrete example of extreme relevance for nuclear fusion.
Nonlinear Fluctuation Behavior of Financial Time Series Model by Statistical Physics System
Directory of Open Access Journals (Sweden)
Wuyang Cheng
2014-01-01
Full Text Available We develop a random financial time series model of stock market by one of statistical physics systems, the stochastic contact interacting system. Contact process is a continuous time Markov process; one interpretation of this model is as a model for the spread of an infection, where the epidemic spreading mimics the interplay of local infections and recovery of individuals. From this financial model, we study the statistical behaviors of return time series, and the corresponding behaviors of returns for Shanghai Stock Exchange Composite Index (SSECI and Hang Seng Index (HSI are also comparatively studied. Further, we investigate the Zipf distribution and multifractal phenomenon of returns and price changes. Zipf analysis and MF-DFA analysis are applied to investigate the natures of fluctuations for the stock market.
Analysis of Data from a Series of Events by a Geometric Process Model
Institute of Scientific and Technical Information of China (English)
Yeh Lam; Li-xing Zhu; Jennifer S. K. Chan; Qun Liu
2004-01-01
Geometric process was first introduced by Lam[10,11]. A stochastic process {Xi, i = 1, 2,…} is called a geometric process (GP) if, for some a > 0, {ai-1Xi, i = 1, 2,…} forms a renewal process. In thispaper, the GP is used to analyze the data from a series of events. A nonparametric method is introduced forthe estimation of the three parameters in the GP. The limiting distributions of the three estimators are studied.Through the analysis of some real data sets, the GP model is compared with other three homogeneous andnonhomogeneous Poisson models. It seems that on average the GP model is the best model among these fourmodels in analyzing the data from a series of events.
A regional and nonstationary model for partial duration series of extreme rainfall
DEFF Research Database (Denmark)
Gregersen, Ida Bülow; Madsen, Henrik; Rosbjerg, Dan
2017-01-01
as the explanatory variables in the regional and temporal domain, respectively. Further analysis of partial duration series with nonstationary and regional thresholds shows that the mean exceedances also exhibit a significant variation in space and time for some rainfall durations, while the shape parameter is found...... of extreme rainfall. The framework is built on a partial duration series approach with a nonstationary, regional threshold value. The model is based on generalized linear regression solved by generalized estimation equations. It allows a spatial correlation between the stations in the network and accounts...... furthermore for variable observation periods at each station and in each year. Marginal regional and temporal regression models solved by generalized least squares are used to validate and discuss the results of the full spatiotemporal model. The model is applied on data from a large Danish rain gauge network...
Automated Bayesian model development for frequency detection in biological time series
Directory of Open Access Journals (Sweden)
Oldroyd Giles ED
2011-06-01
Full Text Available Abstract Background A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. Results In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Conclusions Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and
Automated Bayesian model development for frequency detection in biological time series.
Granqvist, Emma; Oldroyd, Giles E D; Morris, Richard J
2011-06-24
A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and the requirement for uniformly sampled data. Biological time
Vector Control Using Series Iron Loss Model of Induction, Motors and Power Loss Minimization
Kheldoun Aissa; Khodja Djalal Eddine
2009-01-01
The iron loss is a source of detuning in vector controlled induction motor drives if the classical rotor vector controller is used for decoupling. In fact, the field orientation will not be satisfied and the output torque will not truck the reference torque mostly used by Loss Model Controllers (LMCs). In addition, this component of loss, among others, may be excessive if the vector controlled induction motor is driving light loads. In this paper, the series iron loss model ...
Lohani, A. K.; Kumar, Rakesh; Singh, R. D.
2012-06-01
SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.
Designing water demand management schemes using a socio-technical modelling approach.
Baki, Sotiria; Rozos, Evangelos; Makropoulos, Christos
2018-05-01
Although it is now widely acknowledged that urban water systems (UWSs) are complex socio-technical systems and that a shift towards a socio-technical approach is critical in achieving sustainable urban water management, still, more often than not, UWSs are designed using a segmented modelling approach. As such, either the analysis focuses on the description of the purely technical sub-system, without explicitly taking into account the system's dynamic socio-economic processes, or a more interdisciplinary approach is followed, but delivered through relatively coarse models, which often fail to provide a thorough representation of the urban water cycle and hence cannot deliver accurate estimations of the hydrosystem's responses. In this work we propose an integrated modelling approach for the study of the complete socio-technical UWS that also takes into account socio-economic and climatic variability. We have developed an integrated model, which is used to investigate the diffusion of household water conservation technologies and its effects on the UWS, under different socio-economic and climatic scenarios. The integrated model is formed by coupling a System Dynamics model that simulates the water technology adoption process, and the Urban Water Optioneering Tool (UWOT) for the detailed simulation of the urban water cycle. The model and approach are tested and demonstrated in an urban redevelopment area in Athens, Greece under different socio-economic scenarios and policy interventions. It is suggested that the proposed approach can establish quantifiable links between socio-economic change and UWS responses and therefore assist decision makers in designing more effective and resilient long-term strategies for water conservation. Copyright © 2017 Elsevier B.V. All rights reserved.
Multi-Step Time Series Forecasting with an Ensemble of Varied Length Mixture Models.
Ouyang, Yicun; Yin, Hujun
2018-05-01
Many real-world problems require modeling and forecasting of time series, such as weather temperature, electricity demand, stock prices and foreign exchange (FX) rates. Often, the tasks involve predicting over a long-term period, e.g. several weeks or months. Most existing time series models are inheritably for one-step prediction, that is, predicting one time point ahead. Multi-step or long-term prediction is difficult and challenging due to the lack of information and uncertainty or error accumulation. The main existing approaches, iterative and independent, either use one-step model recursively or treat the multi-step task as an independent model. They generally perform poorly in practical applications. In this paper, as an extension of the self-organizing mixture autoregressive (AR) model, the varied length mixture (VLM) models are proposed to model and forecast time series over multi-steps. The key idea is to preserve the dependencies between the time points within the prediction horizon. Training data are segmented to various lengths corresponding to various forecasting horizons, and the VLM models are trained in a self-organizing fashion on these segments to capture these dependencies in its component AR models of various predicting horizons. The VLM models form a probabilistic mixture of these varied length models. A combination of short and long VLM models and an ensemble of them are proposed to further enhance the prediction performance. The effectiveness of the proposed methods and their marked improvements over the existing methods are demonstrated through a number of experiments on synthetic data, real-world FX rates and weather temperatures.
PSO-MISMO modeling strategy for multistep-ahead time series prediction.
Bao, Yukun; Xiong, Tao; Hu, Zhongyi
2014-05-01
Multistep-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research. Recently, the multiple-input several multiple-outputs (MISMO) modeling strategy has been proposed as a promising alternative for multistep-ahead time series prediction, exhibiting advantages compared with the two currently dominating strategies, the iterated and the direct strategies. Built on the established MISMO strategy, this paper proposes a particle swarm optimization (PSO)-based MISMO modeling strategy, which is capable of determining the number of sub-models in a self-adaptive mode, with varying prediction horizons. Rather than deriving crisp divides with equal-size s prediction horizons from the established MISMO, the proposed PSO-MISMO strategy, implemented with neural networks, employs a heuristic to create flexible divides with varying sizes of prediction horizons and to generate corresponding sub-models, providing considerable flexibility in model construction, which has been validated with simulated and real datasets.
DEFF Research Database (Denmark)
Ørregård Nielsen, Morten
This paper proves consistency and asymptotic normality for the conditional-sum-of-squares estimator, which is equivalent to the conditional maximum likelihood estimator, in multivariate fractional time series models. The model is parametric and quite general, and, in particular, encompasses...... the multivariate non-cointegrated fractional ARIMA model. The novelty of the consistency result, in particular, is that it applies to a multivariate model and to an arbitrarily large set of admissible parameter values, for which the objective function does not converge uniformly in probablity, thus making...
Energy Technology Data Exchange (ETDEWEB)
Shield, Stephen Allan [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Dai, Zhenxue [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2015-08-18
Meteorological inputs are an important part of subsurface flow and transport modeling. The choice of source for meteorological data used as inputs has significant impacts on the results of subsurface flow and transport studies. One method to obtain the meteorological data required for flow and transport studies is the use of weather generating models. This paper compares the difference in performance of two weather generating models at Technical Area 54 of Los Alamos National Lab. Technical Area 54 is contains several waste pits for low-level radioactive waste and is the site for subsurface flow and transport studies. This makes the comparison of the performance of the two weather generators at this site particularly valuable.
STRUCTURAL AND FUNCTIONAL MODEL OF FORMING INFORMATIONAL COMPETENCE OF TECHNICAL UNIVERSITY STUDENTS
Directory of Open Access Journals (Sweden)
Taras Ostapchuk
2016-11-01
Full Text Available The article elaborates and analyses the structural and functional model of formation of information competence of technical university students. The system and mutual relationships between its elements are revealed. It is found out that the presence of the target structure of the proposed model, process and result-evaluative blocks ensure its functioning and the opportunity to optimize the learning process for technical school students’ information training. It is established that the formation of technical university students’ information competence based on components such as motivational value, as well as operational activity, cognitive, and reflexive one. These criteria (motivation, operational and activity, cognitive, reflective, indexes and levels (reproductive, technologized, constructive forming technical university students’ information competence are disclosed. Expediency of complex organizational and educational conditions in the stages of information competence is justified. The complex organizational and pedagogical conditions include: orientation in the organization and implementation of class work for technical university students’ positive value treatment; the issue of forming professionalism; informatization of educational and socio-cultural environment of higher technical educational institutions; orientation of technical university students’ training to the demands of European and international standards on information competence as a factor in the formation of competitiveness at the labor market; introducing a special course curriculum that will provide competence formation due to the use of information technology in professional activities. Forms (lecture, visualization, problem lecture, combined lecture, scientific online conference, recitals, excursions, etc., tools (computer lab, multimedia projector, interactive whiteboard, multimedia technology (audio, video, the Internet technologies; social networks, etc
Time series models of environmental exposures: Good predictions or good understanding.
Barnett, Adrian G; Stephen, Dimity; Huang, Cunrui; Wolkewitz, Martin
2017-04-01
Time series data are popular in environmental epidemiology as they make use of the natural experiment of how changes in exposure over time might impact on disease. Many published time series papers have used parameter-heavy models that fully explained the second order patterns in disease to give residuals that have no short-term autocorrelation or seasonality. This is often achieved by including predictors of past disease counts (autoregression) or seasonal splines with many degrees of freedom. These approaches give great residuals, but add little to our understanding of cause and effect. We argue that modelling approaches should rely more on good epidemiology and less on statistical tests. This includes thinking about causal pathways, making potential confounders explicit, fitting a limited number of models, and not over-fitting at the cost of under-estimating the true association between exposure and disease. Copyright © 2017 Elsevier Inc. All rights reserved.
Statistical models and time series forecasting of sulfur dioxide: a case study Tehran.
Hassanzadeh, S; Hosseinibalam, F; Alizadeh, R
2009-08-01
This study performed a time-series analysis, frequency distribution and prediction of SO(2) levels for five stations (Pardisan, Vila, Azadi, Gholhak and Bahman) in Tehran for the period of 2000-2005. Most sites show a quite similar characteristic with highest pollution in autumn-winter time and least pollution in spring-summer. The frequency distributions show higher peaks at two residential sites. The potential for SO(2) problems is high because of high emissions and the close geographical proximity of the major industrial and urban centers. The ACF and PACF are nonzero for several lags, indicating a mixed (ARMA) model, then at Bahman station an ARMA model was used for forecasting SO(2). The partial autocorrelations become close to 0 after about 5 lags while the autocorrelations remain strong through all the lags shown. The results proved that ARMA (2,2) model can provides reliable, satisfactory predictions for time series.
He, Yuning
2015-01-01
Safety of unmanned aerial systems (UAS) is paramount, but the large number of dynamically changing controller parameters makes it hard to determine if the system is currently stable, and the time before loss of control if not. We propose a hierarchical statistical model using Treed Gaussian Processes to predict (i) whether a flight will be stable (success) or become unstable (failure), (ii) the time-to-failure if unstable, and (iii) time series outputs for flight variables. We first classify the current flight input into success or failure types, and then use separate models for each class to predict the time-to-failure and time series outputs. As different inputs may cause failures at different times, we have to model variable length output curves. We use a basis representation for curves and learn the mappings from input to basis coefficients. We demonstrate the effectiveness of our prediction methods on a NASA neuro-adaptive flight control system.
Li, Qiongge; Chan, Maria F
2017-01-01
Over half of cancer patients receive radiotherapy (RT) as partial or full cancer treatment. Daily quality assurance (QA) of RT in cancer treatment closely monitors the performance of the medical linear accelerator (Linac) and is critical for continuous improvement of patient safety and quality of care. Cumulative longitudinal QA measurements are valuable for understanding the behavior of the Linac and allow physicists to identify trends in the output and take preventive actions. In this study, artificial neural networks (ANNs) and autoregressive moving average (ARMA) time-series prediction modeling techniques were both applied to 5-year daily Linac QA data. Verification tests and other evaluations were then performed for all models. Preliminary results showed that ANN time-series predictive modeling has more advantages over ARMA techniques for accurate and effective applicability in the dosimetry and QA field. © 2016 New York Academy of Sciences.
Intuitionistic Fuzzy Time Series Forecasting Model Based on Intuitionistic Fuzzy Reasoning
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Ya’nan Wang
2016-01-01
Full Text Available Fuzzy sets theory cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. In this regard, an intuitionistic fuzzy time series forecasting model is built. In the new model, a fuzzy clustering algorithm is used to divide the universe of discourse into unequal intervals, and a more objective technique for ascertaining the membership function and nonmembership function of the intuitionistic fuzzy set is proposed. On these bases, forecast rules based on intuitionistic fuzzy approximate reasoning are established. At last, contrast experiments on the enrollments of the University of Alabama and the Taiwan Stock Exchange Capitalization Weighted Stock Index are carried out. The results show that the new model has a clear advantage of improving the forecast accuracy.
The Gaussian Graphical Model in Cross-Sectional and Time-Series Data.
Epskamp, Sacha; Waldorp, Lourens J; Mõttus, René; Borsboom, Denny
2018-04-16
We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in three kinds of psychological data sets: data sets in which consecutive cases are assumed independent (e.g., cross-sectional data), temporally ordered data sets (e.g., n = 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In time-series analysis, the GGM can be used to model the residual structure of a vector-autoregression analysis (VAR), also termed graphical VAR. Two network models can then be obtained: a temporal network and a contemporaneous network. When analyzing data from multiple subjects, a GGM can also be formed on the covariance structure of stationary means-the between-subjects network. We discuss the interpretation of these models and propose estimation methods to obtain these networks, which we implement in the R packages graphicalVAR and mlVAR. The methods are showcased in two empirical examples, and simulation studies on these methods are included in the supplementary materials.
Faes, Luca; Nollo, Giandomenico
2010-11-01
The Partial Directed Coherence (PDC) and its generalized formulation (gPDC) are popular tools for investigating, in the frequency domain, the concept of Granger causality among multivariate (MV) time series. PDC and gPDC are formalized in terms of the coefficients of an MV autoregressive (MVAR) model which describes only the lagged effects among the time series and forsakes instantaneous effects. However, instantaneous effects are known to affect linear parametric modeling, and are likely to occur in experimental time series. In this study, we investigate the impact on the assessment of frequency domain causality of excluding instantaneous effects from the model underlying PDC evaluation. Moreover, we propose the utilization of an extended MVAR model including both instantaneous and lagged effects. This model is used to assess PDC either in accordance with the definition of Granger causality when considering only lagged effects (iPDC), or with an extended form of causality, when we consider both instantaneous and lagged effects (ePDC). The approach is first evaluated on three theoretical examples of MVAR processes, which show that the presence of instantaneous correlations may produce misleading profiles of PDC and gPDC, while ePDC and iPDC derived from the extended model provide here a correct interpretation of extended and lagged causality. It is then applied to representative examples of cardiorespiratory and EEG MV time series. They suggest that ePDC and iPDC are better interpretable than PDC and gPDC in terms of the known cardiovascular and neural physiologies.
International Nuclear Information System (INIS)
J.D. Schreiber
2006-01-01
This technical work plan (TWP) describes work activities to be performed by the Near-Field Environment Team. The objective of the work scope covered by this TWP is to generate Revision 03 of EBS Radionuclide Transport Abstraction, referred to herein as the radionuclide transport abstraction (RTA) report. The RTA report is being revised primarily to address condition reports (CRs), to address issues identified by the Independent Validation Review Team (IVRT), to address the potential impact of transport, aging, and disposal (TAD) canister design on transport models, and to ensure integration with other models that are closely associated with the RTA report and being developed or revised in other analysis/model reports in response to IVRT comments. The RTA report will be developed in accordance with the most current version of LP-SIII.10Q-BSC and will reflect current administrative procedures (LP-3.15Q-BSC, ''Managing Technical Product Inputs''; LP-SIII.2Q-BSC, ''Qualification of Unqualified Data''; etc.), and will develop related Document Input Reference System (DIRS) reports and data qualifications as applicable in accordance with prevailing procedures. The RTA report consists of three models: the engineered barrier system (EBS) flow model, the EBS transport model, and the EBS-unsaturated zone (UZ) interface model. The flux-splitting submodel in the EBS flow model will change, so the EBS flow model will be validated again. The EBS transport model and validation of the model will be substantially revised in Revision 03 of the RTA report, which is the main subject of this TWP. The EBS-UZ interface model may be changed in Revision 03 of the RTA report due to changes in the conceptualization of the UZ transport abstraction model (a particle tracker transport model based on the discrete fracture transfer function will be used instead of the dual-continuum transport model previously used). Validation of the EBS-UZ interface model will be revised to be consistent with
A Bayesian Approach for Summarizing and Modeling Time-Series Exposure Data with Left Censoring.
Houseman, E Andres; Virji, M Abbas
2017-08-01
Direct reading instruments are valuable tools for measuring exposure as they provide real-time measurements for rapid decision making. However, their use is limited to general survey applications in part due to issues related to their performance. Moreover, statistical analysis of real-time data is complicated by autocorrelation among successive measurements, non-stationary time series, and the presence of left-censoring due to limit-of-detection (LOD). A Bayesian framework is proposed that accounts for non-stationary autocorrelation and LOD issues in exposure time-series data in order to model workplace factors that affect exposure and estimate summary statistics for tasks or other covariates of interest. A spline-based approach is used to model non-stationary autocorrelation with relatively few assumptions about autocorrelation structure. Left-censoring is addressed by integrating over the left tail of the distribution. The model is fit using Markov-Chain Monte Carlo within a Bayesian paradigm. The method can flexibly account for hierarchical relationships, random effects and fixed effects of covariates. The method is implemented using the rjags package in R, and is illustrated by applying it to real-time exposure data. Estimates for task means and covariates from the Bayesian model are compared to those from conventional frequentist models including linear regression, mixed-effects, and time-series models with different autocorrelation structures. Simulations studies are also conducted to evaluate method performance. Simulation studies with percent of measurements below the LOD ranging from 0 to 50% showed lowest root mean squared errors for task means and the least biased standard deviations from the Bayesian model compared to the frequentist models across all levels of LOD. In the application, task means from the Bayesian model were similar to means from the frequentist models, while the standard deviations were different. Parameter estimates for covariates
International Nuclear Information System (INIS)
Jafri, Y.Z.; Kamal, L.
2007-01-01
Various statistical techniques was used on five-year data from 1998-2002 of average humidity, rainfall, maximum and minimum temperatures, respectively. The relationships to regression analysis time series (RATS) were developed for determining the overall trend of these climate parameters on the basis of which forecast models can be corrected and modified. We computed the coefficient of determination as a measure of goodness of fit, to our polynomial regression analysis time series (PRATS). The correlation to multiple linear regression (MLR) and multiple linear regression analysis time series (MLRATS) were also developed for deciphering the interdependence of weather parameters. Spearman's rand correlation and Goldfeld-Quandt test were used to check the uniformity or non-uniformity of variances in our fit to polynomial regression (PR). The Breusch-Pagan test was applied to MLR and MLRATS, respectively which yielded homoscedasticity. We also employed Bartlett's test for homogeneity of variances on a five-year data of rainfall and humidity, respectively which showed that the variances in rainfall data were not homogenous while in case of humidity, were homogenous. Our results on regression and regression analysis time series show the best fit to prediction modeling on climatic data of Quetta, Pakistan. (author)
Loredo, Thomas; Budavari, Tamas; Scargle, Jeffrey D.
2018-01-01
This presentation provides an overview of open-source software packages addressing two challenging classes of astrostatistics problems. (1) CUDAHM is a C++ framework for hierarchical Bayesian modeling of cosmic populations, leveraging graphics processing units (GPUs) to enable applying this computationally challenging paradigm to large datasets. CUDAHM is motivated by measurement error problems in astronomy, where density estimation and linear and nonlinear regression must be addressed for populations of thousands to millions of objects whose features are measured with possibly complex uncertainties, potentially including selection effects. An example calculation demonstrates accurate GPU-accelerated luminosity function estimation for simulated populations of $10^6$ objects in about two hours using a single NVIDIA Tesla K40c GPU. (2) Time Series Explorer (TSE) is a collection of software in Python and MATLAB for exploratory analysis and statistical modeling of astronomical time series. It comprises a library of stand-alone functions and classes, as well as an application environment for interactive exploration of times series data. The presentation will summarize key capabilities of this emerging project, including new algorithms for analysis of irregularly-sampled time series.
Directory of Open Access Journals (Sweden)
Parneet Paul
2013-02-01
Full Text Available The computer modelling and simulation of wastewater treatment plant and their specific technologies, such as membrane bioreactors (MBRs, are becoming increasingly useful to consultant engineers when designing, upgrading, retrofitting, operating and controlling these plant. This research uses traditional phenomenological mechanistic models based on MBR filtration and biochemical processes to measure the effectiveness of alternative and novel time series models based upon input–output system identification methods. Both model types are calibrated and validated using similar plant layouts and data sets derived for this purpose. Results prove that although both approaches have their advantages, they also have specific disadvantages as well. In conclusion, the MBR plant designer and/or operator who wishes to use good quality, calibrated models to gain a better understanding of their process, should carefully consider which model type is selected based upon on what their initial modelling objectives are. Each situation usually proves unique.
Time series modeling for analysis and control advanced autopilot and monitoring systems
Ohtsu, Kohei; Kitagawa, Genshiro
2015-01-01
This book presents multivariate time series methods for the analysis and optimal control of feedback systems. Although ships’ autopilot systems are considered through the entire book, the methods set forth in this book can be applied to many other complicated, large, or noisy feedback control systems for which it is difficult to derive a model of the entire system based on theory in that subject area. The basic models used in this method are the multivariate autoregressive model with exogenous variables (ARX) model and the radial bases function net-type coefficients ARX model. The noise contribution analysis can then be performed through the estimated autoregressive (AR) model and various types of autopilot systems can be designed through the state–space representation of the models. The marine autopilot systems addressed in this book include optimal controllers for course-keeping motion, rolling reduction controllers with rudder motion, engine governor controllers, noise adaptive autopilots, route-tracki...
Watanabe, Hayafumi; Sano, Yukie; Takayasu, Hideki; Takayasu, Misako
2016-11-01
To elucidate the nontrivial empirical statistical properties of fluctuations of a typical nonsteady time series representing the appearance of words in blogs, we investigated approximately 3×10^{9} Japanese blog articles over a period of six years and analyze some corresponding mathematical models. First, we introduce a solvable nonsteady extension of the random diffusion model, which can be deduced by modeling the behavior of heterogeneous random bloggers. Next, we deduce theoretical expressions for both the temporal and ensemble fluctuation scalings of this model, and demonstrate that these expressions can reproduce all empirical scalings over eight orders of magnitude. Furthermore, we show that the model can reproduce other statistical properties of time series representing the appearance of words in blogs, such as functional forms of the probability density and correlations in the total number of blogs. As an application, we quantify the abnormality of special nationwide events by measuring the fluctuation scalings of 1771 basic adjectives.
Clark, Robert W.; Threeton, Mark D.; Ewing, John C.
2010-01-01
Since inception, career and technical education programs have embraced experiential learning as a true learning methodology for students to obtain occupational skills valued by employers. Programs have integrated classroom instruction with laboratory experiences to provide students a significant opportunity to learn. However, it is questionable as…
Evaluating an Automated Number Series Item Generator Using Linear Logistic Test Models
Directory of Open Access Journals (Sweden)
Bao Sheng Loe
2018-04-01
Full Text Available This study investigates the item properties of a newly developed Automatic Number Series Item Generator (ANSIG. The foundation of the ANSIG is based on five hypothesised cognitive operators. Thirteen item models were developed using the numGen R package and eleven were evaluated in this study. The 16-item ICAR (International Cognitive Ability Resource1 short form ability test was used to evaluate construct validity. The Rasch Model and two Linear Logistic Test Model(s (LLTM were employed to estimate and predict the item parameters. Results indicate that a single factor determines the performance on tests composed of items generated by the ANSIG. Under the LLTM approach, all the cognitive operators were significant predictors of item difficulty. Moderate to high correlations were evident between the number series items and the ICAR test scores, with high correlation found for the ICAR Letter-Numeric-Series type items, suggesting adequate nomothetic span. Extended cognitive research is, nevertheless, essential for the automatic generation of an item pool with predictable psychometric properties.
A time series model: First-order integer-valued autoregressive (INAR(1))
Simarmata, D. M.; Novkaniza, F.; Widyaningsih, Y.
2017-07-01
Nonnegative integer-valued time series arises in many applications. A time series model: first-order Integer-valued AutoRegressive (INAR(1)) is constructed by binomial thinning operator to model nonnegative integer-valued time series. INAR (1) depends on one period from the process before. The parameter of the model can be estimated by Conditional Least Squares (CLS). Specification of INAR(1) is following the specification of (AR(1)). Forecasting in INAR(1) uses median or Bayesian forecasting methodology. Median forecasting methodology obtains integer s, which is cumulative density function (CDF) until s, is more than or equal to 0.5. Bayesian forecasting methodology forecasts h-step-ahead of generating the parameter of the model and parameter of innovation term using Adaptive Rejection Metropolis Sampling within Gibbs sampling (ARMS), then finding the least integer s, where CDF until s is more than or equal to u . u is a value taken from the Uniform(0,1) distribution. INAR(1) is applied on pneumonia case in Penjaringan, Jakarta Utara, January 2008 until April 2016 monthly.
Model for the respiratory modulation of the heart beat-to-beat time interval series
Capurro, Alberto; Diambra, Luis; Malta, C. P.
2005-09-01
In this study we present a model for the respiratory modulation of the heart beat-to-beat interval series. The model consists of a set of differential equations used to simulate the membrane potential of a single rabbit sinoatrial node cell, excited with a periodic input signal with added correlated noise. This signal, which simulates the input from the autonomous nervous system to the sinoatrial node, was included in the pacemaker equations as a modulation of the iNaK current pump and the potassium current iK. We focus at modeling the heart beat-to-beat time interval series from normal subjects during meditation of the Kundalini Yoga and Chi techniques. The analysis of the experimental data indicates that while the embedding of pre-meditation and control cases have a roughly circular shape, it acquires a polygonal shape during meditation, triangular for the Kundalini Yoga data and quadrangular in the case of Chi data. The model was used to assess the waveshape of the respiratory signals needed to reproduce the trajectory of the experimental data in the phase space. The embedding of the Chi data could be reproduced using a periodic signal obtained by smoothing a square wave. In the case of Kundalini Yoga data, the embedding was reproduced with a periodic signal obtained by smoothing a triangular wave having a rising branch of longer duration than the decreasing branch. Our study provides an estimation of the respiratory signal using only the heart beat-to-beat time interval series.
Extracting Knowledge From Time Series An Introduction to Nonlinear Empirical Modeling
Bezruchko, Boris P
2010-01-01
This book addresses the fundamental question of how to construct mathematical models for the evolution of dynamical systems from experimentally-obtained time series. It places emphasis on chaotic signals and nonlinear modeling and discusses different approaches to the forecast of future system evolution. In particular, it teaches readers how to construct difference and differential model equations depending on the amount of a priori information that is available on the system in addition to the experimental data sets. This book will benefit graduate students and researchers from all natural sciences who seek a self-contained and thorough introduction to this subject.
The string prediction models as an invariants of time series in forex market
Richard Pincak; Marian Repasan
2011-01-01
In this paper we apply a new approach of the string theory to the real financial market. It is direct extension and application of the work [1] into prediction of prices. The models are constructed with an idea of prediction models based on the string invariants (PMBSI). The performance of PMBSI is compared to support vector machines (SVM) and artificial neural networks (ANN) on an artificial and a financial time series. Brief overview of the results and analysis is given. The first model is ...
MARKOWITZ' MODEL WITH FUNDAMENTAL AND TECHNICAL ANALYSIS – COMPLEMENTARY METHODS OR NOT
Directory of Open Access Journals (Sweden)
Branka Marasović
2011-02-01
Full Text Available As it is well known there are few “starting points” in portfolio optimization process, i.e. in the stock selection process. Famous Markowitz’ optimization model is unavoidable in this job. On the other side, someone may say that the indicators of the fundamental analysis must be the starting point. Beside that, the suggestions of the technical analysis must be taken into consideration. There are really numerous studies of the each approach separately, but it is almost impossible to find researches combining these approaches in logic and efficient unity. The main task of the paper is to find out if these approaches are complementary and if they are, how to apply them as efficient unit process. The empirical part of the study uses share sample from the Croatian stock market. Beside Markowitz’ MV model, fundamental and technical analysis, big role in the paper has an original multi-criterion approach.
Stifter, Cynthia A.; Rovine, Michael
2015-01-01
The focus of the present longitudinal study, to examine mother-infant interaction during the administration of immunizations at 2 and 6?months of age, used hidden Markov modelling, a time series approach that produces latent states to describe how mothers and infants work together to bring the infant to a soothed state. Results revealed a…
Directory of Open Access Journals (Sweden)
D.M. Kozachenko
2013-06-01
Full Text Available Purpose. The article aims to create a mathematical model of the railway station functioning for the solving of problems of station technology development on the plan-schedule basis. Methodology. The methods of graph theory and object-oriented analysis are used as research methods. The model of the station activity plan-schedule includes a model of technical equipment of the station (plan-schedule net and a model of the station functioning , which are formalized on the basis of parametric graphs. Findings. The presented model is implemented as an application to the graphics package AutoCAD. The software is developed in Visual LISP and Visual Basic. Taking into account that the construction of the plan-schedule is mostly a traditional process of adding, deleting, and modifying of icons, the developed interface is intuitively understandable for a technologist and practically does not require additional training. Originality. A mathematical model was created on the basis of the theory of graphs and object-oriented analysis in order to evaluate the technical and process of railway stations indicators; it is focused on solving problems of technology development of their work. Practical value. The proposed mathematical model is implemented as an application to the graphics package of AutoCAD. The presence of a mathematical model allows carrying out an automatic analysis of the plan-schedule and, thereby, reducing the period of its creation more than twice.
Wilson, Barry T.; Knight, Joseph F.; McRoberts, Ronald E.
2018-03-01
Imagery from the Landsat Program has been used frequently as a source of auxiliary data for modeling land cover, as well as a variety of attributes associated with tree cover. With ready access to all scenes in the archive since 2008 due to the USGS Landsat Data Policy, new approaches to deriving such auxiliary data from dense Landsat time series are required. Several methods have previously been developed for use with finer temporal resolution imagery (e.g. AVHRR and MODIS), including image compositing and harmonic regression using Fourier series. The manuscript presents a study, using Minnesota, USA during the years 2009-2013 as the study area and timeframe. The study examined the relative predictive power of land cover models, in particular those related to tree cover, using predictor variables based solely on composite imagery versus those using estimated harmonic regression coefficients. The study used two common non-parametric modeling approaches (i.e. k-nearest neighbors and random forests) for fitting classification and regression models of multiple attributes measured on USFS Forest Inventory and Analysis plots using all available Landsat imagery for the study area and timeframe. The estimated Fourier coefficients developed by harmonic regression of tasseled cap transformation time series data were shown to be correlated with land cover, including tree cover. Regression models using estimated Fourier coefficients as predictor variables showed a two- to threefold increase in explained variance for a small set of continuous response variables, relative to comparable models using monthly image composites. Similarly, the overall accuracies of classification models using the estimated Fourier coefficients were approximately 10-20 percentage points higher than the models using the image composites, with corresponding individual class accuracies between six and 45 percentage points higher.
Pedersen, Rune
2017-01-01
This is a project proposal derived from an urge to re-define the governance of ICT in healthcare towards regional and national standardization of the patient pathways. The focus is on a two-levelled approach for governing EPR systems where the clinicians' model structured variables and patient pathways. The overall goal is a patient centric EPR portfolio. This paper define and enlighten the need for establishing the socio- technical architect role necessary to obtain the capabilities of a modern structured EPR system. Clinicians are not capable to moderate between the technical and the clinical.
Hanzer, F.; Marke, T.; Steiger, R.; Strasser, U.
2012-04-01
Tourism and particularly winter tourism is a key factor for the Austrian economy. Judging from currently available climate simulations, the Austrian Alps show a particularly high vulnerability to climatic changes. To reduce the exposure of ski areas towards changes in natural snow conditions as well as to generally enhance snow conditions at skiing sites, technical snowmaking is widely utilized across Austrian ski areas. While such measures result in better snow conditions at the skiing sites and are important for the local skiing industry, its economic efficiency has also to be taken into account. The current work emerges from the project CC-Snow II, where improved future climate scenario simulations are used to determine future natural and artificial snow conditions and their effects on tourism and economy in the Austrian Alps. In a first step, a simple technical snowmaking approach is incorporated into the process based snow model AMUNDSEN, which operates at a spatial resolution of 10-50 m and a temporal resolution of 1-3 hours. Locations of skiing slopes within a ski area in Styria, Austria, were digitized and imported into the model environment. During a predefined time frame in the beginning of the ski season, the model produces a maximum possible amount of technical snow and distributes the associated snow on the slopes, whereas afterwards, until to the end of the ski season, the model tries to maintain a certain snow depth threshold value on the slopes. Due to only few required input parameters, this approach is easily transferable to other ski areas. In our poster contribution, we present first results of this snowmaking approach and give an overview of the data and methodology applied. In a further step in CC-Snow, this simple bulk approach will be extended to consider actual snow cannon locations and technical specifications, which will allow a more detailed description of technical snow production as well as cannon-based recordings of water and energy
International Nuclear Information System (INIS)
Janker, Karl Albert
2015-01-01
This thesis describes a model which generates renewable power generation time series as input data for energy system models. The focus is on photovoltaic systems and wind turbines. The basis is a high resolution global raster data set of weather data for many years. This data is validated, corrected and preprocessed. The composition of the hourly generation data is done via simulation of the respective technology. The generated time series are aggregated for different regions and are validated against historical production time series.
Modeling of plasma chemistry in a corona streamer pulse series in air
International Nuclear Information System (INIS)
Nowakowska, H.; Stanco, J.; Dors, M.; Mizeraczyk, J.
2002-01-01
The aim of this study is to analyse the chemistry in air treated by a series of corona discharge streamers. Attention is focused on the conversion of ozone and nitrogen oxides. In the model it is assumed that the streamer head of relatively small geometrical dimensions propagates from the anode to the cathode, leaving the streamer channel behind. Any elemental gas volume in the streamer path is subjected first to the conditions of the streamer head, and next to those of the streamer channel. The kinetics of plasma-chemical processes occurring in the gas is modeled numerically for a single streamer and a series of streamers. The temporal evolution of 25 chemical compounds initially present or produced in air is calculated. (author)
Directory of Open Access Journals (Sweden)
Sergey Viktorovich Kuznetsov
2017-01-01
Full Text Available Modern aircraft are equipped with complicated systems and complexes of avionics. Aircraft and its avionics tech- nical operation process is observed as a process with changing of operation states. Mathematical models of avionics pro- cesses and systems of technical operation are represented as Markov chains, Markov and semi-Markov processes. The pur- pose is to develop the graph-models of avionics technical operation processes, describing their work in flight, as well as during maintenance on the ground in the various systems of technical operation. The graph-models of processes and sys- tems of on-board complexes and functional avionics systems in flight are proposed. They are based on the state tables. The models are specified for the various technical operation systems: the system with control of the reliability level, the system with parameters control and the system with resource control. The events, which cause the avionics complexes and func- tional systems change their technical state, are failures and faults of built-in test equipment. Avionics system of technical operation with reliability level control is applicable for objects with constant or slowly varying in time failure rate. Avion- ics system of technical operation with resource control is mainly used for objects with increasing over time failure rate. Avionics system of technical operation with parameters control is used for objects with increasing over time failure rate and with generalized parameters, which can provide forecasting and assign the borders of before-fail technical states. The pro- posed formal graphical approach avionics complexes and systems models designing is the basis for models and complex systems and facilities construction, both for a single aircraft and for an airline aircraft fleet, or even for the entire aircraft fleet of some specific type. The ultimate graph-models for avionics in various systems of technical operation permit the beginning of
Time series modelling to forecast prehospital EMS demand for diabetic emergencies.
Villani, Melanie; Earnest, Arul; Nanayakkara, Natalie; Smith, Karen; de Courten, Barbora; Zoungas, Sophia
2017-05-05
Acute diabetic emergencies are often managed by prehospital Emergency Medical Services (EMS). The projected growth in prevalence of diabetes is likely to result in rising demand for prehospital EMS that are already under pressure. The aims of this study were to model the temporal trends and provide forecasts of prehospital attendances for diabetic emergencies. A time series analysis on monthly cases of hypoglycemia and hyperglycemia was conducted using data from the Ambulance Victoria (AV) electronic database between 2009 and 2015. Using the seasonal autoregressive integrated moving average (SARIMA) modelling process, different models were evaluated. The most parsimonious model with the highest accuracy was selected. Forty-one thousand four hundred fifty-four prehospital diabetic emergencies were attended over a seven-year period with an increase in the annual median monthly caseload between 2009 (484.5) and 2015 (549.5). Hypoglycemia (70%) and people with type 1 diabetes (48%) accounted for most attendances. The SARIMA (0,1,0,12) model provided the best fit, with a MAPE of 4.2% and predicts a monthly caseload of approximately 740 by the end of 2017. Prehospital EMS demand for diabetic emergencies is increasing. SARIMA time series models are a valuable tool to allow forecasting of future caseload with high accuracy and predict increasing cases of prehospital diabetic emergencies into the future. The model generated by this study may be used by service providers to allow appropriate planning and resource allocation of EMS for diabetic emergencies.
Prediction of traffic-related nitrogen oxides concentrations using Structural Time-Series models
Lawson, Anneka Ruth; Ghosh, Bidisha; Broderick, Brian
2011-09-01
Ambient air quality monitoring, modeling and compliance to the standards set by European Union (EU) directives and World Health Organization (WHO) guidelines are required to ensure the protection of human and environmental health. Congested urban areas are most susceptible to traffic-related air pollution which is the most problematic source of air pollution in Ireland. Long-term continuous real-time monitoring of ambient air quality at such urban centers is essential but often not realistic due to financial and operational constraints. Hence, the development of a resource-conservative ambient air quality monitoring technique is essential to ensure compliance with the threshold values set by the standards. As an intelligent and advanced statistical methodology, a Structural Time Series (STS) based approach has been introduced in this paper to develop a parsimonious and computationally simple air quality model. In STS methodology, the different components of a time-series dataset such as the trend, seasonal, cyclical and calendar variations can be modeled separately. To test the effectiveness of the proposed modeling strategy, average hourly concentrations of nitrogen dioxide and nitrogen oxides from a congested urban arterial in Dublin city center were modeled using STS methodology. The prediction error estimates from the developed air quality model indicate that the STS model can be a useful tool in predicting nitrogen dioxide and nitrogen oxides concentrations in urban areas and will be particularly useful in situations where the information on external variables such as meteorology or traffic volume is not available.
Applications of soft computing in time series forecasting simulation and modeling techniques
Singh, Pritpal
2016-01-01
This book reports on an in-depth study of fuzzy time series (FTS) modeling. It reviews and summarizes previous research work in FTS modeling and also provides a brief introduction to other soft-computing techniques, such as artificial neural networks (ANNs), rough sets (RS) and evolutionary computing (EC), focusing on how these techniques can be integrated into different phases of the FTS modeling approach. In particular, the book describes novel methods resulting from the hybridization of FTS modeling approaches with neural networks and particle swarm optimization. It also demonstrates how a new ANN-based model can be successfully applied in the context of predicting Indian summer monsoon rainfall. Thanks to its easy-to-read style and the clear explanations of the models, the book can be used as a concise yet comprehensive reference guide to fuzzy time series modeling, and will be valuable not only for graduate students, but also for researchers and professionals working for academic, business and governmen...
Time Series Modeling of Army Mission Command Communication Networks: An Event-Driven Analysis
2013-06-01
Lehmann, D. R. (1984). How advertising affects sales: Meta- analysis of econometric results. Journal of Marketing Research , 21, 65-74. Barabási, A. L...317-357. Leone, R. P. (1983). Modeling sales-advertising relationships: An integrated time series- econometric approach. Journal of Marketing ... Research , 20, 291-295. McGrath, J. E., & Kravitz, D. A. (1982). Group research. Annual Review of Psychology, 33, 195- 230. Monge, P. R., & Contractor
MODELING OF TECHNICAL CHANNELS OF INFORMATION LEAKAGE AT DISTRIBUTED CONTROL OBJECTS
Directory of Open Access Journals (Sweden)
Aleksander Vladimirovich Karpov
2018-05-01
Full Text Available The significant increase in requirements for distributed control objects’ functioning can’t be realized only at the expense of the widening and strengthening of security control measures. The first step in ensuring the information security at such objects is the analysis of the conditions of their functioning and modeling of technical channels of information leakage. The development of models of such channels is essentially the only method of complete study of their opportunities and it is pointed toward receiving quantitative assessments of the safe operation of compound objects. The evaluation data are necessary to make a decision on the degree of the information security from a leak according to the current criterion. The existing models are developed for the standard concentrated objects and allow to evaluate the level of information security from a leak on each of channels separately, what involves the significant increase in the required protective resource and time of assessment of information security on an object in general. The article deals with a logical-and-probabilistic method of a security assessment of structurally-compound objects. The model of a security leak on the distributed control objects is cited as an example. It is recommended to use a software package of an automated structurally-logistical modeling of compound systems, which allows to evaluate risk of information leakage in the loudspeaker. A possibility of information leakage by technical channels is evaluated and such differential characteristics of the safe operation of the distributed control objects as positive and negative contributions of the initiating events and conditions, which cause a leak are calculated. Purpose. The aim is a quantitative assessment of data risk, which is necessary for justifying the rational composition of organizational and technical protection measures, as well as a variant of the structure of the information security system from a
Decision support for life extension of technical systems through virtual age modelling
International Nuclear Information System (INIS)
Pérez Ramírez, Pedro A.; Utne, Ingrid Bouwer
2013-01-01
This article presents a virtual age model for decision support regarding life extension of ageing repairable systems. The aim of the model is to evaluate different life extension decision alternatives and their impact on the future performance of the system. The model can be applied to systems operated continuously (e.g., process systems) and systems operated on demand (e.g., safety systems). Deterioration and efficiency of imperfect maintenance is assessed when there is limited or no degradation data, and only failure and maintenance data is available. Systems that are in operation can be studied, meaning that the systems may be degraded. The current degradation is represented by a “current virtual age”, which is calculated from recorded maintenance data. The model parameters are estimated with the maximum likelihood method. A case study illustrates the application of the model for life extension of two fire water pumps in an oil and gas facility. The performance of the pump system is assessed with respect to number of failures, safety unavailability and costs during the life extension period. -- Highlights: ► Life extension assessment of technical systems using virtual age model is proposed. ► A virtual age model is generalised for systems in stand-by and continuous operation. ► The concept of current virtual age describes technical condition of the system. ► Different decision alternatives for life extension can be easily analysed. ► The decision process is improved even when only scarce failure data is available
Modeling multivariate time series on manifolds with skew radial basis functions.
Jamshidi, Arta A; Kirby, Michael J
2011-01-01
We present an approach for constructing nonlinear empirical mappings from high-dimensional domains to multivariate ranges. We employ radial basis functions and skew radial basis functions for constructing a model using data that are potentially scattered or sparse. The algorithm progresses iteratively, adding a new function at each step to refine the model. The placement of the functions is driven by a statistical hypothesis test that accounts for correlation in the multivariate range variables. The test is applied on training and validation data and reveals nonstatistical or geometric structure when it fails. At each step, the added function is fit to data contained in a spatiotemporally defined local region to determine the parameters--in particular, the scale of the local model. The scale of the function is determined by the zero crossings of the autocorrelation function of the residuals. The model parameters and the number of basis functions are determined automatically from the given data, and there is no need to initialize any ad hoc parameters save for the selection of the skew radial basis functions. Compactly supported skew radial basis functions are employed to improve model accuracy, order, and convergence properties. The extension of the algorithm to higher-dimensional ranges produces reduced-order models by exploiting the existence of correlation in the range variable data. Structure is tested not just in a single time series but between all pairs of time series. We illustrate the new methodologies using several illustrative problems, including modeling data on manifolds and the prediction of chaotic time series.
Time series modeling of live-cell shape dynamics for image-based phenotypic profiling.
Gordonov, Simon; Hwang, Mun Kyung; Wells, Alan; Gertler, Frank B; Lauffenburger, Douglas A; Bathe, Mark
2016-01-01
Live-cell imaging can be used to capture spatio-temporal aspects of cellular responses that are not accessible to fixed-cell imaging. As the use of live-cell imaging continues to increase, new computational procedures are needed to characterize and classify the temporal dynamics of individual cells. For this purpose, here we present the general experimental-computational framework SAPHIRE (Stochastic Annotation of Phenotypic Individual-cell Responses) to characterize phenotypic cellular responses from time series imaging datasets. Hidden Markov modeling is used to infer and annotate morphological state and state-switching properties from image-derived cell shape measurements. Time series modeling is performed on each cell individually, making the approach broadly useful for analyzing asynchronous cell populations. Two-color fluorescent cells simultaneously expressing actin and nuclear reporters enabled us to profile temporal changes in cell shape following pharmacological inhibition of cytoskeleton-regulatory signaling pathways. Results are compared with existing approaches conventionally applied to fixed-cell imaging datasets, and indicate that time series modeling captures heterogeneous dynamic cellular responses that can improve drug classification and offer additional important insight into mechanisms of drug action. The software is available at http://saphire-hcs.org.
a Landsat Time-Series Stacks Model for Detection of Cropland Change
Chen, J.; Chen, J.; Zhang, J.
2017-09-01
Global, timely, accurate and cost-effective cropland monitoring with a fine spatial resolution will dramatically improve our understanding of the effects of agriculture on greenhouse gases emissions, food safety, and human health. Time-series remote sensing imagery have been shown particularly potential to describe land cover dynamics. The traditional change detection techniques are often not capable of detecting land cover changes within time series that are severely influenced by seasonal difference, which are more likely to generate pseuso changes. Here,we introduced and tested LTSM ( Landsat time-series stacks model), an improved Continuous Change Detection and Classification (CCDC) proposed previously approach to extract spectral trajectories of land surface change using a dense Landsat time-series stacks (LTS). The method is expected to eliminate pseudo changes caused by phenology driven by seasonal patterns. The main idea of the method is that using all available Landsat 8 images within a year, LTSM consisting of two term harmonic function are estimated iteratively for each pixel in each spectral band .LTSM can defines change area by differencing the predicted and observed Landsat images. The LTSM approach was compared with change vector analysis (CVA) method. The results indicated that the LTSM method correctly detected the "true change" without overestimating the "false" one, while CVA pointed out "true change" pixels with a large number of "false changes". The detection of change areas achieved an overall accuracy of 92.37 %, with a kappa coefficient of 0.676.
2010-02-12
... replacing aging float level switch conduit assemblies, periodically inspecting the external dry bay system... Model 737-700 (IGW) Series Airplanes Equipped With Auxiliary Fuel Tanks Installed in Accordance With... airworthiness directive (AD) for certain Model 737-700 (IGW) series airplanes. This proposed AD would require...
Klein, A.A.B.; Melard, G.; Zahaf, T.
2000-01-01
The Fisher information matrix is of fundamental importance for the analysis of parameter estimation of time series models. In this paper the exact information matrix of a multivariate Gaussian time series model expressed in state space form is derived. A computationally efficient procedure is used
Big Data impacts on stochastic Forecast Models: Evidence from FX time series
Directory of Open Access Journals (Sweden)
Sebastian Dietz
2013-12-01
Full Text Available With the rise of the Big Data paradigm new tasks for prediction models appeared. In addition to the volume problem of such data sets nonlinearity becomes important, as the more detailed data sets contain also more comprehensive information, e.g. about non regular seasonal or cyclical movements as well as jumps in time series. This essay compares two nonlinear methods for predicting a high frequency time series, the USD/Euro exchange rate. The first method investigated is Autoregressive Neural Network Processes (ARNN, a neural network based nonlinear extension of classical autoregressive process models from time series analysis (see Dietz 2011. Its advantage is its simple but scalable time series process model architecture, which is able to include all kinds of nonlinearities based on the universal approximation theorem of Hornik, Stinchcombe and White 1989 and the extensions of Hornik 1993. However, restrictions related to the numeric estimation procedures limit the flexibility of the model. The alternative is a Support Vector Machine Model (SVM, Vapnik 1995. The two methods compared have different approaches of error minimization (Empirical error minimization at the ARNN vs. structural error minimization at the SVM. Our new finding is, that time series data classified as “Big Data” need new methods for prediction. Estimation and prediction was performed using the statistical programming language R. Besides prediction results we will also discuss the impact of Big Data on data preparation and model validation steps. Normal 0 21 false false false DE X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Normale Tabelle"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";}
Aircraft/Air Traffic Management Functional Analysis Model: Technical Description. 2.0
Etheridge, Melvin; Plugge, Joana; Retina, Nusrat
1998-01-01
The Aircraft/Air Traffic Management Functional Analysis Model, Version 2.0 (FAM 2.0), is a discrete event simulation model designed to support analysis of alternative concepts in air traffic management and control. FAM 2.0 was developed by the Logistics Management Institute (LMI) under a National Aeronautics and Space Administration (NASA) contract. This document provides a technical description of FAM 2.0 and its computer files to enable the modeler and programmer to make enhancements or modifications to the model. Those interested in a guide for using the model in analysis should consult the companion document, Aircraft/Air Traffic Management Functional Analysis Model, Version 2.0 Users Manual.
Mukhin, Dmitry; Gavrilov, Andrey; Loskutov, Evgeny; Feigin, Alexander
2016-04-01
We suggest a method for empirical forecast of climate dynamics basing on the reconstruction of reduced dynamical models in a form of random dynamical systems [1,2] derived from observational time series. The construction of proper embedding - the set of variables determining the phase space the model works in - is no doubt the most important step in such a modeling, but this task is non-trivial due to huge dimension of time series of typical climatic fields. Actually, an appropriate expansion of observational time series is needed yielding the number of principal components considered as phase variables, which are to be efficient for the construction of low-dimensional evolution operator. We emphasize two main features the reduced models should have for capturing the main dynamical properties of the system: (i) taking into account time-lagged teleconnections in the atmosphere-ocean system and (ii) reflecting the nonlinear nature of these teleconnections. In accordance to these principles, in this report we present the methodology which includes the combination of a new way for the construction of an embedding by the spatio-temporal data expansion and nonlinear model construction on the basis of artificial neural networks. The methodology is aplied to NCEP/NCAR reanalysis data including fields of sea level pressure, geopotential height, and wind speed, covering Northern Hemisphere. Its efficiency for the interannual forecast of various climate phenomena including ENSO, PDO, NAO and strong blocking event condition over the mid latitudes, is demonstrated. Also, we investigate the ability of the models to reproduce and predict the evolution of qualitative features of the dynamics, such as spectral peaks, critical transitions and statistics of extremes. This research was supported by the Government of the Russian Federation (Agreement No. 14.Z50.31.0033 with the Institute of Applied Physics RAS) [1] Y. I. Molkov, E. M. Loskutov, D. N. Mukhin, and A. M. Feigin, "Random
Fouilloux, V; Doguet, F; Kotsakis, A; Dubrowski, A; Berdah, S
2015-03-01
To develop a standardized simulation-based curriculum to teach medical knowledge and technical, communication and critical thinking skills necessary to initiate and wean from cardiopulmonary bypass (CPB) to junior cardiac trainees (CTs) in France. Performance on post-curricular tests was compared between CTs who participated in the new curriculum to those who did not. The simulation-based curriculum was developed by content and education experts. Simulations sequentially taught the skills necessary for initiating and weaning from CPB as well as managing crises by adding fidelity and complexity to scenarios. Nine CTs were randomly assigned to the new curriculum (n=5) or the traditional curriculum (n=4). Skills were assessed using tests of medical knowledge and technical, communication (GRS) and critical thinking (SCT) skills. A two-sample Wilcoxon rank-sum test compared average scores between the two groups. Alpha of 0.05 was set to indicate statistically significant differences. The resutls revealed that CTs in the new curriculum significantly outperformed CTs in the traditional curriculum on technical (18.2 vs 14.8, p=0.05) and communication (3.5 vs 2.2, p=0.013) skills. There was no significant difference between CTs in the new curriculum in the Script Concordance Test (16.5 vs 14.8, p=0.141) and knowledge tests (26.9 vs 24.6, p=0.14) compared to CTs in the traditional curriculum. Our new curriculum teaches communication and technical skills necessary for CPB. The results of this pilot study are encouraging and relevant. They give grounds for future research with a larger panel of trainees. Based on the current distribution of scores, a sample size of 12 CTs per group should yield significant results for all tests. © The Author(s) 2014.
A stochastic HMM-based forecasting model for fuzzy time series.
Li, Sheng-Tun; Cheng, Yi-Chung
2010-10-01
Recently, fuzzy time series have attracted more academic attention than traditional time series due to their capability of dealing with the uncertainty and vagueness inherent in the data collected. The formulation of fuzzy relations is one of the key issues affecting forecasting results. Most of the present works adopt IF-THEN rules for relationship representation, which leads to higher computational overhead and rule redundancy. Sullivan and Woodall proposed a Markov-based formulation and a forecasting model to reduce computational overhead; however, its applicability is limited to handling one-factor problems. In this paper, we propose a novel forecasting model based on the hidden Markov model by enhancing Sullivan and Woodall's work to allow handling of two-factor forecasting problems. Moreover, in order to make the nature of conjecture and randomness of forecasting more realistic, the Monte Carlo method is adopted to estimate the outcome. To test the effectiveness of the resulting stochastic model, we conduct two experiments and compare the results with those from other models. The first experiment consists of forecasting the daily average temperature and cloud density in Taipei, Taiwan, and the second experiment is based on the Taiwan Weighted Stock Index by forecasting the exchange rate of the New Taiwan dollar against the U.S. dollar. In addition to improving forecasting accuracy, the proposed model adheres to the central limit theorem, and thus, the result statistically approximates to the real mean of the target value being forecast.
A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method.
Yang, Jun-He; Cheng, Ching-Hsue; Chan, Chia-Pan
2017-01-01
Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir's water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir's water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.
A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method
Directory of Open Access Journals (Sweden)
Jun-He Yang
2017-01-01
Full Text Available Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir’s water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir’s water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.
Zhang, Yingying; Wang, Juncheng; Vorontsov, A M; Hou, Guangli; Nikanorova, M N; Wang, Hongliang
2014-01-01
The international marine ecological safety monitoring demonstration station in the Yellow Sea was developed as a collaborative project between China and Russia. It is a nonprofit technical workstation designed as a facility for marine scientific research for public welfare. By undertaking long-term monitoring of the marine environment and automatic data collection, this station will provide valuable information for marine ecological protection and disaster prevention and reduction. The results of some initial research by scientists at the research station into predictive modeling of marine ecological environments and early warning are described in this paper. Marine ecological processes are influenced by many factors including hydrological and meteorological conditions, biological factors, and human activities. Consequently, it is very difficult to incorporate all these influences and their interactions in a deterministic or analysis model. A prediction model integrating a time series prediction approach with neural network nonlinear modeling is proposed for marine ecological parameters. The model explores the natural fluctuations in marine ecological parameters by learning from the latest observed data automatically, and then predicting future values of the parameter. The model is updated in a "rolling" fashion with new observed data from the monitoring station. Prediction experiments results showed that the neural network prediction model based on time series data is effective for marine ecological prediction and can be used for the development of early warning systems.
Directory of Open Access Journals (Sweden)
Mansoor Ahmed Siddiqui
2017-06-01
Full Text Available This research work is aimed at optimizing the availability of a framework comprising of two units linked together in series configuration utilizing Markov Model and Monte Carlo (MC Simulation techniques. In this article, effort has been made to develop a maintenance model that incorporates three distinct states for each unit, while taking into account their different levels of deterioration. Calculations are carried out using the proposed model for two distinct cases of corrective repair, namely perfect and imperfect repairs, with as well as without opportunistic maintenance. Initially, results are accomplished using an analytical technique i.e., Markov Model. Validation of the results achieved is later carried out with the help of MC Simulation. In addition, MC Simulation based codes also work well for the frameworks that follow non-exponential failure and repair rates, and thus overcome the limitations offered by the Markov Model.
Zhang, Yong; Zhong, Miner; Geng, Nana; Jiang, Yunjian
2017-01-01
The market demand for electric vehicles (EVs) has increased in recent years. Suitable models are necessary to understand and forecast EV sales. This study presents a singular spectrum analysis (SSA) as a univariate time-series model and vector autoregressive model (VAR) as a multivariate model. Empirical results suggest that SSA satisfactorily indicates the evolving trend and provides reasonable results. The VAR model, which comprised exogenous parameters related to the market on a monthly basis, can significantly improve the prediction accuracy. The EV sales in China, which are categorized into battery and plug-in EVs, are predicted in both short term (up to December 2017) and long term (up to 2020), as statistical proofs of the growth of the Chinese EV industry.
Estimating and Analyzing Savannah Phenology with a Lagged Time Series Model
DEFF Research Database (Denmark)
Boke-Olen, Niklas; Lehsten, Veiko; Ardo, Jonas
2016-01-01
cycle due to their areal coverage and can have an effect on the food security in regions that depend on subsistence farming. In this study we investigate how soil moisture, mean annual precipitation, and day length control savannah phenology by developing a lagged time series model. The model uses...... climate data for 15 flux tower sites across four continents, and normalized difference vegetation index from satellite to optimize a statistical phenological model. We show that all three variables can be used to estimate savannah phenology on a global scale. However, it was not possible to create...... a simplified savannah model that works equally well for all sites on the global scale without inclusion of more site specific parameters. The simplified model showed no bias towards tree cover or between continents and resulted in a cross-validated r2 of 0.6 and root mean squared error of 0.1. We therefore...
The partial duration series method in regional index-flood modeling
DEFF Research Database (Denmark)
Madsen, Henrik; Rosbjerg, Dan
1997-01-01
A regional index-flood method based on the partial duration series model is introduced. The model comprises the assumptions of a Poisson-distributed number of threshold exceedances and generalized Pareto (GP) distributed peak magnitudes. The regional T-year event estimator is based on a regional...... estimator is superior to the at-site estimator even in extremely heterogenous regions, the performance of the regional estimator being relatively better in regions with a negative shape parameter. When the record length increases, the relative performance of the regional estimator decreases, but it is still...
DEFF Research Database (Denmark)
Madsen, Henrik; Rosbjerg, Dan
1997-01-01
parameters is inferred from regional data using generalized least squares (GLS) regression. Two different Bayesian T-year event estimators are introduced: a linear estimator that requires only some moments of the prior distributions to be specified and a parametric estimator that is based on specified......A regional estimation procedure that combines the index-flood concept with an empirical Bayes method for inferring regional information is introduced. The model is based on the partial duration series approach with generalized Pareto (GP) distributed exceedances. The prior information of the model...
Sensor response monitoring in pressurized water reactors using time series modeling
International Nuclear Information System (INIS)
Upadhyaya, B.R.; Kerlin, T.W.
1978-01-01
Random data analysis in nuclear power reactors for purposes of process surveillance, pattern recognition and monitoring of temperature, pressure, flow and neutron sensors has gained increasing attention in view of their potential for helping to ensure safe plant operation. In this study, application of autoregressive moving-average (ARMA) time series modeling for monitoring temperature sensor response characteristrics is presented. The ARMA model is used to estimate the step and ramp response of the sensors and the related time constant and ramp delay time. The ARMA parameters are estimated by a two-stage algorithm in the spectral domain. Results of sensor testing for an operating pressurized water reactor are presented. 16 refs
Nguyen, Hien D; Ullmann, Jeremy F P; McLachlan, Geoffrey J; Voleti, Venkatakaushik; Li, Wenze; Hillman, Elizabeth M C; Reutens, David C; Janke, Andrew L
2018-02-01
Calcium is a ubiquitous messenger in neural signaling events. An increasing number of techniques are enabling visualization of neurological activity in animal models via luminescent proteins that bind to calcium ions. These techniques generate large volumes of spatially correlated time series. A model-based functional data analysis methodology via Gaussian mixtures is suggested for the clustering of data from such visualizations is proposed. The methodology is theoretically justified and a computationally efficient approach to estimation is suggested. An example analysis of a zebrafish imaging experiment is presented.
Time Series Model of Wind Speed for Multi Wind Turbines based on Mixed Copula
Directory of Open Access Journals (Sweden)
Nie Dan
2016-01-01
Full Text Available Because wind power is intermittent, random and so on, large scale grid will directly affect the safe and stable operation of power grid. In order to make a quantitative study on the characteristics of the wind speed of wind turbine, the wind speed time series model of the multi wind turbine generator is constructed by using the mixed Copula-ARMA function in this paper, and a numerical example is also given. The research results show that the model can effectively predict the wind speed, ensure the efficient operation of the wind turbine, and provide theoretical basis for the stability of wind power grid connected operation.
Directory of Open Access Journals (Sweden)
Jia Chaolong
2013-01-01
Full Text Available Good track geometry state ensures the safe operation of the railway passenger service and freight service. Railway transportation plays an important role in the Chinese economic and social development. This paper studies track irregularity standard deviation time series data and focuses on the characteristics and trend changes of track state by applying clustering analysis. Linear recursive model and linear-ARMA model based on wavelet decomposition reconstruction are proposed, and all they offer supports for the safe management of railway transportation.
International Nuclear Information System (INIS)
Faura, F.
1997-01-01
In the present paper a technical-economic model for an aluminium high pressure die casting system has been developed. In order to obtain the necessary data for correlations utilized by the model, has been analyzed the production systems of companies that use these processes. This has allowed to determine the most important technological variables that affect to the economical aspect of the process. A computer application has been developed which allows to explore easily the influence of different system parameters. (Author) 12 refs
A dynamic model to explain hydration behaviour along the lanthanide series
International Nuclear Information System (INIS)
Duvail, M.; Spezia, R.; Vitorge, P.
2008-01-01
An understanding of the hydration structure of heavy atoms, such as transition metals, lanthanides and actinides, in aqueous solution is of fundamental importance in order to address their solvation properties and chemical reactivity. Herein we present a systematic molecular dynamics study of Ln 3+ hydration in bulk water that can be used as reference for experimental and theoretical research in this and related fields. Our study of hydration structure and dynamics along the entire Ln 3+ series provides a dynamic picture of the CN behavioural change from light (CN=9 predominating) to heavy (CN=8 predominating) lanthanides consistent with the exchange mechanism proposed by Helm, Merbach and co-workers. This scenario is summarized in this work. The hydrated light lanthanides are stable TTP structures containing two kinds of water molecules: six molecules forming the trigonal prism and three in the centre triangle. Towards the middle of the series both ionic radii and polarizabilities decrease, such that first-shell water-water repulsion increases and water-cation attraction decreases. This mainly applies for molecules of the centre triangle of the nine-fold structure. Thus, one of these molecules stay in the second hydration sphere of the lanthanide for longer average times, as one progresses along the lanthanide series. The interchange between predominantly CN=9 and CN=8 is found between Tb and Dy. Therefore, we propose a model that determines the properties governing the change in the first-shell coordination number across the series, confirming the basic hypothesis proposed by Helm and Merbach. We show that it is not a sudden change in behaviour, but rather that it results from a statistical predominance of one first hydration shell structure containing nine water molecules over one containing eight. This is observed progressively across the series. (O.M.)
A new Markov-chain-related statistical approach for modelling synthetic wind power time series
International Nuclear Information System (INIS)
Pesch, T; Hake, J F; Schröders, S; Allelein, H J
2015-01-01
The integration of rising shares of volatile wind power in the generation mix is a major challenge for the future energy system. To address the uncertainties involved in wind power generation, models analysing and simulating the stochastic nature of this energy source are becoming increasingly important. One statistical approach that has been frequently used in the literature is the Markov chain approach. Recently, the method was identified as being of limited use for generating wind time series with time steps shorter than 15–40 min as it is not capable of reproducing the autocorrelation characteristics accurately. This paper presents a new Markov-chain-related statistical approach that is capable of solving this problem by introducing a variable second lag. Furthermore, additional features are presented that allow for the further adjustment of the generated synthetic time series. The influences of the model parameter settings are examined by meaningful parameter variations. The suitability of the approach is demonstrated by an application analysis with the example of the wind feed-in in Germany. It shows that—in contrast to conventional Markov chain approaches—the generated synthetic time series do not systematically underestimate the required storage capacity to balance wind power fluctuation. (paper)
Bernaola-Galván, Pedro A.; Gómez-Extremera, Manuel; Romance, A. Ramón; Carpena, Pedro
2017-09-01
The correlation properties of the magnitude of a time series are associated with nonlinear and multifractal properties and have been applied in a great variety of fields. Here we have obtained the analytical expression of the autocorrelation of the magnitude series (C|x |) of a linear Gaussian noise as a function of its autocorrelation (Cx). For both, models and natural signals, the deviation of C|x | from its expectation in linear Gaussian noises can be used as an index of nonlinearity that can be applied to relatively short records and does not require the presence of scaling in the time series under study. In a model of artificial Gaussian multifractal signal we use this approach to analyze the relation between nonlinearity and multifractallity and show that the former implies the latter but the reverse is not true. We also apply this approach to analyze experimental data: heart-beat records during rest and moderate exercise. For each individual subject, we observe higher nonlinearities during rest. This behavior is also achieved on average for the analyzed set of 10 semiprofessional soccer players. This result agrees with the fact that other measures of complexity are dramatically reduced during exercise and can shed light on its relationship with the withdrawal of parasympathetic tone and/or the activation of sympathetic activity during physical activity.
2010-08-05
... Airworthiness Directives; McDonnell Douglas Corporation Model DC- 9-10 Series Airplanes, DC-9-30 Series... existing airworthiness directive (AD), which applies to all McDonnell Douglas Model DC-9-10 series..., 2010). That AD applies to all McDonnell Douglas Corporation Model DC-9-10 series airplanes, DC-9-30...
Modeling associations between latent event processes governing time series of pulsing hormones.
Liu, Huayu; Carlson, Nichole E; Grunwald, Gary K; Polotsky, Alex J
2017-10-31
This work is motivated by a desire to quantify relationships between two time series of pulsing hormone concentrations. The locations of pulses are not directly observed and may be considered latent event processes. The latent event processes of pulsing hormones are often associated. It is this joint relationship we model. Current approaches to jointly modeling pulsing hormone data generally assume that a pulse in one hormone is coupled with a pulse in another hormone (one-to-one association). However, pulse coupling is often imperfect. Existing joint models are not flexible enough for imperfect systems. In this article, we develop a more flexible class of pulse association models that incorporate parameters quantifying imperfect pulse associations. We propose a novel use of the Cox process model as a model of how pulse events co-occur in time. We embed the Cox process model into a hormone concentration model. Hormone concentration is the observed data. Spatial birth and death Markov chain Monte Carlo is used for estimation. Simulations show the joint model works well for quantifying both perfect and imperfect associations and offers estimation improvements over single hormone analyses. We apply this model to luteinizing hormone (LH) and follicle stimulating hormone (FSH), two reproductive hormones. Use of our joint model results in an ability to investigate novel hypotheses regarding associations between LH and FSH secretion in obese and non-obese women. © 2017, The International Biometric Society.
Time series segmentation: a new approach based on Genetic Algorithm and Hidden Markov Model
Toreti, A.; Kuglitsch, F. G.; Xoplaki, E.; Luterbacher, J.
2009-04-01
The subdivision of a time series into homogeneous segments has been performed using various methods applied to different disciplines. In climatology, for example, it is accompanied by the well-known homogenization problem and the detection of artificial change points. In this context, we present a new method (GAMM) based on Hidden Markov Model (HMM) and Genetic Algorithm (GA), applicable to series of independent observations (and easily adaptable to autoregressive processes). A left-to-right hidden Markov model, estimating the parameters and the best-state sequence, respectively, with the Baum-Welch and Viterbi algorithms, was applied. In order to avoid the well-known dependence of the Baum-Welch algorithm on the initial condition, a Genetic Algorithm was developed. This algorithm is characterized by mutation, elitism and a crossover procedure implemented with some restrictive rules. Moreover the function to be minimized was derived following the approach of Kehagias (2004), i.e. it is the so-called complete log-likelihood. The number of states was determined applying a two-fold cross-validation procedure (Celeux and Durand, 2008). Being aware that the last issue is complex, and it influences all the analysis, a Multi Response Permutation Procedure (MRPP; Mielke et al., 1981) was inserted. It tests the model with K+1 states (where K is the state number of the best model) if its likelihood is close to K-state model. Finally, an evaluation of the GAMM performances, applied as a break detection method in the field of climate time series homogenization, is shown. 1. G. Celeux and J.B. Durand, Comput Stat 2008. 2. A. Kehagias, Stoch Envir Res 2004. 3. P.W. Mielke, K.J. Berry, G.W. Brier, Monthly Wea Rev 1981.
Patient specific dynamic geometric models from sequential volumetric time series image data.
Cameron, B M; Robb, R A
2004-01-01
Generating patient specific dynamic models is complicated by the complexity of the motion intrinsic and extrinsic to the anatomic structures being modeled. Using a physics-based sequentially deforming algorithm, an anatomically accurate dynamic four-dimensional model can be created from a sequence of 3-D volumetric time series data sets. While such algorithms may accurately track the cyclic non-linear motion of the heart, they generally fail to accurately track extrinsic structural and non-cyclic motion. To accurately model these motions, we have modified a physics-based deformation algorithm to use a meta-surface defining the temporal and spatial maxima of the anatomic structure as the base reference surface. A mass-spring physics-based deformable model, which can expand or shrink with the local intrinsic motion, is applied to the metasurface, deforming this base reference surface to the volumetric data at each time point. As the meta-surface encompasses the temporal maxima of the structure, any extrinsic motion is inherently encoded into the base reference surface and allows the computation of the time point surfaces to be performed in parallel. The resultant 4-D model can be interactively transformed and viewed from different angles, showing the spatial and temporal motion of the anatomic structure. Using texture maps and per-vertex coloring, additional data such as physiological and/or biomechanical variables (e.g., mapping electrical activation sequences onto contracting myocardial surfaces) can be associated with the dynamic model, producing a 5-D model. For acquisition systems that may capture only limited time series data (e.g., only images at end-diastole/end-systole or inhalation/exhalation), this algorithm can provide useful interpolated surfaces between the time points. Such models help minimize the number of time points required to usefully depict the motion of anatomic structures for quantitative assessment of regional dynamics.
Cheng, C. M.; Peng, Z. K.; Zhang, W. M.; Meng, G.
2017-03-01
Nonlinear problems have drawn great interest and extensive attention from engineers, physicists and mathematicians and many other scientists because most real systems are inherently nonlinear in nature. To model and analyze nonlinear systems, many mathematical theories and methods have been developed, including Volterra series. In this paper, the basic definition of the Volterra series is recapitulated, together with some frequency domain concepts which are derived from the Volterra series, including the general frequency response function (GFRF), the nonlinear output frequency response function (NOFRF), output frequency response function (OFRF) and associated frequency response function (AFRF). The relationship between the Volterra series and other nonlinear system models and nonlinear problem solving methods are discussed, including the Taylor series, Wiener series, NARMAX model, Hammerstein model, Wiener model, Wiener-Hammerstein model, harmonic balance method, perturbation method and Adomian decomposition. The challenging problems and their state of arts in the series convergence study and the kernel identification study are comprehensively introduced. In addition, a detailed review is then given on the applications of Volterra series in mechanical engineering, aeroelasticity problem, control engineering, electronic and electrical engineering.
SITE-94. The CRYSTAL Geosphere Transport Model: Technical documentation version 2.1
International Nuclear Information System (INIS)
Worgan, K.; Robinson, P.
1995-12-01
CRYSTAL, a one-dimensional contaminant transport model of a densely fissured geosphere, was originally developed for the SKI Project-90 performance assessment program. It has since been extended to include matrix blocks of alternative basic geometries. CRYSTAL predicts the transport of arbitrary-length decay chains by advection, diffusion and surface sorption in the fissures and diffusion into the rock matrix blocks. The model equations are solved in Laplace transform space, and inverted numerically to the time domain. This approach avoids time-stepping and consequently is numerically very efficient. The source term for crystal may be supplied internally using either simple leaching or band release submodels or by input of a general time-series output from a near-field model. The time series input is interfaced with the geosphere model using the method of convolution. The response of the geosphere to delta-function inputs from each nuclide is combined with the time series outputs from the near-field, to obtain the nuclide flux emerging from the far-field. 14 refs
Analysis of hohlraum energetics of the SG series and the NIF experiments with energy balance model
Directory of Open Access Journals (Sweden)
Guoli Ren
2017-01-01
Full Text Available The basic energy balance model is applied to analyze the hohlraum energetics data from the Shenguang (SG series laser facilities and the National Ignition Facility (NIF experiments published in the past few years. The analysis shows that the overall hohlraum energetics data are in agreement with the energy balance model within 20% deviation. The 20% deviation might be caused by the diversity in hohlraum parameters, such as material, laser pulse, gas filling density, etc. In addition, the NIF's ignition target designs and our ignition target designs given by simulations are also in accordance with the energy balance model. This work confirms the value of the energy balance model for ignition target design and experimental data assessment, and demonstrates that the NIF energy is enough to achieve ignition if a 1D spherical radiation drive could be created, meanwhile both the laser plasma instabilities and hydrodynamic instabilities could be suppressed.
Scargle, Jeffrey D.
1990-01-01
While chaos arises only in nonlinear systems, standard linear time series models are nevertheless useful for analyzing data from chaotic processes. This paper introduces such a model, the chaotic moving average. This time-domain model is based on the theorem that any chaotic process can be represented as the convolution of a linear filter with an uncorrelated process called the chaotic innovation. A technique, minimum phase-volume deconvolution, is introduced to estimate the filter and innovation. The algorithm measures the quality of a model using the volume covered by the phase-portrait of the innovation process. Experiments on synthetic data demonstrate that the algorithm accurately recovers the parameters of simple chaotic processes. Though tailored for chaos, the algorithm can detect both chaos and randomness, distinguish them from each other, and separate them if both are present. It can also recover nonminimum-delay pulse shapes in non-Gaussian processes, both random and chaotic.
Studies in astronomical time series analysis: Modeling random processes in the time domain
Scargle, J. D.
1979-01-01
Random process models phased in the time domain are used to analyze astrophysical time series data produced by random processes. A moving average (MA) model represents the data as a sequence of pulses occurring randomly in time, with random amplitudes. An autoregressive (AR) model represents the correlations in the process in terms of a linear function of past values. The best AR model is determined from sampled data and transformed to an MA for interpretation. The randomness of the pulse amplitudes is maximized by a FORTRAN algorithm which is relatively stable numerically. Results of test cases are given to study the effects of adding noise and of different distributions for the pulse amplitudes. A preliminary analysis of the optical light curve of the quasar 3C 273 is given.
Weber, Juliane; Zachow, Christopher; Witthaut, Dirk
2018-03-01
Wind power generation exhibits a strong temporal variability, which is crucial for system integration in highly renewable power systems. Different methods exist to simulate wind power generation but they often cannot represent the crucial temporal fluctuations properly. We apply the concept of additive binary Markov chains to model a wind generation time series consisting of two states: periods of high and low wind generation. The only input parameter for this model is the empirical autocorrelation function. The two-state model is readily extended to stochastically reproduce the actual generation per period. To evaluate the additive binary Markov chain method, we introduce a coarse model of the electric power system to derive backup and storage needs. We find that the temporal correlations of wind power generation, the backup need as a function of the storage capacity, and the resting time distribution of high and low wind events for different shares of wind generation can be reconstructed.
Weber, Juliane; Zachow, Christopher; Witthaut, Dirk
2018-03-01
Wind power generation exhibits a strong temporal variability, which is crucial for system integration in highly renewable power systems. Different methods exist to simulate wind power generation but they often cannot represent the crucial temporal fluctuations properly. We apply the concept of additive binary Markov chains to model a wind generation time series consisting of two states: periods of high and low wind generation. The only input parameter for this model is the empirical autocorrelation function. The two-state model is readily extended to stochastically reproduce the actual generation per period. To evaluate the additive binary Markov chain method, we introduce a coarse model of the electric power system to derive backup and storage needs. We find that the temporal correlations of wind power generation, the backup need as a function of the storage capacity, and the resting time distribution of high and low wind events for different shares of wind generation can be reconstructed.
Forecast models for suicide: Time-series analysis with data from Italy.
Preti, Antonio; Lentini, Gianluca
2016-01-01
The prediction of suicidal behavior is a complex task. To fine-tune targeted preventative interventions, predictive analytics (i.e. forecasting future risk of suicide) is more important than exploratory data analysis (pattern recognition, e.g. detection of seasonality in suicide time series). This study sets out to investigate the accuracy of forecasting models of suicide for men and women. A total of 101 499 male suicides and of 39 681 female suicides - occurred in Italy from 1969 to 2003 - were investigated. In order to apply the forecasting model and test its accuracy, the time series were split into a training set (1969 to 1996; 336 months) and a test set (1997 to 2003; 84 months). The main outcome was the accuracy of forecasting models on the monthly number of suicides. These measures of accuracy were used: mean absolute error; root mean squared error; mean absolute percentage error; mean absolute scaled error. In both male and female suicides a change in the trend pattern was observed, with an increase from 1969 onwards to reach a maximum around 1990 and decrease thereafter. The variances attributable to the seasonal and trend components were, respectively, 24% and 64% in male suicides, and 28% and 41% in female ones. Both annual and seasonal historical trends of monthly data contributed to forecast future trends of suicide with a margin of error around 10%. The finding is clearer in male than in female time series of suicide. The main conclusion of the study is that models taking seasonality into account seem to be able to derive information on deviation from the mean when this occurs as a zenith, but they fail to reproduce it when it occurs as a nadir. Preventative efforts should concentrate on the factors that influence the occurrence of increases above the main trend in both seasonal and cyclic patterns of suicides.
Hensman, James; Lawrence, Neil D; Rattray, Magnus
2013-08-20
Time course data from microarrays and high-throughput sequencing experiments require simple, computationally efficient and powerful statistical models to extract meaningful biological signal, and for tasks such as data fusion and clustering. Existing methodologies fail to capture either the temporal or replicated nature of the experiments, and often impose constraints on the data collection process, such as regularly spaced samples, or similar sampling schema across replications. We propose hierarchical Gaussian processes as a general model of gene expression time-series, with application to a variety of problems. In particular, we illustrate the method's capacity for missing data imputation, data fusion and clustering.The method can impute data which is missing both systematically and at random: in a hold-out test on real data, performance is significantly better than commonly used imputation methods. The method's ability to model inter- and intra-cluster variance leads to more biologically meaningful clusters. The approach removes the necessity for evenly spaced samples, an advantage illustrated on a developmental Drosophila dataset with irregular replications. The hierarchical Gaussian process model provides an excellent statistical basis for several gene-expression time-series tasks. It has only a few additional parameters over a regular GP, has negligible additional complexity, is easily implemented and can be integrated into several existing algorithms. Our experiments were implemented in python, and are available from the authors' website: http://staffwww.dcs.shef.ac.uk/people/J.Hensman/.
International Nuclear Information System (INIS)
Lefieux, V.
2007-10-01
Reseau de Transport d'Electricite (RTE), in charge of operating the French electric transportation grid, needs an accurate forecast of the power consumption in order to operate it correctly. The forecasts used everyday result from a model combining a nonlinear parametric regression and a SARIMA model. In order to obtain an adaptive forecasting model, nonparametric forecasting methods have already been tested without real success. In particular, it is known that a nonparametric predictor behaves badly with a great number of explanatory variables, what is commonly called the curse of dimensionality. Recently, semi parametric methods which improve the pure nonparametric approach have been proposed to estimate a regression function. Based on the concept of 'dimension reduction', one those methods (called MAVE : Moving Average -conditional- Variance Estimate) can apply to time series. We study empirically its effectiveness to predict the future values of an autoregressive time series. We then adapt this method, from a practical point of view, to forecast power consumption. We propose a partially linear semi parametric model, based on the MAVE method, which allows to take into account simultaneously the autoregressive aspect of the problem and the exogenous variables. The proposed estimation procedure is practically efficient. (author)
An expanded system simulation model for solar energy storage (technical report), volume 1
Warren, A. W.
1979-01-01
The simulation model for wind energy storage (SIMWEST) program now includes wind and/or photovoltaic systems utilizing any combination of five types of storage (pumped hydro, battery, thermal, flywheel and pneumatic) and is available for the UNIVAC 1100 series and the CDC 6000 series computers. The level of detail is consistent with a role of evaluating the economic feasibility as well as the general performance of wind and/or photovoltaic energy systems. The software package consists of two basic programs and a library of system, environmental, and load components. The first program is a precompiler which generates computer models (in FORTRAN) of complex wind and/or photovoltaic source/storage/application systems, from user specifications using the respective library components. The second program provides the techno-economic system analysis with the respective I/0, the integration of system dynamics, and the iteration for conveyance of variables.
2010-05-21
... Airworthiness Directives; Airbus Model A300 Series Airplanes; Model A300 B4-600, B4-600R, F4-600R Series..., B4-622, B4- 605R, B4-622R, F4-605R, F4-622R, and C4-605R Variant F airplanes; and Model A310-203...
Energy Technology Data Exchange (ETDEWEB)
J.D. Schreiber
2006-12-08
This technical work plan (TWP) describes work activities to be performed by the Near-Field Environment Team. The objective of the work scope covered by this TWP is to generate Revision 03 of EBS Radionuclide Transport Abstraction, referred to herein as the radionuclide transport abstraction (RTA) report. The RTA report is being revised primarily to address condition reports (CRs), to address issues identified by the Independent Validation Review Team (IVRT), to address the potential impact of transport, aging, and disposal (TAD) canister design on transport models, and to ensure integration with other models that are closely associated with the RTA report and being developed or revised in other analysis/model reports in response to IVRT comments. The RTA report will be developed in accordance with the most current version of LP-SIII.10Q-BSC and will reflect current administrative procedures (LP-3.15Q-BSC, ''Managing Technical Product Inputs''; LP-SIII.2Q-BSC, ''Qualification of Unqualified Data''; etc.), and will develop related Document Input Reference System (DIRS) reports and data qualifications as applicable in accordance with prevailing procedures. The RTA report consists of three models: the engineered barrier system (EBS) flow model, the EBS transport model, and the EBS-unsaturated zone (UZ) interface model. The flux-splitting submodel in the EBS flow model will change, so the EBS flow model will be validated again. The EBS transport model and validation of the model will be substantially revised in Revision 03 of the RTA report, which is the main subject of this TWP. The EBS-UZ interface model may be changed in Revision 03 of the RTA report due to changes in the conceptualization of the UZ transport abstraction model (a particle tracker transport model based on the discrete fracture transfer function will be used instead of the dual-continuum transport model previously used). Validation of the EBS-UZ interface model
Building new meanings in technical English from the perspective of the lexical constellation model
Directory of Open Access Journals (Sweden)
Camino Rea Rizzo
2010-10-01
Full Text Available The need to name and communicate to others new concepts in specific domains of human activity leads to the formation of new terms. However, many of the technical words in English are not new from the point of view of form. They rather derive from the common stock of general language: new lexical units are built from already existing forms and/or meanings. The original form is used for naming a new concept by adding a distinctive specialized lexical feature while keeping some semantic features of the original concept. In this paper, we aim at explaining and visualizing the nature of some of the processes that allow for the construction of new senses in technical words through a branching and expanding process, as explained in the lexical constellation model. The analysis is performed on three words widely used in telecommunication English: “bus”, “hub” and “chip”. The understanding of the process may be of great help for learners of ESP in general and technical English in particular.
Schültke, Elisabeth; Trippel, Michael; Bräuer-Krisch, Elke; Renier, Michel; Bartzsch, Stefan; Requardt, Herwig; Döbrössy, Máté D; Nikkhah, Guido
2013-01-01
We have conducted the first in-vivo experiments in pencilbeam irradiation, a new synchrotron radiation technique based on the principle of microbeam irradiation, a concept of spatially fractionated high-dose irradiation. In an animal model of adult C57 BL/6J mice we have determined technical and physiological limitations with the present technical setup of the technique. Fifty-eight animals were distributed in eleven experimental groups, ten groups receiving whole brain radiotherapy with arrays of 50 µm wide beams. We have tested peak doses ranging between 172 Gy and 2,298 Gy at 3 mm depth. Animals in five groups received whole brain radiotherapy with a center-to-center (ctc) distance of 200 µm and a peak-to-valley ratio (PVDR) of ∼ 100, in the other five groups the ctc was 400 µm (PVDR ∼ 400). Motor and memory abilities were assessed during a six months observation period following irradiation. The lower dose limit, determined by the technical equipment, was at 172 Gy. The LD50 was about 1,164 Gy for a ctc of 200 µm and higher than 2,298 Gy for a ctc of 400 µm. Age-dependent loss in motor and memory performance was seen in all groups. Better overall performance (close to that of healthy controls) was seen in the groups irradiated with a ctc of 400 µm.
An Overview of the Technical Basis of HENRE 2.0 Models
2015-08-01
important for estimating health effects, but is also key in developing appropriate guidelines for responders and decontamination protocols. Modeling of...residential buildings (2008-2010). US Department of Homeland Security/US Fire Administration, National Fire Data Center . Topical Fire Report Series... Surgical Research, 61(1), 17-22. Harris, J.W., and Nonan, T.R., 1968. “Early Vascular Permeability Changes in Whole-body X- irradiated Rats,” Radiation
Parrott, Edith L.; Weiland, Karen J.
2017-01-01
This is the presentation for the AIAA Space conference in September 2017. It highlights key information from Using Model-Based Systems Engineering to Provide Artifacts for NASA Project Life-cycle and Technical Reviews paper.
Fractality of profit landscapes and validation of time series models for stock prices
Yi, Il Gu; Oh, Gabjin; Kim, Beom Jun
2013-08-01
We apply a simple trading strategy for various time series of real and artificial stock prices to understand the origin of fractality observed in the resulting profit landscapes. The strategy contains only two parameters p and q, and the sell (buy) decision is made when the log return is larger (smaller) than p (-q). We discretize the unit square (p,q) ∈ [0,1] × [0,1] into the N × N square grid and the profit Π(p,q) is calculated at the center of each cell. We confirm the previous finding that local maxima in profit landscapes are scattered in a fractal-like fashion: the number M of local maxima follows the power-law form M ˜ Na, but the scaling exponent a is found to differ for different time series. From comparisons of real and artificial stock prices, we find that the fat-tailed return distribution is closely related to the exponent a ≈ 1.6 observed for real stock markets. We suggest that the fractality of profit landscape characterized by a ≈ 1.6 can be a useful measure to validate time series model for stock prices.
Bakker, Mark
2010-08-01
A new analytic solution approach is presented for the modeling of steady flow to pumping wells near rivers in strip aquifers; all boundaries of the river and strip aquifer may be curved. The river penetrates the aquifer only partially and has a leaky stream bed. The water level in the river may vary spatially. Flow in the aquifer below the river is semi-confined while flow in the aquifer adjacent to the river is confined or unconfined and may be subject to areal recharge. Analytic solutions are obtained through superposition of analytic elements and Fourier series. Boundary conditions are specified at collocation points along the boundaries. The number of collocation points is larger than the number of coefficients in the Fourier series and a solution is obtained in the least squares sense. The solution is analytic while boundary conditions are met approximately. Very accurate solutions are obtained when enough terms are used in the series. Several examples are presented for domains with straight and curved boundaries, including a well pumping near a meandering river with a varying water level. The area of the river bottom where water infiltrates into the aquifer is delineated and the fraction of river water in the well water is computed for several cases.
International Nuclear Information System (INIS)
Yao Zhenkai
2012-01-01
A composite evaluation model of commercial geological exploration and mining development project was discussed, this new model consists of polity-economy-technique (PET) synthetic evaluation sub-model and geology-mining-metallurgy (GMM) technique evaluation sub-model. Besides, some key technical problems in commercial negotiation, such as information screening, quoted price and analysis of deadline, were briefly analyzed. (author)
Loos, Martin; Krauss, Martin; Fenner, Kathrin
2012-09-18
Formation of soil nonextractable residues (NER) is central to the fate and persistence of pesticides. To investigate pools and extent of NER formation, an established inverse modeling approach for pesticide soil degradation time series was evaluated with a Monte Carlo Markov Chain (MCMC) sampling procedure. It was found that only half of 73 pesticide degradation time series from a homogeneous soil source allowed for well-behaved identification of kinetic parameters with a four-pool model containing a parent compound, a metabolite, a volatile, and a NER pool. A subsequent simulation indeed confirmed distinct parameter combinations of low identifiability. Taking the resulting uncertainties into account, several conclusions regarding NER formation and its impact on persistence assessment could nonetheless be drawn. First, rate constants for transformation of parent compounds to metabolites were correlated to those for transformation of parent compounds to NER, leading to degradation half-lives (DegT50) typically not being larger than disappearance half-lives (DT50) by more than a factor of 2. Second, estimated rate constants were used to evaluate NER formation over time. This showed that NER formation, particularly through the metabolite pool, may be grossly underestimated when using standard incubation periods. It further showed that amounts and uncertainties in (i) total NER, (ii) NER formed from the parent pool, and (iii) NER formed from the metabolite pool vary considerably among data sets at t→∞, with no clear dominance between (ii) and (iii). However, compounds containing aromatic amine moieties were found to form significantly more total NER when extrapolating to t→∞ than the other compounds studied. Overall, our study stresses the general need for assessing uncertainties, identifiability issues, and resulting biases when using inverse modeling of degradation time series for evaluating persistence and NER formation.
The evaluator as technical assistant: A model for systemic reform support
Century, Jeanne Rose
This study explored evaluation of systemic reform. Specifically, it focused on the evaluation of a systemic effort to improve K-8 science, mathematics and technology education. The evaluation was of particular interest because it used both technical assistance and evaluation strategies. Through studying the combination of these roles, this investigation set out to increase understanding of potentially new evaluator roles, distinguish important characteristics of the evaluator/project participant relationship, and identify how these roles and characteristics contribute to effective evaluation of systemic science education reform. This qualitative study used interview, document analysis, and participant observation as methods of data collection. Interviews were conducted with project leaders, project participants, and evaluators and focused on the evaluation strategies and process, the use of the evaluation, and technical assistance. Documents analyzed included transcripts of evaluation team meetings and reports, memoranda and other print materials generated by the project leaders and the evaluators. Data analysis consisted of analytic and interpretive procedures consistent with the qualitative data collected and entailed a combined process of coding transcripts of interviews and meetings, field notes, and other documents; analyzing and organizing findings; writing of reflective and analytic memos; and designing and diagramming conceptual relationships. The data analysis resulted in the development of the Multi-Function Model for Systemic Reform Support. This model organizes systemic reform support into three functions: evaluation, technical assistance, and a third, named here as "systemic perspective." These functions work together to support the project's educational goals as well as a larger goal--building capacity in project participants. This model can now serve as an informed starting point or "blueprint" for strategically supporting systemic reform.
Non-linear auto-regressive models for cross-frequency coupling in neural time series
Tallot, Lucille; Grabot, Laetitia; Doyère, Valérie; Grenier, Yves; Gramfort, Alexandre
2017-01-01
We address the issue of reliably detecting and quantifying cross-frequency coupling (CFC) in neural time series. Based on non-linear auto-regressive models, the proposed method provides a generative and parametric model of the time-varying spectral content of the signals. As this method models the entire spectrum simultaneously, it avoids the pitfalls related to incorrect filtering or the use of the Hilbert transform on wide-band signals. As the model is probabilistic, it also provides a score of the model “goodness of fit” via the likelihood, enabling easy and legitimate model selection and parameter comparison; this data-driven feature is unique to our model-based approach. Using three datasets obtained with invasive neurophysiological recordings in humans and rodents, we demonstrate that these models are able to replicate previous results obtained with other metrics, but also reveal new insights such as the influence of the amplitude of the slow oscillation. Using simulations, we demonstrate that our parametric method can reveal neural couplings with shorter signals than non-parametric methods. We also show how the likelihood can be used to find optimal filtering parameters, suggesting new properties on the spectrum of the driving signal, but also to estimate the optimal delay between the coupled signals, enabling a directionality estimation in the coupling. PMID:29227989
Time series modeling of soil moisture dynamics on a steep mountainous hillside
Kim, Sanghyun
2016-05-01
The response of soil moisture to rainfall events along hillslope transects is an important hydrologic process and a critical component of interactions between soil vegetation and the atmosphere. In this context, the research described in this article addresses the spatial distribution of soil moisture as a function of topography. In order to characterize the temporal variation in soil moisture on a steep mountainous hillside, a transfer function, including a model for noise, was introduced. Soil moisture time series with similar rainfall amounts, but different wetness gradients were measured in the spring and fall. Water flux near the soil moisture sensors was modeled and mathematical expressions were developed to provide a basis for input-output modeling of rainfall and soil moisture using hydrological processes such as infiltration, exfiltration and downslope lateral flow. The characteristics of soil moisture response can be expressed in terms of model structure. A seasonal comparison of models reveals differences in soil moisture response to rainfall, possibly associated with eco-hydrological process and evapotranspiration. Modeling results along the hillslope indicate that the spatial structure of the soil moisture response patterns mainly appears in deeper layers. Similarities between topographic attributes and stochastic model structures are spatially organized. The impact of temporal and spatial discretization scales on parameter expression is addressed in the context of modeling results that link rainfall events and soil moisture.
Multidimensional k-nearest neighbor model based on EEMD for financial time series forecasting
Zhang, Ningning; Lin, Aijing; Shang, Pengjian
2017-07-01
In this paper, we propose a new two-stage methodology that combines the ensemble empirical mode decomposition (EEMD) with multidimensional k-nearest neighbor model (MKNN) in order to forecast the closing price and high price of the stocks simultaneously. The modified algorithm of k-nearest neighbors (KNN) has an increasingly wide application in the prediction of all fields. Empirical mode decomposition (EMD) decomposes a nonlinear and non-stationary signal into a series of intrinsic mode functions (IMFs), however, it cannot reveal characteristic information of the signal with much accuracy as a result of mode mixing. So ensemble empirical mode decomposition (EEMD), an improved method of EMD, is presented to resolve the weaknesses of EMD by adding white noise to the original data. With EEMD, the components with true physical meaning can be extracted from the time series. Utilizing the advantage of EEMD and MKNN, the new proposed ensemble empirical mode decomposition combined with multidimensional k-nearest neighbor model (EEMD-MKNN) has high predictive precision for short-term forecasting. Moreover, we extend this methodology to the case of two-dimensions to forecast the closing price and high price of the four stocks (NAS, S&P500, DJI and STI stock indices) at the same time. The results indicate that the proposed EEMD-MKNN model has a higher forecast precision than EMD-KNN, KNN method and ARIMA.
Time Series Modeling of Human Operator Dynamics in Manual Control Tasks
Biezad, D. J.; Schmidt, D. K.
1984-01-01
A time-series technique is presented for identifying the dynamic characteristics of the human operator in manual control tasks from relatively short records of experimental data. Control of system excitation signals used in the identification is not required. The approach is a multi-channel identification technique for modeling multi-input/multi-output situations. The method presented includes statistical tests for validity, is designed for digital computation, and yields estimates for the frequency response of the human operator. A comprehensive relative power analysis may also be performed for validated models. This method is applied to several sets of experimental data; the results are discussed and shown to compare favorably with previous research findings. New results are also presented for a multi-input task that was previously modeled to demonstrate the strengths of the method.
Applications and Comparisons of Four Time Series Models in Epidemiological Surveillance Data
Young, Alistair A.; Li, Xiaosong
2014-01-01
Public health surveillance systems provide valuable data for reliable predication of future epidemic events. This paper describes a study that used nine types of infectious disease data collected through a national public health surveillance system in mainland China to evaluate and compare the performances of four time series methods, namely, two decomposition methods (regression and exponential smoothing), autoregressive integrated moving average (ARIMA) and support vector machine (SVM). The data obtained from 2005 to 2011 and in 2012 were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The accuracy of the statistical models in forecasting future epidemic disease proved their effectiveness in epidemiological surveillance. Although the comparisons found that no single method is completely superior to the others, the present study indeed highlighted that the SVMs outperforms the ARIMA model and decomposition methods in most cases. PMID:24505382
Mining Gene Regulatory Networks by Neural Modeling of Expression Time-Series.
Rubiolo, Mariano; Milone, Diego H; Stegmayer, Georgina
2015-01-01
Discovering gene regulatory networks from data is one of the most studied topics in recent years. Neural networks can be successfully used to infer an underlying gene network by modeling expression profiles as times series. This work proposes a novel method based on a pool of neural networks for obtaining a gene regulatory network from a gene expression dataset. They are used for modeling each possible interaction between pairs of genes in the dataset, and a set of mining rules is applied to accurately detect the subjacent relations among genes. The results obtained on artificial and real datasets confirm the method effectiveness for discovering regulatory networks from a proper modeling of the temporal dynamics of gene expression profiles.
Agarwal, G. C.; Osafo-Charles, F.; Oneill, W. D.; Gottlieb, G. L.
1982-01-01
Time series analysis is applied to model human operator dynamics in pursuit and compensatory tracking modes. The normalized residual criterion is used as a one-step analytical tool to encompass the processes of identification, estimation, and diagnostic checking. A parameter constraining technique is introduced to develop more reliable models of human operator dynamics. The human operator is adequately modeled by a second order dynamic system both in pursuit and compensatory tracking modes. In comparing the data sampling rates, 100 msec between samples is adequate and is shown to provide better results than 200 msec sampling. The residual power spectrum and eigenvalue analysis show that the human operator is not a generator of periodic characteristics.
Development of New Loan Payment Models with Piecewise Geometric Gradient Series
Directory of Open Access Journals (Sweden)
Erdal Aydemir
2014-12-01
Full Text Available Engineering economics plays an important role in decision making. Also, the cash flows, time value of money and interest rates are the most important research fields in mathematical finance. Generalized formulae obtained from a variety of models with the time value of money and cash flows are inadequate to solve some problems. In this study, a new generalized formulae is considered for the first time and derived from a loan payment model which is a certain number of payment amount determined by customer at the beginning of payment period and the other repayments with piecewise linear gradient series. As a result, some numerical examples with solutions are given for the developed models.
Model for the heart beat-to-beat time series during meditation
Capurro, A.; Diambra, L.; Malta, C. P.
2003-09-01
We present a model for the respiratory modulation of the heart beat-to-beat interval series. The model consists of a pacemaker, that simulates the membrane potential of the sinoatrial node, modulated by a periodic input signal plus correlated noise that simulates the respiratory input. The model was used to assess the waveshape of the respiratory signals needed to reproduce in the phase space the trajectory of experimental heart beat-to-beat interval data. The data sets were recorded during meditation practices of the Chi and Kundalini Yoga techniques. Our study indicates that in the first case the respiratory signal has the shape of a smoothed square wave, and in the second case it has the shape of a smoothed triangular wave.
Kwasniok, Frank
2013-11-01
A time series analysis method for predicting the probability density of a dynamical system is proposed. A nonstationary parametric model of the probability density is estimated from data within a maximum likelihood framework and then extrapolated to forecast the future probability density and explore the system for critical transitions or tipping points. A full systematic account of parameter uncertainty is taken. The technique is generic, independent of the underlying dynamics of the system. The method is verified on simulated data and then applied to prediction of Arctic sea-ice extent.
Kane, Michael J; Price, Natalie; Scotch, Matthew; Rabinowitz, Peter
2014-08-13
Time series models can play an important role in disease prediction. Incidence data can be used to predict the future occurrence of disease events. Developments in modeling approaches provide an opportunity to compare different time series models for predictive power. We applied ARIMA and Random Forest time series models to incidence data of outbreaks of highly pathogenic avian influenza (H5N1) in Egypt, available through the online EMPRES-I system. We found that the Random Forest model outperformed the ARIMA model in predictive ability. Furthermore, we found that the Random Forest model is effective for predicting outbreaks of H5N1 in Egypt. Random Forest time series modeling provides enhanced predictive ability over existing time series models for the prediction of infectious disease outbreaks. This result, along with those showing the concordance between bird and human outbreaks (Rabinowitz et al. 2012), provides a new approach to predicting these dangerous outbreaks in bird populations based on existing, freely available data. Our analysis uncovers the time-series structure of outbreak severity for highly pathogenic avain influenza (H5N1) in Egypt.
A Time Series Model for Assessing the Trend and Forecasting the Road Traffic Accident Mortality.
Yousefzadeh-Chabok, Shahrokh; Ranjbar-Taklimie, Fatemeh; Malekpouri, Reza; Razzaghi, Alireza
2016-09-01
Road traffic accident (RTA) is one of the main causes of trauma and known as a growing public health concern worldwide, especially in developing countries. Assessing the trend of fatalities in the past years and forecasting it enables us to make the appropriate planning for prevention and control. This study aimed to assess the trend of RTAs and forecast it in the next years by using time series modeling. In this historical analytical study, the RTA mortalities in Zanjan Province, Iran, were evaluated during 2007 - 2013. The time series analyses including Box-Jenkins models were used to assess the trend of accident fatalities in previous years and forecast it for the next 4 years. The mean age of the victims was 37.22 years (SD = 20.01). From a total of 2571 deaths, 77.5% (n = 1992) were males and 22.5% (n = 579) were females. The study models showed a descending trend of fatalities in the study years. The SARIMA (1, 1, 3) (0, 1, 0) 12 model was recognized as a best fit model in forecasting the trend of fatalities. Forecasting model also showed a descending trend of traffic accident mortalities in the next 4 years. There was a decreasing trend in the study and the future years. It seems that implementation of some interventions in the recent decade has had a positive effect on the decline of RTA fatalities. Nevertheless, there is still a need to pay more attention in order to prevent the occurrence and the mortalities related to traffic accidents.
A Time Series Model for Assessing the Trend and Forecasting the Road Traffic Accident Mortality
Yousefzadeh-Chabok, Shahrokh; Ranjbar-Taklimie, Fatemeh; Malekpouri, Reza; Razzaghi, Alireza
2016-01-01
Background Road traffic accident (RTA) is one of the main causes of trauma and known as a growing public health concern worldwide, especially in developing countries. Assessing the trend of fatalities in the past years and forecasting it enables us to make the appropriate planning for prevention and control. Objectives This study aimed to assess the trend of RTAs and forecast it in the next years by using time series modeling. Materials and Methods In this historical analytical study, the RTA mortalities in Zanjan Province, Iran, were evaluated during 2007 - 2013. The time series analyses including Box-Jenkins models were used to assess the trend of accident fatalities in previous years and forecast it for the next 4 years. Results The mean age of the victims was 37.22 years (SD = 20.01). From a total of 2571 deaths, 77.5% (n = 1992) were males and 22.5% (n = 579) were females. The study models showed a descending trend of fatalities in the study years. The SARIMA (1, 1, 3) (0, 1, 0) 12 model was recognized as a best fit model in forecasting the trend of fatalities. Forecasting model also showed a descending trend of traffic accident mortalities in the next 4 years. Conclusions There was a decreasing trend in the study and the future years. It seems that implementation of some interventions in the recent decade has had a positive effect on the decline of RTA fatalities. Nevertheless, there is still a need to pay more attention in order to prevent the occurrence and the mortalities related to traffic accidents. PMID:27800467
Modeling commodity salam contract between two parties for discrete and continuous time series
Hisham, Azie Farhani Badrol; Jaffar, Maheran Mohd
2017-08-01
In order for Islamic finance to remain competitive as the conventional, there needs a new development of Islamic compliance product such as Islamic derivative that can be used to manage the risk. However, under syariah principles and regulations, all financial instruments must not be conflicting with five syariah elements which are riba (interest paid), rishwah (corruption), gharar (uncertainty or unnecessary risk), maysir (speculation or gambling) and jahl (taking advantage of the counterparty's ignorance). This study has proposed a traditional Islamic contract namely salam that can be built as an Islamic derivative product. Although a lot of studies has been done on discussing and proposing the implementation of salam contract as the Islamic product however they are more into qualitative and law issues. Since there is lack of quantitative study of salam contract being developed, this study introduces mathematical models that can value the appropriate salam price for a commodity salam contract between two parties. In modeling the commodity salam contract, this study has modified the existing conventional derivative model and come out with some adjustments to comply with syariah rules and regulations. The cost of carry model has been chosen as the foundation to develop the commodity salam model between two parties for discrete and continuous time series. However, the conventional time value of money results from the concept of interest that is prohibited in Islam. Therefore, this study has adopted the idea of Islamic time value of money which is known as the positive time preference, in modeling the commodity salam contract between two parties for discrete and continuous time series.
A Technical Review on Biomass Processing: Densification, Preprocessing, Modeling and Optimization
Energy Technology Data Exchange (ETDEWEB)
Jaya Shankar Tumuluru; Christopher T. Wright
2010-06-01
It is now a well-acclaimed fact that burning fossil fuels and deforestation are major contributors to climate change. Biomass from plants can serve as an alternative renewable and carbon-neutral raw material for the production of bioenergy. Low densities of 40–60 kg/m3 for lignocellulosic and 200–400 kg/m3 for woody biomass limits their application for energy purposes. Prior to use in energy applications these materials need to be densified. The densified biomass can have bulk densities over 10 times the raw material helping to significantly reduce technical limitations associated with storage, loading and transportation. Pelleting, briquetting, or extrusion processing are commonly used methods for densification. The aim of the present research is to develop a comprehensive review of biomass processing that includes densification, preprocessing, modeling and optimization. The specific objective include carrying out a technical review on (a) mechanisms of particle bonding during densification; (b) methods of densification including extrusion, briquetting, pelleting, and agglomeration; (c) effects of process and feedstock variables and biomass biochemical composition on the densification (d) effects of preprocessing such as grinding, preheating, steam explosion, and torrefaction on biomass quality and binding characteristics; (e) models for understanding the compression characteristics; and (f) procedures for response surface modeling and optimization.
Poole, Sandra; Vis, Marc; Knight, Rodney; Seibert, Jan
2017-01-01
Ecologically relevant streamflow characteristics (SFCs) of ungauged catchments are often estimated from simulated runoff of hydrologic models that were originally calibrated on gauged catchments. However, SFC estimates of the gauged donor catchments and subsequently the ungauged catchments can be substantially uncertain when models are calibrated using traditional approaches based on optimization of statistical performance metrics (e.g., Nash–Sutcliffe model efficiency). An improved calibration strategy for gauged catchments is therefore crucial to help reduce the uncertainties of estimated SFCs for ungauged catchments. The aim of this study was to improve SFC estimates from modeled runoff time series in gauged catchments by explicitly including one or several SFCs in the calibration process. Different types of objective functions were defined consisting of the Nash–Sutcliffe model efficiency, single SFCs, or combinations thereof. We calibrated a bucket-type runoff model (HBV – Hydrologiska Byråns Vattenavdelning – model) for 25 catchments in the Tennessee River basin and evaluated the proposed calibration approach on 13 ecologically relevant SFCs representing major flow regime components and different flow conditions. While the model generally tended to underestimate the tested SFCs related to mean and high-flow conditions, SFCs related to low flow were generally overestimated. The highest estimation accuracies were achieved by a SFC-specific model calibration. Estimates of SFCs not included in the calibration process were of similar quality when comparing a multi-SFC calibration approach to a traditional model efficiency calibration. For practical applications, this implies that SFCs should preferably be estimated from targeted runoff model calibration, and modeled estimates need to be carefully interpreted.
Time series modelling of global mean temperature for managerial decision-making.
Romilly, Peter
2005-07-01
Climate change has important implications for business and economic activity. Effective management of climate change impacts will depend on the availability of accurate and cost-effective forecasts. This paper uses univariate time series techniques to model the properties of a global mean temperature dataset in order to develop a parsimonious forecasting model for managerial decision-making over the short-term horizon. Although the model is estimated on global temperature data, the methodology could also be applied to temperature data at more localised levels. The statistical techniques include seasonal and non-seasonal unit root testing with and without structural breaks, as well as ARIMA and GARCH modelling. A forecasting evaluation shows that the chosen model performs well against rival models. The estimation results confirm the findings of a number of previous studies, namely that global mean temperatures increased significantly throughout the 20th century. The use of GARCH modelling also shows the presence of volatility clustering in the temperature data, and a positive association between volatility and global mean temperature.
Modeling time-series count data: the unique challenges facing political communication studies.
Fogarty, Brian J; Monogan, James E
2014-05-01
This paper demonstrates the importance of proper model specification when analyzing time-series count data in political communication studies. It is common for scholars of media and politics to investigate counts of coverage of an issue as it evolves over time. Many scholars rightly consider the issues of time dependence and dynamic causality to be the most important when crafting a model. However, to ignore the count features of the outcome variable overlooks an important feature of the data. This is particularly the case when modeling data with a low number of counts. In this paper, we argue that the Poisson autoregressive model (Brandt and Williams, 2001) accurately meets the needs of many media studies. We replicate the analyses of Flemming et al. (1997), Peake and Eshbaugh-Soha (2008), and Ura (2009) and demonstrate that models missing some of the assumptions of the Poisson autoregressive model often yield invalid inferences. We also demonstrate that the effect of any of these models can be illustrated dynamically with estimates of uncertainty through a simulation procedure. The paper concludes with implications of these findings for the practical researcher. Copyright © 2013 Elsevier Inc. All rights reserved.
A parameter network and model pyramid for managing technical information flow
International Nuclear Information System (INIS)
Sinnock, S.; Hartman, H.A.
1994-01-01
Prototypes of information management tools have been developed that can help communicate the technical basis for nuclear waste disposal to a broad audience of program scientists and engineers, project managers, and informed observers from stakeholder organizations. These tools include system engineering concepts, parameter networks expressed as influence diagrams, associated model hierarchies, and a relational database. These tools are used to express relationships among data-collection parameters, model input parameters, model output parameters, systems requirements, physical elements of a system description, and functional analysis of the contribution of physical elements and their associated parameters in satisfying the system requirements. By organizing parameters, models, physical elements, functions, and requirements in a visually reviewable network and a relational database the severe communication challenges facing participants in the nuclear waste dialog can be addressed. The network identifies the influences that data collected in the field have on measures of repository suitability, providing a visual, traceable map that clarifies the role of data and models in supporting conclusions about repository suitability. The map allows conclusions to be traced directly to the underlying parameters and models. Uncertainty in these underlying elements can be exposed to open review in the context of the effects uncertainty has on judgements about suitability. A parameter network provides a stage upon which an informed social dialog about the technical merits of a nuclear waste repository can be conducted. The basis for such dialog must be that stage, if decisions about repository suitability are to be based on a repository's ability to meet requirements embodied in laws and regulations governing disposal of nuclear wastes
International Nuclear Information System (INIS)
Doke, Ryosuke; Yasue, Ken-ichi; Niizato, Tadafumi; Nakayasu, Akio
2012-01-01
Geological hazard assessments are being used to make important decisions relevant to nuclear facilities such as a repository for deep geological disposal of high-level radioactive waste. With respect to such repositories, topographic evolution is a key issue for description of the long-term evolution of a groundwater flow characteristics in time spans of tens to hundreds of thousands of years. The construction of topographic evolution models is complex, involving tacit knowledge and working processes. Therefore, it is important to externalise, that is to explicitly present the tacit knowledge and decision-making processes used by experts in the model building unambiguously, with thorough documentation and to provide key knowledge to support planning and implementation of investigations. In this study, documentation of the technical know-how used for the construction of a topographic evolution model is demonstrated. The process followed in the construction of the model is illustrated using task-flow logic diagrams; the process involves four main tasks with several sub-tasks. The task-flow followed for an investigation to estimate uplift rates linked to the task-flow for the modelling of topographic evolution is also illustrated. In addition, the decision-making processes in the investigation are expressed in logical IF-THEN format for each task. Based on the documented technical know-how, an IT-based Expert System was constructed. In future work, it is necessary to analyse the knowledge, including the management of uncertainties in the modelling and investigations, and to integrate fundamental ideas for managing uncertainties with expert system. (authors)
Porta, Alberto; Bari, Vlasta; Ranuzzi, Giovanni; De Maria, Beatrice; Baselli, Giuseppe
2017-09-01
We propose a multiscale complexity (MSC) method assessing irregularity in assigned frequency bands and being appropriate for analyzing the short time series. It is grounded on the identification of the coefficients of an autoregressive model, on the computation of the mean position of the poles generating the components of the power spectral density in an assigned frequency band, and on the assessment of its distance from the unit circle in the complex plane. The MSC method was tested on simulations and applied to the short heart period (HP) variability series recorded during graded head-up tilt in 17 subjects (age from 21 to 54 years, median = 28 years, 7 females) and during paced breathing protocols in 19 subjects (age from 27 to 35 years, median = 31 years, 11 females) to assess the contribution of time scales typical of the cardiac autonomic control, namely in low frequency (LF, from 0.04 to 0.15 Hz) and high frequency (HF, from 0.15 to 0.5 Hz) bands to the complexity of the cardiac regulation. The proposed MSC technique was compared to a traditional model-free multiscale method grounded on information theory, i.e., multiscale entropy (MSE). The approach suggests that the reduction of HP variability complexity observed during graded head-up tilt is due to a regularization of the HP fluctuations in LF band via a possible intervention of sympathetic control and the decrement of HP variability complexity observed during slow breathing is the result of the regularization of the HP variations in both LF and HF bands, thus implying the action of physiological mechanisms working at time scales even different from that of respiration. MSE did not distinguish experimental conditions at time scales larger than 1. Over a short time series MSC allows a more insightful association between cardiac control complexity and physiological mechanisms modulating cardiac rhythm compared to a more traditional tool such as MSE.
EO Model for Tacit Knowledge Externalization in Socio-Technical Enterprises
Directory of Open Access Journals (Sweden)
Shreyas Suresh Rao
2017-03-01
Full Text Available Aim/Purpose: A vital business activity within socio-technical enterprises is tacit knowledge externalization, which elicits and explicates tacit knowledge of enterprise employees as external knowledge. The aim of this paper is to integrate diverse aspects of externalization through the Enterprise Ontology model. Background: Across two decades, researchers have explored various aspects of tacit knowledge externalization. However, from the existing works, it is revealed that there is no uniform representation of the externalization process, which has resulted in divergent and contradictory interpretations across the literature. Methodology\t: The Enterprise Ontology model is constructed step-wise through the conceptual and measurement views. While the conceptual view encompasses three patterns that model the externalization process, the measurement view employs certainty-factor model to empirically measure the outcome of the externalization process. Contribution: The paper contributes towards knowledge management literature in two ways. The first contribution is the Enterprise Ontology model that integrates diverse aspects of externalization. The second contribution is a Web application that validates the model through a case study in banking. Findings: The findings show that the Enterprise Ontology model and the patterns are pragmatic in externalizing the tacit knowledge of experts in a problem-solving scenario within a banking enterprise. Recommendations for Practitioners\t: Consider the diverse aspects (what, where, when, why, and how during the tacit knowledge externalization process. Future Research:\tTo extend the Enterprise Ontology model to include externalization from partially automated enterprise systems.
Energy Technology Data Exchange (ETDEWEB)
Ian Sue Wing
2006-04-18
The research supported by this award pursued three lines of inquiry: (1) The construction of dynamic general equilibrium models to simulate the accumulation and substitution of knowledge, which has resulted in the preparation and submission of several papers: (a) A submitted pedagogic paper which clarifies the structure and operation of computable general equilibrium (CGE) models (C.2), and a review article in press which develops a taxonomy for understanding the representation of technical change in economic and engineering models for climate policy analysis (B.3). (b) A paper which models knowledge directly as a homogeneous factor, and demonstrates that inter-sectoral reallocation of knowledge is the key margin of adjustment which enables induced technical change to lower the costs of climate policy (C.1). (c) An empirical paper which estimates the contribution of embodied knowledge to aggregate energy intensity in the U.S. (C.3), followed by a companion article which embeds these results within a CGE model to understand the degree to which autonomous energy efficiency improvement (AEEI) is attributable to technical change as opposed to sub-sectoral shifts in industrial composition (C.4) (d) Finally, ongoing theoretical work to characterize the precursors and implications of the response of innovation to emission limits (E.2). (2) Data development and simulation modeling to understand how the characteristics of discrete energy supply technologies determine their succession in response to emission limits when they are embedded within a general equilibrium framework. This work has produced two peer-reviewed articles which are currently in press (B.1 and B.2). (3) Empirical investigation of trade as an avenue for the transmission of technological change to developing countries, and its implications for leakage, which has resulted in an econometric study which is being revised for submission to a journal (E.1). As work commenced on this topic, the U.S. withdrawal
Estimating the basic reproduction rate of HFMD using the time series SIR model in Guangdong, China.
Directory of Open Access Journals (Sweden)
Zhicheng Du
Full Text Available Hand, foot, and mouth disease (HFMD has caused a substantial burden of disease in China, especially in Guangdong Province. Based on notifiable cases, we use the time series Susceptible-Infected-Recovered model to estimate the basic reproduction rate (R0 and the herd immunity threshold, understanding the transmission and persistence of HFMD more completely for efficient intervention in this province. The standardized difference between the reported and fitted time series of HFMD was 0.009 (<0.2. The median basic reproduction rate of total, enterovirus 71, and coxsackievirus 16 cases in Guangdong were 4.621 (IQR: 3.907-5.823, 3.023 (IQR: 2.289-4.292 and 7.767 (IQR: 6.903-10.353, respectively. The heatmap of R0 showed semiannual peaks of activity, including a major peak in spring and early summer (about the 12th week followed by a smaller peak in autumn (about the 36th week. The county-level model showed that Longchuan (R0 = 33, Gaozhou (R0 = 24, Huazhou (R0 = 23 and Qingxin (R0 = 19 counties have higher basic reproduction rate than other counties in the province. The epidemic of HFMD in Guangdong Province is still grim, and strategies like the World Health Organization's expanded program on immunization need to be implemented. An elimination of HFMD in Guangdong might need a Herd Immunity Threshold of 78%.
Stochastic modeling for time series InSAR: with emphasis on atmospheric effects
Cao, Yunmeng; Li, Zhiwei; Wei, Jianchao; Hu, Jun; Duan, Meng; Feng, Guangcai
2018-02-01
Despite the many applications of time series interferometric synthetic aperture radar (TS-InSAR) techniques in geophysical problems, error analysis and assessment have been largely overlooked. Tropospheric propagation error is still the dominant error source of InSAR observations. However, the spatiotemporal variation of atmospheric effects is seldom considered in the present standard TS-InSAR techniques, such as persistent scatterer interferometry and small baseline subset interferometry. The failure to consider the stochastic properties of atmospheric effects not only affects the accuracy of the estimators, but also makes it difficult to assess the uncertainty of the final geophysical results. To address this issue, this paper proposes a network-based variance-covariance estimation method to model the spatiotemporal variation of tropospheric signals, and to estimate the temporal variance-covariance matrix of TS-InSAR observations. The constructed stochastic model is then incorporated into the TS-InSAR estimators both for parameters (e.g., deformation velocity, topography residual) estimation and uncertainty assessment. It is an incremental and positive improvement to the traditional weighted least squares methods to solve the multitemporal InSAR time series. The performance of the proposed method is validated by using both simulated and real datasets.
Directory of Open Access Journals (Sweden)
António Manuel Martins de Almeida
2016-06-01
Full Text Available Tourism is the leading economic sector in most islands and for that reason market trends are closely monitored due to the huge impacts of relatively minor changes in the demand patterns. An interesting line of research regarding the analysis of market trends concerns the examination of time series to get an historical overview of the data patterns. The modelling of demand patterns is obviously dependent on data availability, and the measurement of changes in demand patterns is quite often focused on a few decades. In this paper, we use long-term time-series data to analyse the evolution of the main markets in Madeira, by country of origin, in order to re-examine the Butler life cycle model, based on data available from 1946 onwards. This study is an opportunity to document the historical development of the industry in Madeira and to introduce the discussion about the rejuvenation of a mature destination. Tourism development in Madeira has experienced rapid growth until the late 90s, as one of the leading destinations in the European context. However, annual growth rates are not within acceptable ranges, which lead policy-makers and experts to recommend a thoughtfully assessment of the industry prospects.
A Virtual Machine Migration Strategy Based on Time Series Workload Prediction Using Cloud Model
Directory of Open Access Journals (Sweden)
Yanbing Liu
2014-01-01
Full Text Available Aimed at resolving the issues of the imbalance of resources and workloads at data centers and the overhead together with the high cost of virtual machine (VM migrations, this paper proposes a new VM migration strategy which is based on the cloud model time series workload prediction algorithm. By setting the upper and lower workload bounds for host machines, forecasting the tendency of their subsequent workloads by creating a workload time series using the cloud model, and stipulating a general VM migration criterion workload-aware migration (WAM, the proposed strategy selects a source host machine, a destination host machine, and a VM on the source host machine carrying out the task of the VM migration. Experimental results and analyses show, through comparison with other peer research works, that the proposed method can effectively avoid VM migrations caused by momentary peak workload values, significantly lower the number of VM migrations, and dynamically reach and maintain a resource and workload balance for virtual machines promoting an improved utilization of resources in the entire data center.
Bodmer, James E; English, Anthony; Brady, Megan; Blackwell, Ken; Haxhinasto, Kari; Fotedar, Sunaina; Borgman, Kurt; Bai, Er-Wei; Moy, Alan B
2005-09-01
Transendothelial impedance across an endothelial monolayer grown on a microelectrode has previously been modeled as a repeating pattern of disks in which the electrical circuit consists of a resistor and capacitor in series. Although this numerical model breaks down barrier function into measurements of cell-cell adhesion, cell-matrix adhesion, and membrane capacitance, such solution parameters can be inaccurate without understanding model stability and error. In this study, we have evaluated modeling stability and error by using a chi(2) evaluation and Levenberg-Marquardt nonlinear least-squares (LM-NLS) method of the real and/or imaginary data in which the experimental measurement is compared with the calculated measurement derived by the model. Modeling stability and error were dependent on current frequency and the type of experimental data modeled. Solution parameters of cell-matrix adhesion were most susceptible to modeling instability. Furthermore, the LM-NLS method displayed frequency-dependent instability of the solution parameters, regardless of whether the real or imaginary data were analyzed. However, the LM-NLS method identified stable and reproducible solution parameters between all types of experimental data when a defined frequency spectrum of the entire data set was selected on the basis of a criterion of minimizing error. The frequency bandwidth that produced stable solution parameters varied greatly among different data types. Thus a numerical model based on characterizing transendothelial impedance as a resistor and capacitor in series and as a repeating pattern of disks is not sufficient to characterize the entire frequency spectrum of experimental transendothelial impedance.
Directory of Open Access Journals (Sweden)
Jian Ma
2014-01-01
Full Text Available Previous researches have proved the positive effect of creative human capital and its development on the development of economy. Yet, the technical efficiency of creative human capital and its effects are still under research. The authors are trying to estimate the technical efficiency value in Chinese context, which is adjusted by the environmental variables and statistical noises, by establishing a three-stage data envelopment analysis model, using data from 2003 to 2010. The research results indicate that, in this period, the entirety of creative human capital in China and the technical efficiency value in different regions and different provinces is still in the low level and could be promoted. Otherwise, technical non-efficiency is mostly derived from the scale nonefficiency and rarely affected by pure technical efficiency. The research also examines environmental variables’ marked effects on the technical efficiency, and it shows that different environmental variables differ in the aspect of their own effects. The expansion of the scale of education, development of healthy environment, growth of GDP, development of skill training, and population migration could reduce the input of creative human capital and promote the technical efficiency, while development of trade and institutional change, on the contrary, would block the input of creative human capital and the promotion the technical efficiency.
Cooling load calculation by the radiant time series method - effect of solar radiation models
Energy Technology Data Exchange (ETDEWEB)
Costa, Alexandre M.S. [Universidade Estadual de Maringa (UEM), PR (Brazil)], E-mail: amscosta@uem.br
2010-07-01
In this work was analyzed numerically the effect of three different models for solar radiation on the cooling load calculated by the radiant time series' method. The solar radiation models implemented were clear sky, isotropic sky and anisotropic sky. The radiant time series' method (RTS) was proposed by ASHRAE (2001) for replacing the classical methods of cooling load calculation, such as TETD/TA. The method is based on computing the effect of space thermal energy storage on the instantaneous cooling load. The computing is carried out by splitting the heat gain components in convective and radiant parts. Following the radiant part is transformed using time series, which coefficients are a function of the construction type and heat gain (solar or non-solar). The transformed result is added to the convective part, giving the instantaneous cooling load. The method was applied for investigate the influence for an example room. The location used was - 23 degree S and 51 degree W and the day was 21 of January, a typical summer day in the southern hemisphere. The room was composed of two vertical walls with windows exposed to outdoors with azimuth angles equals to west and east directions. The output of the different models of solar radiation for the two walls in terms of direct and diffuse components as well heat gains were investigated. It was verified that the clear sky exhibited the less conservative (higher values) for the direct component of solar radiation, with the opposite trend for the diffuse component. For the heat gain, the clear sky gives the higher values, three times higher for the peek hours than the other models. Both isotropic and anisotropic models predicted similar magnitude for the heat gain. The same behavior was also verified for the cooling load. The effect of room thermal inertia was decreasing the cooling load during the peak hours. On the other hand the higher thermal inertia values are the greater for the non peak hours. The effect
Guarnaccia, Claudio; Quartieri, Joseph; Tepedino, Carmine
2017-06-01
One of the most hazardous physical polluting agents, considering their effects on human health, is acoustical noise. Airports are a strong source of acoustical noise, due to the airplanes turbines, to the aero-dynamical noise of transits, to the acceleration or the breaking during the take-off and landing phases of aircrafts, to the road traffic around the airport, etc.. The monitoring and the prediction of the acoustical level emitted by airports can be very useful to assess the impact on human health and activities. In the airports noise scenario, thanks to flights scheduling, the predominant sources may have a periodic behaviour. Thus, a Time Series Analysis approach can be adopted, considering that a general trend and a seasonal behaviour can be highlighted and used to build a predictive model. In this paper, two different approaches are adopted, thus two predictive models are constructed and tested. The first model is based on deterministic decomposition and is built composing the trend, that is the long term behaviour, the seasonality, that is the periodic component, and the random variations. The second model is based on seasonal autoregressive moving average, and it belongs to the stochastic class of models. The two different models are fitted on an acoustical level dataset collected close to the Nice (France) international airport. Results will be encouraging and will show good prediction performances of both the adopted strategies. A residual analysis is performed, in order to quantify the forecasting error features.
A Long-Term Prediction Model of Beijing Haze Episodes Using Time Series Analysis
Directory of Open Access Journals (Sweden)
Xiaoping Yang
2016-01-01
Full Text Available The rapid industrial development has led to the intermittent outbreak of pm2.5 or haze in developing countries, which has brought about great environmental issues, especially in big cities such as Beijing and New Delhi. We investigated the factors and mechanisms of haze change and present a long-term prediction model of Beijing haze episodes using time series analysis. We construct a dynamic structural measurement model of daily haze increment and reduce the model to a vector autoregressive model. Typical case studies on 886 continuous days indicate that our model performs very well on next day’s Air Quality Index (AQI prediction, and in severely polluted cases (AQI ≥ 300 the accuracy rate of AQI prediction even reaches up to 87.8%. The experiment of one-week prediction shows that our model has excellent sensitivity when a sudden haze burst or dissipation happens, which results in good long-term stability on the accuracy of the next 3–7 days’ AQI prediction.
Scaling symmetry, renormalization, and time series modeling: the case of financial assets dynamics.
Zamparo, Marco; Baldovin, Fulvio; Caraglio, Michele; Stella, Attilio L
2013-12-01
We present and discuss a stochastic model of financial assets dynamics based on the idea of an inverse renormalization group strategy. With this strategy we construct the multivariate distributions of elementary returns based on the scaling with time of the probability density of their aggregates. In its simplest version the model is the product of an endogenous autoregressive component and a random rescaling factor designed to embody also exogenous influences. Mathematical properties like increments' stationarity and ergodicity can be proven. Thanks to the relatively low number of parameters, model calibration can be conveniently based on a method of moments, as exemplified in the case of historical data of the S&P500 index. The calibrated model accounts very well for many stylized facts, like volatility clustering, power-law decay of the volatility autocorrelation function, and multiscaling with time of the aggregated return distribution. In agreement with empirical evidence in finance, the dynamics is not invariant under time reversal, and, with suitable generalizations, skewness of the return distribution and leverage effects can be included. The analytical tractability of the model opens interesting perspectives for applications, for instance, in terms of obtaining closed formulas for derivative pricing. Further important features are the possibility of making contact, in certain limits, with autoregressive models widely used in finance and the possibility of partially resolving the long- and short-memory components of the volatility, with consistent results when applied to historical series.
Scaling symmetry, renormalization, and time series modeling: The case of financial assets dynamics
Zamparo, Marco; Baldovin, Fulvio; Caraglio, Michele; Stella, Attilio L.
2013-12-01
We present and discuss a stochastic model of financial assets dynamics based on the idea of an inverse renormalization group strategy. With this strategy we construct the multivariate distributions of elementary returns based on the scaling with time of the probability density of their aggregates. In its simplest version the model is the product of an endogenous autoregressive component and a random rescaling factor designed to embody also exogenous influences. Mathematical properties like increments’ stationarity and ergodicity can be proven. Thanks to the relatively low number of parameters, model calibration can be conveniently based on a method of moments, as exemplified in the case of historical data of the S&P500 index. The calibrated model accounts very well for many stylized facts, like volatility clustering, power-law decay of the volatility autocorrelation function, and multiscaling with time of the aggregated return distribution. In agreement with empirical evidence in finance, the dynamics is not invariant under time reversal, and, with suitable generalizations, skewness of the return distribution and leverage effects can be included. The analytical tractability of the model opens interesting perspectives for applications, for instance, in terms of obtaining closed formulas for derivative pricing. Further important features are the possibility of making contact, in certain limits, with autoregressive models widely used in finance and the possibility of partially resolving the long- and short-memory components of the volatility, with consistent results when applied to historical series.
Accurate estimation of energy expenditure (EE) in children and adolescents is required for a better understanding of physiological, behavioral, and environmental factors affecting energy balance. Cross-sectional time series (CSTS) models, which account for correlation structure of repeated observati...
Time Series Neural Network Model for Part-of-Speech Tagging Indonesian Language
Tanadi, Theo
2018-03-01
Part-of-speech tagging (POS tagging) is an important part in natural language processing. Many methods have been used to do this task, including neural network. This paper models a neural network that attempts to do POS tagging. A time series neural network is modelled to solve the problems that a basic neural network faces when attempting to do POS tagging. In order to enable the neural network to have text data input, the text data will get clustered first using Brown Clustering, resulting a binary dictionary that the neural network can use. To further the accuracy of the neural network, other features such as the POS tag, suffix, and affix of previous words would also be fed to the neural network.
Artificial neural networks for modeling time series of beach litter in the southern North Sea.
Schulz, Marcus; Matthies, Michael
2014-07-01
In European marine waters, existing monitoring programs of beach litter need to be improved concerning litter items used as indicators of pollution levels, efficiency, and effectiveness. In order to ease and focus future monitoring of beach litter on few important litter items, feed-forward neural networks consisting of three layers were developed to relate single litter items to general categories of marine litter. The neural networks developed were applied to seven beaches in the southern North Sea and modeled time series of five general categories of marine litter, such as litter from fishing, shipping, and tourism. Results of regression analyses show that general categories were predicted significantly moderately to well. Measured and modeled data were in the same order of magnitude, and minima and maxima overlapped well. Neural networks were found to be eligible tools to deliver reliable predictions of marine litter with low computational effort and little input of information. Copyright © 2014 Elsevier Ltd. All rights reserved.
An Application of the Coherent Noise Model for the Prediction of Aftershock Magnitude Time Series
Directory of Open Access Journals (Sweden)
Stavros-Richard G. Christopoulos
2017-01-01
Full Text Available Recently, the study of the coherent noise model has led to a simple (binary prediction algorithm for the forthcoming earthquake magnitude in aftershock sequences. This algorithm is based on the concept of natural time and exploits the complexity exhibited by the coherent noise model. Here, using the relocated catalogue from Southern California Seismic Network for 1981 to June 2011, we evaluate the application of this algorithm for the aftershocks of strong earthquakes of magnitude M≥6. The study is also extended by using the Global Centroid Moment Tensor Project catalogue to the case of the six strongest earthquakes in the Earth during the last almost forty years. The predictor time series exhibits the ubiquitous 1/f noise behavior.
2010-07-01
... summarizing each substantive verbal contact we receive about this proposed AD. Discussion The European Aviation Safety Agency (EASA), which is the Technical Agent for the Member States of the European Community... affect about 122 products of U.S. registry. We also estimate that it would take about 81 work-hours per...