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; Pulwarty, Roger; Stefanski, Robert
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.
Suarez, Max J. (Editor); Chang, Yehui; Schubert, Siegfried D.; Lin, Shian-Jiann; Nebuda, Sharon; Shen, Bo-Wen
2001-01-01
This document describes the climate of version 1 of the NASA-NCAR model developed at the Data Assimilation Office (DAO). The model consists of a new finite-volume dynamical core and an implementation of the NCAR climate community model (CCM-3) physical parameterizations. The version of the model examined here was integrated at a resolution of 2 degrees latitude by 2.5 degrees longitude and 32 levels. The results are based on assimilation that was forced with observed sea surface temperature and sea ice for the period 1979-1995, and are compared with NCEP/NCAR reanalyses and various other observational data sets. The results include an assessment of seasonal means, subseasonal transients including the Madden Julian Oscillation, and interannual variability. The quantities include zonal and meridional winds, temperature, specific humidity, geopotential height, stream function, velocity potential, precipitation, sea level pressure, and cloud radiative forcing.
Suarez, Max J. (Editor); Takacs, Lawrence L.; Molod, Andrea; Wang, Tina
1994-01-01
This technical report documents Version 1 of the Goddard Earth Observing System (GEOS) General Circulation Model (GCM). The GEOS-1 GCM is being used by NASA's Data Assimilation Office (DAO) to produce multiyear data sets for climate research. This report provides a documentation of the model components used in the GEOS-1 GCM, a complete description of model diagnostics available, and a User's Guide to facilitate GEOS-1 GCM experiments.
Ph.H.B.F. Franses (Philip Hans); R. Paap (Richard)
2004-01-01
textabstractThis book considers periodic time series models for seasonal data, characterized by parameters that differ across the seasons, and focuses on their usefulness for out-of-sample forecasting. Providing an up-to-date survey of the recent developments in periodic time series, the book
Koster, Randal D. (Editor); Bosilovich, Michael G.; Akella, Santha; Lawrence, Coy; Cullather, Richard; Draper, Clara; Gelaro, Ronald; Kovach, Robin; Liu, Qing; Molod, Andrea; Norris, Peter; Wargan, Krzysztof; Chao, Winston; Reichle, Rolf; Takacs, Lawrence; Todling, Ricardo; Vikhliaev, Yury; Bloom, Steve; Collow, Allison; Partyka, Gary; Labow, Gordon; Pawson, Steven; Reale, Oreste; Schubert, Siegfried; Suarez, Max
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.
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.
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
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.
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
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).
Suarez, Max J. (Editor); Takacs, Lawrence L.
1995-01-01
A detailed description of the numerical formulation of Version 2 of the ARIES/GEOS 'dynamical core' is presented. This code is a nearly 'plug-compatible' dynamics for use in atmospheric general circulation models (GCMs). It is a finite difference model on a staggered latitude-longitude C-grid. It uses second-order differences for all terms except the advection of vorticity by the rotation part of the flow, which is done at fourth-order accuracy. This dynamical core is currently being used in the climate (ARIES) and data assimilation (GEOS) GCMs at Goddard.
Suarez, Max J. (Editor); Pfaendtner, James; Bloom, Stephen; Lamich, David; Seablom, Michael; Sienkiewicz, Meta; Stobie, James; Dasilva, Arlindo
1995-01-01
This report describes the analysis component of the Goddard Earth Observing System, Data Assimilation System, Version 1 (GEOS-1 DAS). The general features of the data assimilation system are outlined, followed by a thorough description of the statistical interpolation algorithm, including specification of error covariances and quality control of observations. We conclude with a discussion of the current status of development of the GEOS data assimilation system. The main components of GEOS-1 DAS are an atmospheric general circulation model and an Optimal Interpolation algorithm. The system is cycled using the Incremental Analysis Update (IAU) technique in which analysis increments are introduced as time independent forcing terms in a forecast model integration. The system is capable of producing dynamically balanced states without the explicit use of initialization, as well as a time-continuous representation of non- observables such as precipitation and radiational fluxes. This version of the data assimilation system was used in the five-year reanalysis project completed in April 1994 by Goddard's Data Assimilation Office (DAO) Data from this reanalysis are available from the Goddard Distributed Active Center (DAAC), which is part of NASA's Earth Observing System Data and Information System (EOSDIS). For information on how to obtain these data sets, contact the Goddard DAAC at (301) 286-3209, EMAIL daac@gsfc.nasa.gov.
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…
Medical Equipment Maintenance Programme Overview WHO Medical device technical series
Organization, World Health
2011-01-01
WHO and partners have been working towards devising an agenda an action plan tools and guidelines to increase access to appropriate medical devices. This document is part of a series of reference documents being developed for use at the country level. The series will include the following subject areas: . policy framework for health technology . medical device regulations . health technology assessment . health technology management . needs assessment of medical devices . medical device procurement . medical equipment donations . medical equipment inventory management . medical equipment maint
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...
A Simple Fuzzy Time Series Forecasting Model
DEFF Research Database (Denmark)
Ortiz-Arroyo, Daniel
2016-01-01
In this paper we describe a new ﬁrst order fuzzy time series forecasting model. We show that our automatic fuzzy partitioning method provides an accurate approximation to the time series that when combined with rule forecasting and an OWA operator improves forecasting accuracy. Our model does...... not attempt to provide the best results in comparison with other forecasting methods but to show how to improve ﬁrst order models using simple techniques. However, we show that our ﬁrst order model is still capable of outperforming some more complex higher order fuzzy time series models....
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
Technical Assistance to Schools and School Support Organizations. Closing the Achievement Gap Series
Read, Tory
2009-01-01
This is the final report in Casey's "Closing the Achievement Gap" series that highlights stories, results, and lessons learned from more than a decade of education investing. This publication presents an overview of the Foundation's support to four organizations, each of which targeted technical assistance to a specific audience. It summarizes…
Manhattan Project Technical Series The Chemistry of Uranium (I) Chapters 1-10
Energy Technology Data Exchange (ETDEWEB)
Rabinowitch, E. I. [Argonne National Laboratory (ANL), Argonne, IL (United States); Katz, J. J. [Argonne National Laboratory (ANL), Argonne, IL (United States)
1946-09-30
This constitutes Chapters 1 through 10. inclusive, of The Survey Volume on Uranium Chemistry prepared for the Manhattan Project Technical Series. It is issued fop purposes of review and criticism. It was decided in the Editorial Board meeting on June 11, 1946, that all comments must be communicated to the volume editors at The Argonne National Laboratory within one month after receiving this draft.
THE FOURIER SERIES MODEL IN MAP ANALYSIS.
During the past several years the double Fourier Series has been applied to the analysis of contour-type maps as an alternative to the more commonly...used polynomial model. The double Fourier Series has high potential in the study of areal variations, inasmuch as a succession of trend maps based on...and it is shown that the double Fourier Series can be used to summarize the directional properties of areally-distributed data. An Appendix lists
Modelling and Analysing Socio-Technical Systems
DEFF Research Database (Denmark)
Aslanyan, Zaruhi; Ivanova, Marieta Georgieva; Nielson, Flemming
2015-01-01
with social engineering. Due to this combination of attack steps on technical and social levels, risk assessment in socio-technical systems is complex. Therefore, established risk assessment methods often abstract away the internal structure of an organisation and ignore human factors when modelling...... and assessing attacks. In our work we model all relevant levels of socio-technical systems, and propose evaluation techniques for analysing the security properties of the model. Our approach simplifies the identification of possible attacks and provides qualified assessment and ranking of attacks based...... on the expected impact. We demonstrate our approach on a home-payment system. The system is specifically designed to help elderly or disabled people, who may have difficulties leaving their home, to pay for some services, e.g., care-taking or rent. The payment is performed using the remote control of a television...
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).
FORENSIC COMPUTING MODELS: TECHNICAL OVERVIEW
Directory of Open Access Journals (Sweden)
Gulshan Shrivastava
2012-05-01
Full Text Available In this paper, we deal with introducing a technique of digital forensics for reconstruction of events or evidences after the commitment of a crime through any of the digital devices. It shows a clear transparency between Computer Forensics and Digital Forensics and gives a brief description about the classification of Digital Forensics. It has also been described that how the emergences of various digital forensic models help digital forensic practitioners and examiners in doing digital forensics. Further, discussed Merits and Demerits of the required models and review of every major model.
Nonlinear time series modelling: an introduction
Simon M. Potter
1999-01-01
Recent developments in nonlinear time series modelling are reviewed. Three main types of nonlinear models are discussed: Markov Switching, Threshold Autoregression and Smooth Transition Autoregression. Classical and Bayesian estimation techniques are described for each model. Parametric tests for nonlinearity are reviewed with examples from the three types of models. Finally, forecasting and impulse response analysis is developed.
A Series Dissertation on Tianwan Nuclear Power Station——Technical Characteristics
Institute of Scientific and Technical Information of China (English)
Li Qiankun
2007-01-01
This is the 3 rd topic of "A series dissertation on Tianwan Nuclear Power Station", which focuses on the technical characteristics. The type of this Nuclear Power Station is a Russian AES-91 (WWER-1000/V428) pressurized water reactor (PWR) nuclear power unit. It is an improved concept by the Russian Saint Petersburg Nuclear Power Research and Design Institute based on the experiences in design, construction and operation of WWER-1000/V320 units. Since WWER-1000/V320 is a mature type which has more than 260reactor-year operation experiences, the author guesses the technical characteristics of WWER-1000/V320 are well known, thus the comparison of their technical characteristics is described bellow.
An Excel™ model of a radioactive series
Andrews, D. G. H.
2009-01-01
A computer model of the decay of a radioactive series, written in Visual Basic in Excel™, is presented. The model is based on the random selection of cells in an array. The results compare well with the theoretical equations. The model is a useful tool in teaching this aspect of radioactivity.
Energy Technology Data Exchange (ETDEWEB)
Ohmer, M.C.; Wollan, J.J.; Lawson, L.O.
1975-07-01
This report summarizes the Air Force superconducting wire applications, the goals of future superconducting materials development, the state of the art theories of ac loss in superconductors, and the results of hysteretic loss measurements on a series of niobium--titanium multifilamentary wires. Expressions were developed for magnetization and hysteretic loss for half cycle and full cycle for rod geometries for a critical state model with critical current inversely proportional to field. Bulk effects with surface like character are discussed along with surface shielding fields and the demagnetizing factor. The loss expressions of various models are compared to experimental loss. Universal loss curves constructed from experimental loss curves by appropriate normalization are obtained and used to predict loss accurately. (GRA)
Simple facet joint repair with dynamic pedicular system: Technical note and case series
Ali Fahir Ozer; Tuncer Suzer; Mehdi Sasani; Tunc Oktenoglu; Phillip Cezayirli; Hosein Jafari Marandi; Deniz Ufuk Erbulut
2015-01-01
Simple facet joint repair with dynamic pedicular system: Technical note and case series Ali Ozer, Tuncer Suzer, Mehdi Sasani, Tunc Oktenoglu, Phillip Cezayirli and Hosein Marandi Journal of Craniovertebral Junction and Spine. 6.2 (April-June 2015): p65. Copyright: COPYRIGHT 2015 Medknow Publications and Media Pvt. Ltd. http://www.jcvjs.com/ Full Text: Byline: Ali. Ozer, Tuncer. Suzer, Mehdi. Sasani, Tunc. Oktenoglu, Phillip. Cezayirli, Hosein. Marandi, Deniz. Erbulut Purpose: Facet joints are...
Manhattan Project Technical Series The Chemistry of Uranium (I) Chapters 1-10
Energy Technology Data Exchange (ETDEWEB)
Rabinowitch, E. I. [Argonne National Laboratory (ANL), Argonne, IL (United States); Katz, J. J. [Argonne National Laboratory (ANL), Argonne, IL (United States)
1946-09-30
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.
GOES-R Ground Segment Technical Reference Model
Krause, R. G.; Burnett, M.; Khanna, R.
2012-12-01
NOAA Geostationary Environmental Operational Satellite -R Series (GOES-R) Ground Segment Project (GSP) has developed a Technical Reference Model (TRM) to support the documentation of technologies that could form the basis for a set of requirements that could support the evolution towards a NESDIS enterprise ground system. Architecture and technologies in this TRM can be applied or extended to other ground systems for planning and development. The TRM maps GOES-R technologies to the Office of Management and Budget's (OMB) Federal Enterprise Architecture (FEA) Consolidated Reference Model (CRM) V 2.3 Technical Services Standard (TSS). The FEA TRM categories are the framework for the GOES-R TRM. This poster will present the GOES-R TRM.
Feature Matching in Time Series Modelling
Xia, Yingcun
2011-01-01
Using a time series model to mimic an observed time series has a long history. However, with regard to this objective, conventional estimation methods for discrete-time dynamical models are frequently found to be wanting. In the absence of a true model, we prefer an alternative approach to conventional model fitting that typically involves one-step-ahead prediction errors. Our primary aim is to match the joint probability distribution of the observable time series, including long-term features of the dynamics that underpin the data, such as cycles, long memory and others, rather than short-term prediction. For want of a better name, we call this specific aim {\\it feature matching}. The challenges of model mis-specification, measurement errors and the scarcity of data are forever present in real time series modelling. In this paper, by synthesizing earlier attempts into an extended-likelihood, we develop a systematic approach to empirical time series analysis to address these challenges and to aim at achieving...
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
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...
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
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
Modeling forest industry in Sweden. Technical documentation
Energy Technology Data Exchange (ETDEWEB)
Nystroem, Ingrid [Chalmers Univ. of Technology, Goeteborg (Sweden). Div. of Energy Systems Technology
2001-02-01
At the division of Energy Systems Technology at Chalmers University of Technology a study of energy and material flows in the Swedish forest industry has been made. The study includes analysis of potential long-term development paths for the forest industry and their impact on energy flows and energy related material flows in the forest industry. Within this study a forest industry model and a number of forest industry scenarios have been developed. This report presents a technical description of the constructed model, detailed scenario data and complete results tables for the scenario runs. The report does not include any discussion or analysis of model, input data or results.
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.
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...... and a stationary component. Further, we apply both kinds of indicator saturation to detect additive outliers and level shifts in the industrial production series in five European countries....
Modeling noisy time series Physiological tremor
Timmer, J
1998-01-01
Empirical time series often contain observational noise. We investigate the effect of this noise on the estimated parameters of models fitted to the data. For data of physiological tremor, i.e. a small amplitude oscillation of the outstretched hand of healthy subjects, we compare the results for a linear model that explicitly includes additional observational noise to one that ignores this noise. We discuss problems and possible solutions for nonlinear deterministic as well as nonlinear stochastic processes. Especially we discuss the state space model applicable for modeling noisy stochastic systems and Bock's algorithm capable for modeling noisy deterministic systems.
Forecasting with nonlinear time series models
DEFF Research Database (Denmark)
Kock, Anders Bredahl; Teräsvirta, Timo
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...... 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...
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 & Je...... & Jenkins is performed in a more modern way using the computer resources which are now available...
Time series modeling for automatic target recognition
Sokolnikov, Andre
2012-05-01
Time series modeling is proposed for identification of targets whose images are not clearly seen. The model building takes into account air turbulence, precipitation, fog, smoke and other factors obscuring and distorting the image. The complex of library data (of images, etc.) serving as a basis for identification provides the deterministic part of the identification process, while the partial image features, distorted parts, irrelevant pieces and absence of particular features comprise the stochastic part of the target identification. The missing data approach is elaborated that helps the prediction process for the image creation or reconstruction. The results are provided.
Construct Method of Predicting Satisfaction Model Based on Technical Characteristics
Institute of Scientific and Technical Information of China (English)
YANG Xiao-an; DENG Qian; SUN Guan-long; ZHANG Wei-she
2011-01-01
In order to construct objective relatively mapping relationship model between customer requirements and product technical characteristics, a novel approach based on customer satisfactions information digging from case products and satisfaction information of expert technical characteristics was put forward in this paper. Technical characteristics evaluation values were expressed by rough number, and technical characteristics target sequence was determined on the basis of efficiency, cost type and middle type in this method. Use each calculated satisfactions of customers and technical characteristics as input and output elements to construct BP network model. And we use MATLAB software to simulate this BP network model based on the case of electric bicycles.
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…
Forecasting Daily Time Series using Periodic Unobserved Components Time Series Models
Koopman, Siem Jan; Ooms, Marius
2004-01-01
We explore a periodic analysis in the context of unobserved components time series models that decompose time series into components of interest such as trend and seasonal. Periodic time series models allow dynamic characteristics to depend on the period of the year, month, week or day. In the stand
Forecasting Daily Time Series using Periodic Unobserved Components Time Series Models
Koopman, Siem Jan; Ooms, Marius
2004-01-01
We explore a periodic analysis in the context of unobserved components time series models that decompose time series into components of interest such as trend and seasonal. Periodic time series models allow dynamic characteristics to depend on the period of the year, month, week or day. In the
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
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.
Adapting the Kirkpatrick Model to Technical Communication Products and Services.
Carliner, Saul
1997-01-01
Proposes a four-level model for adapting the Kirkpatrick model of training evaluation to suit technical manuals and services assessing: (1) user satisfaction; (2) user performance; (3) client performance; and (4) client satisfaction. Discusses assessing of the value of work, limitations in evaluating technical communication products, and the…
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.
Time series models of symptoms in schizophrenia.
Tschacher, Wolfgang; Kupper, Zeno
2002-12-15
The symptom courses of 84 schizophrenia patients (mean age: 24.4 years; mean previous admissions: 1.3; 64% males) of a community-based acute ward were examined to identify dynamic patterns of symptoms and to investigate the relation between these patterns and treatment outcome. The symptoms were monitored by systematic daily staff ratings using a scale composed of three factors: psychoticity, excitement, and withdrawal. Patients showed moderate to high symptomatic improvement documented by effect size measures. Each of the 84 symptom trajectories was analyzed by time series methods using vector autoregression (VAR) that models the day-to-day interrelations between symptom factors. Multiple and stepwise regression analyses were then performed on the basis of the VAR models. Two VAR parameters were found to be associated significantly with favorable outcome in this exploratory study: 'withdrawal preceding a reduction of psychoticity' as well as 'excitement preceding an increase of withdrawal'. The findings were interpreted as generating hypotheses about how patients cope with psychotic episodes.
Genetic programming-based chaotic time series modeling
Institute of Scientific and Technical Information of China (English)
张伟; 吴智铭; 杨根科
2004-01-01
This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) algorithm is used for Nonlinear Parameter Estimation (NPE) of dynamic model structures. In addition, GPM integrates the results of Nonlinear Time Series Analysis (NTSA) to adjust the parameters and takes them as the criteria of established models. Experiments showed the effectiveness of such improvements on chaotic time series modeling.
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…
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 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.
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.
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
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.
Technical Considerations in Magnetic Analogue Models
Adams, Patrick W M
2016-01-01
The analogy between vorticity and magnetic fields has been a subject of interest to researchers for a considerable period of time, mainly because of the structural similarities between the systems of equations that govern the evolution of the two fields. We recently presented the analysis of magnetic fields and hydrodynamics vorticity fields and argued for a formal theory of analogue magnetism. This article provides in depth technical details of the relevant considerations for the simulation procedures and extends the analyses to a range of fluids.
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.
Modeling Socio-technical Systems with AgentSpring
Chmieliauskas, A.; Chappin, E.J.L.; Dijkema, G.P.J.
2012-01-01
AgentSpring is a new agent-based modeling framework especially suited to model and simulate complex socio-technical systems, such as energy markets or transport infrastructures. Common problems encountered when modeling and analyzing such systems are how to represent the variety of facts that
Modeling Socio-technical Systems with AgentSpring
Chmieliauskas, A.; Chappin, E.J.L.; Dijkema, G.P.J.
2012-01-01
AgentSpring is a new agent-based modeling framework especially suited to model and simulate complex socio-technical systems, such as energy markets or transport infrastructures. Common problems encountered when modeling and analyzing such systems are how to represent the variety of facts that descri
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…
A multivariate approach to modeling univariate seasonal time series
Ph.H.B.F. Franses (Philip Hans)
1994-01-01
textabstractA seasonal time series can be represented by a vector autoregressive model for the annual series containing the seasonal observations. This model allows for periodically varying coefficients. When the vector elements are integrated, the maximum likelihood cointegration method can be used
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...
Genetic programming-based chaotic time series modeling
Institute of Scientific and Technical Information of China (English)
张伟; 吴智铭; 杨根科
2004-01-01
This paper proposes a Genetic Programming-Based Modeling(GPM)algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space,and the Particle Swarm Optimization(PSO)algorithm is used for Nonlinear Parameter Estimation(NPE)of dynamic model structures. In addition,GPM integrates the results of Nonlinear Time Series Analysis(NTSA)to adjust the parameters and takes them as the criteria of established models.Experiments showed the effectiveness of such improvements on chaotic time series modeling.
Modelling Social-Technical Attacks with Timed Automata
DEFF Research Database (Denmark)
David, Nicolas; David, Alexandre; Hansen, Rene Rydhof
2015-01-01
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.......Attacks on a system often exploit vulnerabilities that arise from human behaviour or other human activity. Attacks of this type, so-called socio-technical attacks, cover everything from social engineering to insider attacks, and they can have a devastating impact on an unprepared organisation....... 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...
Modeling Persistence In Hydrological Time Series Using Fractional Differencing
Hosking, J. R. M.
1984-12-01
The class of autoregressive integrated moving average (ARIMA) time series models may be generalized by permitting the degree of differencing d to take fractional values. Models including fractional differencing are capable of representing persistent series (d > 0) or short-memory series (d = 0). The class of fractionally differenced ARIMA processes provides a more flexible way than has hitherto been available of simultaneously modeling the long-term and short-term behavior of a time series. In this paper some fundamental properties of fractionally differenced ARIMA processes are presented. Methods of simulating these processes are described. Estimation of the parameters of fractionally differenced ARIMA models is discussed, and an approximate maximum likelihood method is proposed. The methodology is illustrated by fitting fractionally differenced models to time series of streamflows and annual temperatures.
LIFE DISTRIBUTION OF SERIES UNDER THE SUCCESSIVE DAMAGE MODEL
Institute of Scientific and Technical Information of China (English)
WANG Dongqian; C. D. Lai; LI Guoying
2003-01-01
We analyse further the reliability behaviour of series and parallel systems in the successive damage model initiated by Downton. The results are compared with those obtained for other models with different bivariate distributions.
General expression for linear and nonlinear time series models
Institute of Scientific and Technical Information of China (English)
Ren HUANG; Feiyun XU; Ruwen CHEN
2009-01-01
The typical time series models such as ARMA, AR, and MA are founded on the normality and stationarity of a system and expressed by a linear difference equation; therefore, they are strictly limited to the linear system. However, some nonlinear factors are within the practical system; thus, it is difficult to fit the model for real systems with the above models. This paper proposes a general expression for linear and nonlinear auto-regressive time series models (GNAR). With the gradient optimization method and modified AIC information criteria integrated with the prediction error, the parameter estimation and order determination are achieved. The model simulation and experiments show that the GNAR model can accurately approximate to the dynamic characteristics of the most nonlinear models applied in academics and engineering. The modeling and prediction accuracy of the GNAR model is superior to the classical time series models. The proposed GNAR model is flexible and effective.
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)...
DEFF Research Database (Denmark)
Madsen, Henrik; Pearson, Charles P.; Rosbjerg, Dan
1997-01-01
Two regional estimation schemes, based on, respectively, partial duration series (PDS) and annual maximum series (AMS), are compared. The PDS model assumes a generalized Pareto (GP) distribution for modeling threshold exceedances corresponding to a generalized extreme value (GEV) distribution...... for annual maxima. First, the accuracy of PDS/GP and AMS/GEV regional index-flood T-year event estimators are compared using Monte Carlo simulations. For estimation in typical regions assuming a realistic degree of heterogeneity, the PDS/GP index-flood model is more efficient. The regional PDS and AMS...
Agent-based modelling of socio-technical systems
van Dam, Koen H; Lukszo, Zofia
2012-01-01
Here is a practical introduction to agent-based modelling of socio-technical systems, based on methodology developed at TU Delft, which has been deployed in a number of case studies. Offers theory, methods and practical steps for creating real-world models.
Technical Assistance Viewed with Bhola's Mega Model of Planned Change.
Chiappetta, Michael
1982-01-01
The practical usefulness of H. S. Bhola's models for planned change is questioned through speculations on problems that would have arisen if the models had been applied in arranging an Inter-American Development Bank technical assistance project in Barbados. (PP)
Lagrangian Time Series Models for Ocean Surface Drifter Trajectories
Sykulski, Adam M; Lilly, Jonathan M; Danioux, Eric
2016-01-01
This paper proposes stochastic models for the analysis of ocean surface trajectories obtained from freely-drifting satellite-tracked instruments. The proposed time series models are used to summarise large multivariate datasets and infer important physical parameters of inertial oscillations and other ocean processes. Nonstationary time series methods are employed to account for the spatiotemporal variability of each trajectory. Because the datasets are large, we construct computationally efficient methods through the use of frequency-domain modelling and estimation, with the data expressed as complex-valued time series. We detail how practical issues related to sampling and model misspecification may be addressed using semi-parametric techniques for time series, and we demonstrate the effectiveness of our stochastic models through application to both real-world data and to numerical model output.
Time series modelling of overflow structures
DEFF Research Database (Denmark)
Carstensen, J.; Harremoës, P.
1997-01-01
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......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...
Stochastic modelling of regional archaeomagnetic series
Hellio, G; Bouligand, C; Jault, D
2015-01-01
SUMMARY We report a new method to infer continuous time series of the declination, inclination and intensity of the magnetic field from archeomagnetic data. Adopting a Bayesian perspective, we need to specify a priori knowledge about the time evolution of the magnetic field. It consists in a time correlation function that we choose to be compatible with present knowledge about the geomagnetic time spectra. The results are presented as distributions of possible values for the declination, inclination or intensity. We find that the methodology can be adapted to account for the age uncertainties of archeological artefacts and we use Markov Chain Monte Carlo to explore the possible dates of observations. We apply the method to intensity datasets from Mari, Syria and to intensity and directional datasets from Paris, France. Our reconstructions display more rapid variations than previous studies and we find that the possible values of geomagnetic field elements are not necessarily normally distributed. Another outp...
The use of synthetic input sequences in time series modeling
Energy Technology Data Exchange (ETDEWEB)
Oliveira, Dair Jose de [Programa de Pos-Graduacao em Engenharia Eletrica, Universidade Federal de Minas Gerais, Av. Antonio Carlos 6627, 31.270-901 Belo Horizonte, MG (Brazil); Letellier, Christophe [CORIA/CNRS UMR 6614, Universite et INSA de Rouen, Av. de l' Universite, BP 12, F-76801 Saint-Etienne du Rouvray cedex (France); Gomes, Murilo E.D. [Programa de Pos-Graduacao em Engenharia Eletrica, Universidade Federal de Minas Gerais, Av. Antonio Carlos 6627, 31.270-901 Belo Horizonte, MG (Brazil); Aguirre, Luis A. [Programa de Pos-Graduacao em Engenharia Eletrica, Universidade Federal de Minas Gerais, Av. Antonio Carlos 6627, 31.270-901 Belo Horizonte, MG (Brazil)], E-mail: aguirre@cpdee.ufmg.br
2008-08-04
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.
The use of synthetic input sequences in time series modeling
de Oliveira, Dair José; Letellier, Christophe; Gomes, Murilo E. D.; Aguirre, Luis A.
2008-08-01
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.
United Nations Industrial Development Organization, Vienna (Austria).
The need to develop managerial and technical personnel in the cement, fertilizer, pulp and paper, sugar, leather and shoe, glass, and metal processing industries of various nations was studied, with emphasis on necessary steps in developing nations to relate occupational requirements to technology, processes, and scale of output. Estimates were…
Some technical issues in geometric modeling
Energy Technology Data Exchange (ETDEWEB)
Peterson, D.P.
1983-01-01
The full impact of CAD/CAM will not be felt until geometric modeling systems support dimensioning and tolerancing, have sophisticated user interfaces, and are capable of routinely handling many representation conversions. The attainment of these capabilities requires a joint effort among users, implementors, and theoreticians of geometric modeling.
Ruin Probability in Linear Time Series Model
Institute of Scientific and Technical Information of China (English)
ZHANG Lihong
2005-01-01
This paper analyzes a continuous time risk model with a linear model used to model the claim process. The time is discretized stochastically using the times when claims occur, using Doob's stopping time theorem and martingale inequalities to obtain expressions for the ruin probability as well as both exponential and non-exponential upper bounds for the ruin probability for an infinite time horizon. Numerical results are included to illustrate the accuracy of the non-exponential bound.
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.
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.
Technical intelligence in animals: the kea model.
Huber, Ludwig; Gajdon, Gyula K
2006-10-01
The ability to act on information flexibly is one of the cornerstones of intelligent behavior. As particularly informative example, tool-oriented behavior has been investigated to determine to which extent nonhuman animals understand means-end relations, object affordances, and have specific motor skills. Even planning with foresight, goal-directed problem solving and immediate causal inference have been a focus of research. However, these cognitive abilities may not be restricted to tool-using animals but may be found also in animals that show high levels of curiosity, object exploration and manipulation, and extractive foraging behavior. The kea, a New Zealand parrot, is a particularly good example. We here review findings from laboratory experiments and field observations of keas revealing surprising cognitive capacities in the physical domain. In an experiment with captive keas, the success rate of individuals that were allowed to observe a trained conspecific was significantly higher than that of naive control subjects due to their acquisition of some functional understanding of the task through observation. In a further experiment using the string-pulling task, a well-probed test for means-end comprehension, we found the keas finding an immediate solution that could not be improved upon in nine further trials. We interpreted their performance as insightful in the sense of being sensitive of the relevant functional properties of the task and thereby producing a new adaptive response without trial-and-error learning. Together, these findings contribute to the ongoing debate on the distribution of higher cognitive skills in the animal kingdom by showing high levels of sensorimotor intelligence in animals that do not use tools. In conclusion, we suggest that the 'Technical intelligence hypothesis' (Byrne, Machiavellian intelligence II: extensions and evaluations, pp 289-211, 1997), which has been proposed to explain the origin of the ape/monkey grade-shift in
A generalized trigonometric series function model for determining ionospheric delay
Institute of Scientific and Technical Information of China (English)
YUAN Yunbin; OU Jikun
2004-01-01
A generalized trigonometric series function (GTSF) model, with an adjustable number of parameters, is proposed and analyzed to study ionosphere by using GPS, especially to provide ionospheric delay correction for single frequency GPS users. The preliminary results show that, in comparison with the trigonometric series function (TSF) model and the polynomial (POLY) model, the GTSF model can more precisely describe the ionospheric variation and more efficiently provide the ionospheric correction when GPS data are used to investigate or extract the earth's ionospheric total electron content. It is also shown that the GTSF model can further improve the precision and accuracy of modeling local ionospheric delays.
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...... model with ML estimation for large positive shape parameters. Since heavy-tailed distributions, corresponding to negative shape parameters, are far the most common in hydrology, the PDS model generally is to be preferred for at-site quantile estimation....... distribution for annual maxima. The performance of the two models in terms of the uncertainty of the T-year event estimator is evaluated in the cases of estimation with, respectively, the maximum likelihood (ML) method, the method of moments (MOM), and the method of probability weighted moments (PWM...
Fisher Information Framework for Time Series Modeling
Venkatesan, R C
2016-01-01
A robust prediction model invoking the Takens embedding theorem, whose \\textit{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 \\textit{working hypothesis} satisfy a time independent Schr\\"{o}dinger-like equation in a vector setting. The inference of i) the probability density function of the coefficients of the \\textit{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 defi...
Convergent series for lattice models with polynomial interactions
Ivanov, Aleksandr S.; Sazonov, Vasily K.
2017-01-01
The standard perturbative weak-coupling expansions in lattice models are asymptotic. The reason for this is hidden in the incorrect interchange of the summation and integration. However, substituting the Gaussian initial approximation of the perturbative expansions by a certain interacting model or regularizing original lattice integrals, one can construct desired convergent series. In this paper we develop methods, which are based on the joint and separate utilization of the regularization and new initial approximation. We prove, that the convergent series exist and can be expressed as re-summed standard perturbation theory for any model on the finite lattice with the polynomial interaction of even degree. We discuss properties of such series and study their applicability to practical computations on the example of the lattice ϕ4-model. We calculate expectation value using the convergent series, the comparison of the results with the Borel re-summation and Monte Carlo simulations shows a good agreement between all these methods.
Convergent series for lattice models with polynomial interactions
Ivanov, Aleksandr S
2016-01-01
The standard perturbative weak-coupling expansions in lattice models are asymptotic. The reason for this is hidden in the incorrect interchange of the summation and integration. However, substituting the Gaussian initial approximation of the perturbative expansions by a certain interacting model or regularizing original lattice integrals, one can construct desired convergent series. In this paper we develop methods, which are based on the joint and separate utilization of the regularization and new initial approximation. We prove, that the convergent series exist and can be expressed as the re-summed standard perturbation theory for any model on the finite lattice with the polynomial interaction of even degree. We discuss properties of such series and make them applicable to practical computations. The workability of the methods is demonstrated on the example of the lattice $\\phi^4$-model. We calculate the operator $\\langle\\phi_n^2\\rangle$ using the convergent series, the comparison of the results with the Bo...
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.
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...... unconditional skewness. We consider modelling the unconditional mean and variance using models that respond nonlinearly or asymmetrically to shocks. We investigate the implications of these models on the third-moment structure of the marginal distribution as well as conditions under which the unconditional...
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…
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
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
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...
Statistical modelling of agrometeorological time series by exponential smoothing
Murat, Małgorzata; Malinowska, Iwona; Hoffmann, Holger; Baranowski, Piotr
2016-01-01
Meteorological time series are used in modelling agrophysical processes of the soil-plant-atmosphere system which determine plant growth and yield. Additionally, long-term meteorological series are used in climate change scenarios. Such studies often require forecasting or projection of meteorological variables, eg the projection of occurrence of the extreme events. The aim of the article was to determine the most suitable exponential smoothing models to generate forecast using data on air temperature, wind speed, and precipitation time series in Jokioinen (Finland), Dikopshof (Germany), Lleida (Spain), and Lublin (Poland). These series exhibit regular additive seasonality or non-seasonality without any trend, which is confirmed by their autocorrelation functions and partial autocorrelation functions. The most suitable models were indicated by the smallest mean absolute error and the smallest root mean squared error.
An Excel[TM] Model of a Radioactive Series
Andrews, D. G. H.
2009-01-01
A computer model of the decay of a radioactive series, written in Visual Basic in Excel[TM], is presented. The model is based on the random selection of cells in an array. The results compare well with the theoretical equations. The model is a useful tool in teaching this aspect of radioactivity. (Contains 4 figures.)
With string model to time series forecasting
Pinčák, Richard; Bartoš, Erik
2015-01-01
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 ma...
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.
Analysis of GARCH modeling in financial markets: an approach based on technical analysis strategies
Directory of Open Access Journals (Sweden)
Mircea Cristian Gherman
2011-08-01
Full Text Available In this paper we performed an analysis in order the make an evidence of GARCH modeling on the performances of trading rules applied for a stock market index. Our study relays on the overlap between econometrical modeling, technical analysis and a simulation computing technique. The non-linear structures presented in the daily returns of the analyzed index and also in other financial series, together with the phenomenon of volatility clustering are premises for applying a GARCH model. In our approach the standardized GARCH innovations are resampled using the bootstrap method. On the simulated data are then applied technical analysis trading strategies. For all the simulated paths the “p-values” are computed in order to verify that the hypothesis concerning the goodness of fit for GARCH model on the BET index is accepted. The processed data with trading rules are showing evidence that GARCH model is a good choice for econometrical modeling of financial time series including the romanian exchange trade index.
Prediction Model of Sewing Technical Condition by Grey Neural Network
Institute of Scientific and Technical Information of China (English)
DONG Ying; FANG Fang; ZHANG Wei-yuan
2007-01-01
The grey system theory and the artificial neural network technology were applied to predict the sewing technical condition. The representative parameters, such as needle, stitch, were selected. Prediction model was established based on the different fabrics' mechanical properties that measured by KES instrument. Grey relevant degree analysis was applied to choose the input parameters of the neural network. The result showed that prediction model has good precision. The average relative error was 4.08% for needle and 4.25% for stitch.
M-X Environmental Technical Report. Social Model.
1980-12-22
Housing • Ccznaity Land Use and Infrastructure Ccmmanity Services and Facilities * Public Finance SECURITY CLASSIFICATION OF THIS PAGE(’When Data Entered...Housing4 o Community Land Use and Infrastructure 0 Community Services and Facilities 0 Public Finance -I . . . . . _ . .. .. .. .,... .. .. ... ’up Ip II...use, and community services model groups, while economic/demographic and public finance models are discussed in separate technical reports. 1.2 THE
Multiplicative ARMA models to generate hourly series of global irradiation
Energy Technology Data Exchange (ETDEWEB)
Mora-Lopez, L. [Universidad de Malaga (Spain). Dpto. Lenguajes y C. Computacion; Sidrach-de-Cardona, M. [Universidad de Malaga (Spain). Dpto. Fisica Aplicada
1998-11-01
A methodology to generate hourly series of global irradiation is proposed. The only input parameter which is required is the monthly mean value of daily global irradiation, which is available for most locations. The procedure to obtain new series is based on the use of a multiplicative autoregressive moving-average statistical model for time series with regular and seasonal components. The multiplicative nature of these models enables capture of the two types of relationships observed in recorded hourly series of global irradiation: on the one hand, the relationship between the value at one hour and the value at the previous hour; and on the other hand, the relationship between the value at one hour in one day and the value at the same hour in the previous day. In this paper the main drawback which arises when using these models to generate new series is solved: namely, the need for available recorded series in order to obtain the three parameters contained in the statistical ARMA model which is proposed (autoregressive coefficient, moving-average coefficient and variance of the error term). Specifically, expressions which enable estimation of these parameters using only monthly mean values of daily global irradiation are proposed in this paper. (author)
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
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.
Hybrid grey model to forecast monitoring series with seasonality
Institute of Scientific and Technical Information of China (English)
WANG Qi-jie; LIAO Xin-hao; ZHOU Yong-hong; ZOU Zheng-rong; ZHU Jian-jun; PENG Yue
2005-01-01
The grey forecasting model has been successfully applied to many fields. However, the precision of GM(1,1) model is not high. In order to remove the seasonal fluctuations in monitoring series before building GM(1,1) model, the forecasting series of GM(1,1) was built, and an inverse process was used to resume the seasonal fluctuations. Two deseasonalization methods were presented , i.e., seasonal index-based deseasonalization and standard normal distribution-based deseasonalization. They were combined with the GM(1,1) model to form hybrid grey models. A simple but practical method to further improve the forecasting results was also suggested. For comparison, a conventional periodic function model was investigated. The concept and algorithms were tested with four years monthly monitoring data. The results show that on the whole the seasonal index-GM(1,1) model outperform the conventional periodic function model and the conventional periodic function model outperform the SND-GM(1,1) model. The mean absolute error and mean square error of seasonal index-GM(1,1) are 30.69% and 54.53% smaller than that of conventional periodic function model, respectively. The high accuracy, straightforward and easy implementation natures of the proposed hybrid seasonal index-grey model make it a powerful analysis technique for seasonal monitoring series.
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
A multivariate heuristic model for fuzzy time-series forecasting.
Huarng, Kun-Huang; Yu, Tiffany Hui-Kuang; Hsu, Yu Wei
2007-08-01
Fuzzy time-series models have been widely applied due to their ability to handle nonlinear data directly and because no rigid assumptions for the data are needed. In addition, many such models have been shown to provide better forecasting results than their conventional counterparts. However, since most of these models require complicated matrix computations, this paper proposes the adoption of a multivariate heuristic function that can be integrated with univariate fuzzy time-series models into multivariate models. Such a multivariate heuristic function can easily be extended and integrated with various univariate models. Furthermore, the integrated model can handle multiple variables to improve forecasting results and, at the same time, avoid complicated computations due to the inclusion of multiple variables.
Transradial approach to treating endovascular cerebral aneurysms: Case series and technical note
Goland, Javier; Doroszuk, Gustavo Fabián; Garbugino, Silvia Lina; Ypa, María Paula
2017-01-01
Background: Several benefits have been described over the years of the transradial versus femoral endovascular approach to cardiac interventions. Consequently, its use has become habitual at most centers that perform cardiac catheterizations. This paper details a right transradial approach, incorporating a variety of coils or flow diverters, which can be utilized for the endovascular treatment of different cerebral aneurysms. Methods: From 2014 to 2016, we performed 40 endovascular procedures to treat cerebral aneurysms adopting the same right transradial approach. Five aneurysms were treated with flow diverters and 35 were treated with coils. Seven of these aneurisms were asymptomatic, whereas 33 had already ruptured. Results: Satisfactory treatment was achieved in all cases through the same approach in the absence of any complications. Conclusions: A right transradial approach may be satisfactory for the endovascular treatment of different cerebral aneurysms, including aneurysms in either hemisphere. This is the largest series of cerebral aneurysms treated through a transradial approach. PMID:28584676
AN EXPERT SYSTEM MODEL FOR THE SELECTION OF TECHNICAL PERSONNEL
Directory of Open Access Journals (Sweden)
Emine COŞGUN
2005-03-01
Full Text Available In this study, a model has been developed for the selection of the technical personnel. In the model Visual Basic has been used as user interface, Microsoft Access has been utilized as database system and CLIPS program has been used as expert system program. The proposed model has been developed by utilizing expert system technology. In the personnel selection process, only the pre-evaluation of the applicants has been taken into consideration. Instead of replacing the expert himself, a decision support program has been developed to analyze the data gathered from the job application forms. The attached study will assist the expert to make faster and more accurate decisions.
Unit root modeling for trending stock market series
Directory of Open Access Journals (Sweden)
Afees A. Salisu
2016-06-01
Full Text Available In this paper, we examine how the unit root for stock market series should be modeled. We employ the Narayan and Liu (2015 trend GARCH-based unit root and its variants in order to more carefully capture the inherent statistical behavior of the series. We utilize daily, weekly and monthly data covering nineteen countries across the regions of America, Asia and Europe. We find that the nature of data frequency matters for unit root testing when dealing with stock market data. Our evidence also suggests that stock market data is better modeled in the presence of structural breaks, conditional heteroscedasticity and time trend.
Analyzing the Dynamics of Nonlinear Multivariate Time Series Models
Institute of Scientific and Technical Information of China (English)
DenghuaZhong; ZhengfengZhang; DonghaiLiu; StefanMittnik
2004-01-01
This paper analyzes the dynamics of nonlinear multivariate time series models that is represented by generalized impulse response functions and asymmetric functions. We illustrate the measures of shock persistences and asymmetric effects of shocks derived from the generalized impulse response functions and asymmetric function in bivariate smooth transition regression models. The empirical work investigates a bivariate smooth transition model of US GDP and the unemployment rate.
Modelling, simulation and inference for multivariate time series of counts
Veraart, Almut E. D.
2016-01-01
This article presents a new continuous-time modelling framework for multivariate time series of counts which have an infinitely divisible marginal distribution. The model is based on a mixed moving average process driven by L\\'{e}vy noise - called a trawl process - where the serial correlation and the cross-sectional dependence are modelled independently of each other. Such processes can exhibit short or long memory. We derive a stochastic simulation algorithm and a statistical inference meth...
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....
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....
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.
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.
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
Model and Variable Selection Procedures for Semiparametric Time Series Regression
Directory of Open Access Journals (Sweden)
Risa Kato
2009-01-01
Full Text Available Semiparametric regression models are very useful for time series analysis. They facilitate the detection of features resulting from external interventions. The complexity of semiparametric models poses new challenges for issues of nonparametric and parametric inference and model selection that frequently arise from time series data analysis. In this paper, we propose penalized least squares estimators which can simultaneously select significant variables and estimate unknown parameters. An innovative class of variable selection procedure is proposed to select significant variables and basis functions in a semiparametric model. The asymptotic normality of the resulting estimators is established. Information criteria for model selection are also proposed. We illustrate the effectiveness of the proposed procedures with numerical simulations.
A generalized exponential time series regression model for electricity prices
DEFF Research Database (Denmark)
Haldrup, Niels; Knapik, Oskar; Proietti, Tomasso
We consider the issue of modeling and forecasting daily electricity spot prices on the Nord Pool Elspot power market. We propose a method that can handle seasonal and non-seasonal persistence by modelling the price series as a generalized exponential process. As the presence of spikes can distort...... the estimation of the dynamic structure of the series we consider an iterative estimation strategy which, conditional on a set of parameter estimates, clears the spikes using a data cleaning algorithm, and reestimates the parameters using the cleaned data so as to robustify the estimates. Conditional...... 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...
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
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...
Transoral robotic approach to parapharyngeal space tumors: Case series and technical limitations.
Boyce, Brian J; Curry, Joseph M; Luginbuhl, Adam; Cognetti, David M
2016-08-01
The transoral robotic approach to parapharyngeal space (PPS) tumors is a new technique with limited data available on its feasibility, safety, and efficacy. We analyzed our experience with transoral robotic excisions of PPS tumors to evaluate the safety and efficacy of this technique. Retrospective chart analysis at tertiary academic medical center. From July 2010 to June 2014, 17 patients who had transoral robotic excision of PPS tumors were included in the study. Our cohort had an average age of 61.6 years and was 52.9% male. All patients had successful removal of their PPS tumors, and the average size of the tumors was 27.3 cm(3) (range 2-80 cm(3) ). Two cases (11.7%) required a cervical incision to assist with tumor removal. The average total operative time was 140.5 minutes. Two PPS PAs had focal areas of capsule rupture and one was fragmented. The average length of stay was 1.8 days (range 1-7 days), and all patients were discharged on an oral diet. Three patients experienced complications. There was no clinical or radiographic evidence of recurrence. This is the largest single-institution case series of transoral robotic approaches to PPS tumors. We demonstrate that this approach is feasible and safe but also note limitations of the robotic approaches for tumors on the far lateral and superior areas of the PPS, which required transcervical assistance. There were no patients who demonstrated recurrent tumor either radiographically or clinically. 4. Laryngoscope, 126:1776-1782, 2016. © 2016 The American Laryngological, Rhinological and Otological Society, Inc.
Deriving dynamic marketing effectiveness from econometric time series models
C. Horváth (Csilla); Ph.H.B.F. Franses (Philip Hans)
2003-01-01
textabstractTo understand the relevance of marketing efforts, it has become standard practice to estimate the long-run and short-run effects of the marketing-mix, using, say, weekly scanner data. A common vehicle for this purpose is an econometric time series model. Issues that are addressed in the
Convergent series for lattice models with polynomial interactions
Directory of Open Access Journals (Sweden)
Aleksandr S. Ivanov
2017-01-01
Full Text Available The standard perturbative weak-coupling expansions in lattice models are asymptotic. The reason for this is hidden in the incorrect interchange of the summation and integration. However, substituting the Gaussian initial approximation of the perturbative expansions by a certain interacting model or regularizing original lattice integrals, one can construct desired convergent series. In this paper we develop methods, which are based on the joint and separate utilization of the regularization and new initial approximation. We prove, that the convergent series exist and can be expressed as re-summed standard perturbation theory for any model on the finite lattice with the polynomial interaction of even degree. We discuss properties of such series and study their applicability to practical computations on the example of the lattice ϕ4-model. We calculate 〈ϕn2〉 expectation value using the convergent series, the comparison of the results with the Borel re-summation and Monte Carlo simulations shows a good agreement between all these methods.
Combined forecasts from linear and nonlinear time series models
N. Terui (Nobuhiko); H.K. van Dijk (Herman)
1999-01-01
textabstractCombined forecasts from a linear and a nonlinear model are investigated for time series with possibly nonlinear characteristics. The forecasts are combined by a constant coefficient regression method as well as a time varying method. The time varying method allows for a locally (non)line
Sparse time series chain graphical models for reconstructing genetic networks
Abegaz, Fentaw; Wit, Ernst
2013-01-01
We propose a sparse high-dimensional time series chain graphical model for reconstructing genetic networks from gene expression data parametrized by a precision matrix and autoregressive coefficient matrix. We consider the time steps as blocks or chains. The proposed approach explores patterns of co
Modeling irregularly spaced residual series as a continuous stochastic process
Von Asmuth, J.R.; Bierkens, M.F.P.
2005-01-01
In this paper, the background and functioning of a simple but effective continuous time approach for modeling irregularly spaced residual series is presented. The basic equations were published earlier by von Asmuth et al. (2002), who used them as part of a continuous time transfer function noise mo
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...
Stochastic Volatility Model and Technical Analysis of Stock Price
Institute of Scientific and Technical Information of China (English)
Wei LIU; Wei An ZHENG
2011-01-01
In the stock market, some popular technical analysis indicators (e.g. Bollinger Bands, RSI,ROC, ...) are widely used by traders. They use the daily (hourly, weekly, ...) stock prices as samples of certain statistics and use the observed relative frequency to show the validity of those well-knownindicators. However, those samples are not independent, so the classical sample survey theory does not apply. In earlier research, we discussed the law of large numbers related to those observations when one assumes Black-Scholes' stock price model. In this paper, we extend the above results to the more popular stochastic volatility model.
M-X Environmental Technical Report. Public Finance Model.
1980-12-22
7AD-A095 802 HENNINGSON DURHAM AND RICHARDSON SANTA 1BAR1BARA CA - UF/A 16/1 MA -x E VIRONMENT L fECHN AL REPORT. PUBLIC FINANCE MODEL. U) DEC A0...CATALOG NUMBER 4. TITLE (and Subtitle) 5. PERIOD COVERED-........... . Final1 M-X Environmental Technical Report, Public Finance Model , 6. PERFORMING ORG...KEY WORDS (Continue on reverse side If necessary and Identify by block number) Public Finance Texas iting Analysis Nevada New Mexico viromnental Report
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.
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.
A Comparative Study of Portmanteau Tests for Univariate Time Series Models
Directory of Open Access Journals (Sweden)
Sohail Chand
2006-07-01
Full Text Available Time series model diagnostic checking is the most important stage of time series model building. In this paper the comparison among several suggested diagnostic tests has been made using the simulation time series data.
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.
Calorimetric measurement and modelling of the equivalent series of capacitors
Seguin, B.; Gosse, J. P.; Ferrieux, J. P.
1999-12-01
The equivalent series resistance of polypropylene capacitors has been determined under rated voltage, in the range 1 kHz 1 MHz, between 220 K and 370 K by a calorimetric technique. The original feature of this determination of capacitor losses lies in the use of the isothermal calorimetry and in the measurement of an electrical power and not of a temperature increase. The frequency dependence of the equivalent series resistance, at various temperatures, enables to separate the losses in the conducting material from those in the dielectric and to get their respective variations as a function of frequency and temperature. These variations of the equivalent series resistance with frequency at a given temperature have been reproduced by using an equivalent circuit composed of resistors, inductors and capacitors. This model has been verified for non-sinusoidal waveforms such as those met with in a filtering circuit and is used to evaluate by simulation the losses of the capacitor.
A refined fuzzy time series model for stock market forecasting
Jilani, Tahseen Ahmed; Burney, Syed Muhammad Aqil
2008-05-01
Time series models have been used to make predictions of stock prices, academic enrollments, weather, road accident casualties, etc. In this paper we present a simple time-variant fuzzy time series forecasting method. The proposed method uses heuristic approach to define frequency-density-based partitions of the universe of discourse. We have proposed a fuzzy metric to use the frequency-density-based partitioning. The proposed fuzzy metric also uses a trend predictor to calculate the forecast. The new method is applied for forecasting TAIEX and enrollments’ forecasting of the University of Alabama. It is shown that the proposed method work with higher accuracy as compared to other fuzzy time series methods developed for forecasting TAIEX and enrollments of the University of Alabama.
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 FORECASTING WITH MULTIPLE CANDIDATE MODELS: SELECTING OR COMBINING?
Institute of Scientific and Technical Information of China (English)
YU Lean; WANG Shouyang; K. K. Lai; Y.Nakamori
2005-01-01
Various mathematical models have been commonly used in time series analysis and forecasting. In these processes, academic researchers and business practitioners often come up against two important problems. One is whether to select an appropriate modeling approach for prediction purposes or to combine these different individual approaches into a single forecast for the different/dissimilar modeling approaches. Another is whether to select the best candidate model for forecasting or to mix the various candidate models with different parameters into a new forecast for the same/similar modeling approaches. In this study, we propose a set of computational procedures to solve the above two issues via two judgmental criteria. Meanwhile, in view of the problems presented in the literature, a novel modeling technique is also proposed to overcome the drawbacks of existing combined forecasting methods. To verify the efficiency and reliability of the proposed procedure and modeling technique, the simulations and real data examples are conducted in this study.The results obtained reveal that the proposed procedure and modeling technique can be used as a feasible solution for time series forecasting with multiple candidate models.
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.
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
Exact series model of Langevin transducers with internal losses.
Nishamol, P A; Ebenezer, D D
2014-03-01
An exact series method is presented to analyze classical Langevin transducers with arbitrary boundary conditions. The transducers consist of an axially polarized piezoelectric solid cylinder sandwiched between two elastic solid cylinders. All three cylinders are of the same diameter. The length to diameter ratio is arbitrary. Complex piezoelectric and elastic coefficients are used to model internal losses. Solutions to the exact linearized governing equations for each cylinder include four series. Each term in each series is an exact solution to the governing equations. Bessel and trigonometric functions that form complete and orthogonal sets in the radial and axial directions, respectively, are used in the series. Asymmetric transducers and boundary conditions are modeled by using axially symmetric and anti-symmetric sets of functions. All interface and boundary conditions are satisfied in a weighted-average sense. The computed input electrical admittance, displacement, and stress in transducers are presented in tables and figures, and are in very good agreement with those obtained using atila-a finite element package for the analysis of sonar transducers. For all the transducers considered in the analysis, the maximum difference between the first three resonance frequencies calculated using the present method and atila is less than 0.03%.
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 auto body repair. This program is designed to run 40 weeks and cover 7 instructional areas: use of basic repair tools; metal bumping (theory and practice); metal refinishing (theory and practice); panel replacement; glass work; spot…
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.
TECHNICAL VISION SYSTEM FOR THE ROBOTIC MODEL OF SURFACE VESSEL
Directory of Open Access Journals (Sweden)
V. S. Gromov
2016-07-01
Full Text Available The paper presents results of work on creation of technical vision systems within the training complex for the verification of control systems by the model of surface vessel. The developed system allows determination of the coordinates and orientation angle of the object of control by means of an external video camera on one bench mark and without the need to install additional equipment on the object of control itself. Testing of the method was carried out on the robotic complex with the model of a surface vessel with a length of 430 mm; coordinates of the control object were determined with the accuracy of 2 mm. This method can be applied as a subsystem of receiving coordinates for systems of automatic control of surface vessels when testing on the scale models.
Technical illustration based on 3D CSG models
Institute of Scientific and Technical Information of China (English)
GENG Wei-dong; DING Lei; YU Hong-feng; PAN Yun-he
2005-01-01
This paper presents an automatic non-photorealistic rendering approach to generating technical illustration from 3D models. It first decomposes the 3D object into a set of CSG primitives, and then performs the hidden surface removal based on the prioritized list, in which the rendition order of CSG primitives is sorted out by depth. Then, each primitive is illustrated by the pre-defined empirical lighting model, and the system mimics the stroke-drawing by user-specified style. In order to artistically and flexibly modulate the illumination, the empirical lighting model is defined by three major components: parameters of multi-level lighting intensities, parametric spatial occupations for each lighting level, and an interpolation method to calculate the lighting units into the spatial occupation of CSG primitives, instead of"pixel-by-pixel" painting. This region-by-region shading facilitates the simulation of illustration styles.
Home care as change of the technical-assistance model.
Silva, Kênia Lara; de Sena, Roseni Rosângela; Seixas, Clarissa Terenzi; Feuerwerker, Laura Camargo Macruz; Merhy, Emerson Elias
2010-02-01
To analyze home care practices of outpatient and hospital services and their constitution as a substitute healthcare network. A qualitative study was carried out using tracer methodology to analyze four outpatient home care services from the Municipal Health Department and one service from a philanthropic hospital in the municipality of Belo Horizonte, Southeastern Brazil, between 2005 and 2007. The following procedures were carried out: interviews with the home care services' managers and teams, analysis of documents and follow-up of cases, holding interviews with patients and caregivers. The analysis was guided by the analytical categories home care integration into the healthcare network and technical-assistance model. Home care implementation was preceded by a political-institutional decision, both with a rationalizing orientation, intending to promote cost reduction, and also with the aim of carrying out the technical-assistance rearrangement of the healthcare networks. These two types of orientation were found to be in conflict, which implies difficulties for conciliating interests of the different players involved in the network, and also the creation of shared management spaces. It was possible to identify technological innovation and families' autonomy in the implementation of the healthcare projects. The teams proved to be cohesive, constructing, in the daily routine, new forms of integrating different perspectives so as to transform the healthcare practices. Challenges were observed in the proposal of integrating the different substitutive healthcare services, as the home care services' capacity to change the technical-assistance model is limited. Home care has potential for constituting a substitutive network by producing new care modalities that cross the projects of users, family members, social network, and home care professionals. Home care as a substitute healthcare modality requires political, conceptual and operational sustainability, as well as
A Parsimonious Bootstrap Method to Model Natural Inflow Energy Series
Directory of Open Access Journals (Sweden)
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.
Kālī: Time series data modeler
Kasliwal, Vishal P.
2016-07-01
The fully parallelized and vectorized software package Kālī models time series data using various stochastic processes such as continuous-time ARMA (C-ARMA) processes and uses Bayesian Markov Chain Monte-Carlo (MCMC) for inferencing a stochastic light curve. Kālimacr; is written in c++ with Python language bindings for ease of use. K¯lī is named jointly after the Hindu goddess of time, change, and power and also as an acronym for KArma LIbrary.
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.
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
Unsupervised Classification During Time-Series Model Building.
Gates, Kathleen M; Lane, Stephanie T; Varangis, E; Giovanello, K; Guiskewicz, K
2017-01-01
Researchers who collect multivariate time-series data across individuals must decide whether to model the dynamic processes at the individual level or at the group level. A recent innovation, group iterative multiple model estimation (GIMME), offers one solution to this dichotomy by identifying group-level time-series models in a data-driven manner while also reliably recovering individual-level patterns of dynamic effects. GIMME is unique in that it does not assume homogeneity in processes across individuals in terms of the patterns or weights of temporal effects. However, it can be difficult to make inferences from the nuances in varied individual-level patterns. The present article introduces an algorithm that arrives at subgroups of individuals that have similar dynamic models. Importantly, the researcher does not need to decide the number of subgroups. The final models contain reliable group-, subgroup-, and individual-level patterns that enable generalizable inferences, subgroups of individuals with shared model features, and individual-level patterns and estimates. We show that integrating community detection into the GIMME algorithm improves upon current standards in two important ways: (1) providing reliable classification and (2) increasing the reliability in the recovery of individual-level effects. We demonstrate this method on functional MRI from a sample of former American football players.
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.
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.
Organisation for Economic Cooperation and Development, Paris (France).
The purposes of this volume are to report a survey of current practice in the construction and use of mathematical models for the education sector: to identify the most important technical and substantive problems confronting the model-building effort; and to bridge the gap between the advancing research pursuit of model-building and the lagging…
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.
A feature fusion based forecasting model for financial time series.
Directory of Open Access Journals (Sweden)
Zhiqiang Guo
Full Text Available 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.
Modeling, design, and optimization of Mindwalker series elastic joint.
Wang, Shiqian; Meijneke, Cor; van der Kooij, Herman
2013-06-01
Weight and power autonomy are limiting the daily use of wearable exoskeleton. Lightweight, efficient and powerful actuation system are not easy to achieve. Choosing the right combinations of existing technologies, such as battery, gear and motor is not a trivial task. In this paper, we propose an optimization framework by setting up a power-based quasi-static model of the exoskeleton joint drivetrain. The goal is to find the most efficient and lightweight combinations. This framework can be generalized for other similar applications by extending or accommodating the model to their own needs. We also present the Mindwalker exoskeleton joint, for which a novel series elastic actuator, consisting of a ballscrew-driven linear actuator and a double spiral spring, was developed and tested. This linear actuator is capable of outputting 960 W power and the exoskeleton joint can output 100 Nm peak torque continuously. The double spiral spring can sense torque between 0.08Nm and 100 Nm and it exhibits linearity of 99.99%, with no backlash or hysteresis. The series elastic joint can track a chirp torque profile with amplitude of 100 Nm over 6 Hz (large torque bandwidth) and for small torque (2 Nm peak-to-peak), it has a bandwidth over 38 Hz. The integrated exoskeleton joint, including the ballscrew-driven linear actuator, the series spring, electronics and the metal housing which hosts these components, weighs 2.9 kg.
A series of rat segmental forelimb ectopic implantation models.
Zhou, Xianyu; Luo, Xusong; Gao, Bowen; Liu, Fei; Gu, Chuan; Yu, Qingxiong; Li, Qingfeng; Zhu, Hainan
2017-05-09
Temporary ectopic implantation has been performed in clinical practice to salvage devascularized amputated tissues for delayed replantation purpose. In this study, we established a series of segmental forelimb ectopic implantation models in rats, including forelimb, forearm, forepaw, digit, and double forelimbs, to mimic the clinical context. Time of amputated limbs harvesting in donors and ectopic implantation process in recipients were recorded. Survival time and mortalities of recipients were also recorded. Sixty days after ectopic implantation, a full-field laser perfusion imager (FLPI) was used to detect the blood flow of amputated limbs and micro-CT imaging was used to examine bone morphological changes. Histological sections of amputated limbs were stained with hematoxylin and eosin to evaluate pathological changes. Implanted amputated limbs in all models achieved long term survival and there were no obvious morphological and histological changes were found according to results of micro-CT and histology study. Thus, a series of rat segmental forelimb temporary ectopic implantation models have been well established. To our knowledge, this is the first rodent animal model related to forelimb temporary ectopic implantation. These models might facilitate further research related to salvage, reconstruction and better aesthetic and functional outcome of upper extremity/digit in temporary ectopic implantation scenario.
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
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
Series Connected Photovoltaic Cells—Modelling and Analysis
Directory of Open Access Journals (Sweden)
Anas Al Tarabsheh
2017-03-01
Full Text Available As solar energy costs continue to drop, the number of large-scale deployment projects increases, and the need for different analysis models for photovoltaic (PV modules in both academia and industry rises. This paper proposes a modified equivalent-circuit model for PV modules. A PV module comprises several series-connected PV cells, to generate more electrical power, where each PV cell has an internal shunt resistance. Our proposed model simplifies the standard one-diode equivalent-circuit (SEC model by removing the shunt resistance and including its effect on the diode part of the circuit, while retaining the original model accuracy. Our proposed equivalent circuit, called here a modified SEC (MSEC, has less number of circuit elements. All of the PV cells are assumed operating under the same ambient conditions where they share the same electric voltage and current values. To ensure the simplification did not come at a reduction in the accuracy of the SEC model, we validate our MSEC model by simulating both under the same conditions, calculate, and compare their current/voltage (I/V characteristics. Our results validate the accuracy of our model with the difference between the two models falling below 1%. Therefore, the proposed model can be adopted as an alternative representation of the equivalent circuit for PV cells and modules.
A Simple Pile-up Model for Time Series Analysis
Sevilla, Diego J. R.
2017-07-01
In this paper, a simple pile-up model is presented. This model calculates the probability P(n| N) of having n counts if N particles collide with a sensor during an exposure time. Through some approximations, an analytic expression depending on only one parameter is obtained. This parameter characterizes the pile-up magnitude, and depends on features of the instrument and the source. The statistical model obtained permits the determination of probability distributions of measured counts from the probability distributions of incoming particles, which is valuable for time series analysis. Applicability limits are discussed, and an example of the improvement that can be achieved in the statistical analysis considering the proposed pile-up model is shown by analyzing real data.
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…
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.
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.
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.
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...
Technical Note: The Simple Diagnostic Photosynthesis and Respiration Model (SDPRM
Directory of Open Access Journals (Sweden)
B. Badawy
2012-10-01
Full Text Available We present a Simple Diagnostic Photosynthesis and Respiration Model (SDPRM that has been developed based on pre-existing formulations. The photosynthesis model is based on the light use efficiency logic, suggested by Monteith1977, for calculating the Gross Primary Production (GPP while the ecosystem respiration (R_{eco} model is based on the formulations introduced by Lloyd1994 and modified by Reichstein2003. SDPRM is driven by satellite-derived fAPAR (fraction of Absorbed Photosynthetically Active Radiation and climate data from NCEP/NCAR. The model estimates 3-hourly values of GPP for seven major biomes and daily R_{eco}. The motivation is to provide a-priori fields of surface CO_{2} fluxes with fine temporal and spatial scales, and their derivatives with respect to adjustable model parameters, for atmospheric CO_{2} inversions. The estimated fluxes from SDPRM showed that the model is capable of producing flux estimates consistent with the ones inferred from atmospheric CO_{2} inversion or simulated from process-based models. In this Technical Note, different analyses were carried out to test the sensitivity of the estimated fluxes of GPP and R_{eco} to their driving forces. The spatial patterns of the climatic controls (temperature, precipitation, water on the interannual variability of GPP are consistent with previous studies even though SDPRM has a very simple structure and few adjustable parameters, and hence it is much easier to modify than more sophisticated process-based models used in these previous studies. According to SDPRM, the results show that temperature is a limiting factor for the interannual variability of R_{eco} over the cold boreal forest, while precipitation is the main limiting factor of R_{eco} over the tropics and the southern hemisphere, consistent with previous regional studies.
DEFF Research Database (Denmark)
Sørup, Hjalte Jomo Danielsen; Madsen, Henrik; Arnbjerg-Nielsen, Karsten
2011-01-01
A very fine temporal and volumetric resolution precipitation time series is modeled using Markov models. Both 1st and 2nd order Markov models as well as seasonal and diurnal models are investigated and evaluated using likelihood based techniques. The 2nd order Markov model is found to be insignif...
Hybrid Perturbation methods based on Statistical Time Series models
San-Juan, Juan Félix; Pérez, Iván; López, Rosario
2016-01-01
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 a...
Crop Yield Forecasted Model Based on Time Series Techniques
Institute of Scientific and Technical Information of China (English)
Li Hong-ying; Hou Yan-lin; Zhou Yong-juan; Zhao Hui-ming
2012-01-01
Traditional studies on potential yield mainly referred to attainable yield： the maximum yield which could be reached by a crop in a given environment. The new concept of crop yield under average climate conditions was defined in this paper, which was affected by advancement of science and technology. Based on the new concept of crop yield, the time series techniques relying on past yield data was employed to set up a forecasting model. The model was tested by using average grain yields of Liaoning Province in China from 1949 to 2005. The testing combined dynamic n-choosing and micro tendency rectification, and an average forecasting error was 1.24%. In the trend line of yield change, and then a yield turning point might occur, in which case the inflexion model was used to solve the problem of yield turn point.
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.
Modeling Large Time Series for Efficient Approximate Query Processing
DEFF Research Database (Denmark)
Perera, Kasun S; Hahmann, Martin; Lehner, Wolfgang
2015-01-01
Evolving customer requirements and increasing competition force business organizations to store increasing amounts of data and query them for information at any given time. Due to the current growth of data volumes, timely extraction of relevant information becomes more and more difficult...... these issues, compression techniques have been introduced in many areas of data processing. In this paper, we outline a new system that does not query complete datasets but instead utilizes models to extract the requested information. For time series data we use Fourier and Cosine transformations and piece...
Technical Note: The Simple Diagnostic Photosynthesis and Respiration Model (SDPRM
Directory of Open Access Journals (Sweden)
B. Badawy
2013-10-01
Full Text Available We present a Simple Diagnostic Photosynthesis and Respiration Model (SDPRM that has been developed based on pre-existing formulations. The photosynthesis model is based on the light use efficiency logic for calculating the gross primary production (GPP, while the ecosystem respiration (Reco is a modified version of an Arrhenius-type equation. SDPRM is driven by satellite-derived fAPAR (fraction of Absorbed Photosynthetically Active Radiation and climate data from the National Center for Environmental Prediction/National Center for Atmospheric Research Reanalysis (NCEP/NCAR. The model estimates 3-hourly values of GPP for seven major biomes and daily Reco. The motivation is to provide a priori fields of surface CO2 fluxes with fine temporal and spatial scales for atmospheric CO2 inversions. The estimated fluxes from SDPRM showed that the model is capable of producing flux estimates consistent with the ones inferred from atmospheric CO2 inversion or simulated from process-based models. In this Technical Note, different analyses were carried out to test the sensitivity of the estimated fluxes of GPP and CO2 to their driving forces. The spatial patterns of the climatic controls (temperature, precipitation, water on the interannual variability of GPP are consistent with previous studies, even though SDPRM has a very simple structure and few adjustable parameters and hence it is much easier to modify in an inversion than more sophisticated process-based models. In SDPRM, temperature is a limiting factor for the interannual variability of Reco over cold boreal forest, while precipitation is the main limiting factor of Reco over the tropics and the southern hemisphere, consistent with previous regional studies.
Modeling Glacier Elevation Change from DEM Time Series
Directory of Open Access Journals (Sweden)
Di Wang
2015-08-01
Full Text Available In this study, a methodology for glacier elevation reconstruction from Digital Elevation Model (DEM time series (tDEM is described for modeling the evolution of glacier elevation and estimating related volume change, with focus on medium-resolution and noisy satellite DEMs. The method is robust with respect to outliers in individual DEM products. Fox Glacier and Franz Josef Glacier in New Zealand are used as test cases based on 31 Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER DEMs and the Shuttle Radar Topography Mission (SRTM DEM. We obtained a mean surface elevation lowering rate of −0.51 ± 0.02 m·a−1 and −0.09 ± 0.02 m·a−1 between 2000 and 2014 for Fox and Franz Josef Glacier, respectively. The specific volume difference between 2000 and 2014 was estimated as −0.77 ± 0.13 m·a−1 and −0.33 ± 0.06 m·a−1 by our tDEM method. The comparably moderate thinning rates are mainly due to volume gains after 2013 that compensate larger thinning rates earlier in the series. Terminus thickening prevailed between 2002 and 2007.
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.
Forecasting inflation in Montenegro using univariate time series models
Directory of Open Access Journals (Sweden)
Milena Lipovina-Božović
2015-04-01
Full Text Available The analysis of price trends and their prognosis is one of the key tasks of the economic authorities in each country. Due to the nature of the Montenegrin economy as small and open economy with euro as currency, forecasting inflation is very specific which is more difficult due to low quality of the data. This paper analyzes the utility and applicability of univariate time series models for forecasting price index in Montenegro. Data analysis of key macroeconomic movements in previous decades indicates the presence of many possible determinants that could influence forecasting result. This paper concludes that the forecasting models (ARIMA based only on its own previous values cannot adequately cover the key factors that determine the price level in the future, probably because of the existence of numerous external factors that influence the price movement in Montenegro.
Madsen, Henrik; Pearson, Charles P.; Rosbjerg, Dan
1997-04-01
Two regional estimation schemes, based on, respectively, partial duration series (PDS) and annual maximum series (AMS), are compared. The PDS model assumes a generalized Pareto (GP) distribution for modeling threshold exceedances corresponding to a generalized extreme value (GEV) distribution for annual maxima. First, the accuracy of PDS/GP and AMS/GEV regional index-flood T-year event estimators are compared using Monte Carlo simulations. For estimation in typical regions assuming a realistic degree of heterogeneity, the PDS/GP index-flood model is more efficient. The regional PDS and AMS procedures are subsequently applied to flood records from 48 catchments in New Zealand. To identify homogeneous groupings of catchments, a split-sample regionalization approach based on catchment characteristics is adopted. The defined groups are more homogeneous for PDS data than for AMS data; a two-way grouping based on annual average rainfall is sufficient to attain homogeneity for PDS, whereas a further partitioning is necessary for AMS. In determination of the regional parent distribution using L- moment ratio diagrams, PDS data, in contrast to AMS data, provide an unambiguous interpretation, supporting a GP distribution.
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 Assistance Model for Long Term Systems Change
Kahn, Lynne; Hurth, Joicey; Diefendorf, Martha; Kasprzak, Christina; Lucas, Anne; Ringwalt, Sharon
2009-01-01
The National Early Childhood Technical Assistance Center (NECTAC) was charged by the U.S. Department of Education's Office of Special Education Programs (OSEP) from 2001-2006 to develop, implement, and evaluate an approach to technical assistance that would result in sustainable systems change in state Early Intervention and Preschool Special…
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.
2013-10-31
... Learjet Model 45 series airplanes. The Model 45 series airplanes are swept-wing aircraft equipped with two... type certification basis for Learjet Model 45 series airplanes. System Security Protection for Aircraft... ensure that continued airworthiness of the aircraft is maintained, including all...
Using the Neumann series expansion for assembling Reduced Order Models
Directory of Open Access Journals (Sweden)
Nasisi S.
2014-06-01
Full Text Available An efficient method to remove the limitation in selecting the master degrees of freedom in a finite element model by means of a model order reduction is presented. A major difficulty of the Guyan reduction and IRS method (Improved Reduced System is represented by the need of appropriately select the master and slave degrees of freedom for the rate of convergence to be high. This study approaches the above limitation by using a particular arrangement of the rows and columns of the assembled matrices K and M and employing a combination between the IRS method and a variant of the analytical selection of masters presented in (Shah, V. N., Raymund, M., Analytical selection of masters for the reduced eigenvalue problem, International Journal for Numerical Methods in Engineering 18 (1 1982 in case first lowest frequencies had to be sought. One of the most significant characteristics of the approach is the use of the Neumann series expansion that motivates this particular arrangement of the matrices’ entries. The method shows a higher rate of convergence when compared to the standard IRS and very accurate results for the lowest reduced frequencies. To show the effectiveness of the proposed method two testing structures and the human vocal tract model employed in (Vampola, T., Horacek, J., Svec, J. G., FE modeling of human vocal tract acoustics. Part I: Prodution of Czech vowels, Acta Acustica United with Acustica 94 (3 2008 are presented.
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.
Modeling PSInSAR time series without phase unwrapping
Zhang, L.; Ding, X.; Lu, Zhiming
2011-01-01
In this paper, we propose a least-squares-based method for multitemporal synthetic aperture radar interferometry that allows one to estimate deformations without the need of phase unwrapping. The method utilizes a series of multimaster wrapped differential interferograms with short baselines and focuses on arcs at which there are no phase ambiguities. An outlier detector is used to identify and remove the arcs with phase ambiguities, and a pseudoinverse of the variancecovariance matrix is used as the weight matrix of the correlated observations. The deformation rates at coherent points are estimated with a least squares model constrained by reference points. The proposed approach is verified with a set of simulated data. ?? 2006 IEEE.
Predicting chaotic time series with a partial model.
Hamilton, Franz; Berry, Tyrus; Sauer, Timothy
2015-07-01
Methods for forecasting time series are a critical aspect of the understanding and control of complex networks. When the model of the network is unknown, nonparametric methods for prediction have been developed, based on concepts of attractor reconstruction pioneered by Takens and others. In this Rapid Communication we consider how to make use of a subset of the system equations, if they are known, to improve the predictive capability of forecasting methods. A counterintuitive implication of the results is that knowledge of the evolution equation of even one variable, if known, can improve forecasting of all variables. The method is illustrated on data from the Lorenz attractor and from a small network with chaotic dynamics.
Forecasting electricity usage using univariate time series models
Hock-Eam, Lim; Chee-Yin, Yip
2014-12-01
Electricity is one of the important energy sources. A sufficient supply of electricity is vital to support a country's development and growth. Due to the changing of socio-economic characteristics, increasing competition and deregulation of electricity supply industry, the electricity demand forecasting is even more important than before. It is imperative to evaluate and compare the predictive performance of various forecasting methods. This will provide further insights on the weakness and strengths of each method. In literature, there are mixed evidences on the best forecasting methods of electricity demand. This paper aims to compare the predictive performance of univariate time series models for forecasting the electricity demand using a monthly data of maximum electricity load in Malaysia from January 2003 to December 2013. Results reveal that the Box-Jenkins method produces the best out-of-sample predictive performance. On the other hand, Holt-Winters exponential smoothing method is a good forecasting method for in-sample predictive performance.
MODELLING GASOLINE DEMAND IN GHANA: A STRUCTURAL TIME SERIES ANALYSIS
Directory of Open Access Journals (Sweden)
Ishmael Ackah
2014-01-01
Full Text Available Concerns about the role of energy consumption in global warming have led to policy designs that seek to reduce fossil fuel consumption or find a less polluting alternative especiallyfor the transport sector. This study seeks to estimate the elasticities of price, income, education and technology on transport gasoline demand sector inGhana. The Structural Time Series Model reports a short-run price and income elasticities of -0.0088 and 0.713. Total factor productivity is -0.408 whilstthe elasticity for education is 2.33. In the long run, the reported price and income elasticities are -0.065 and 5.129 respectively. The long run elasticityfor productivity is -2.935. The study recommends that in order to enhanceefficiency in gasoline consumption in the transport sector, there should beinvestment in productivity.
Technical Assistance Model for Long-Term Systems Change: Three State Examples
Kasprzak, Christina; Hurth, Joicey; Lucas, Anne; Marshall, Jacqueline; Terrell, Adriane; Jones, Elizabeth
2010-01-01
The National Early Childhood Technical Assistance Center (NECTAC) Technical Assistance (TA) Model for Long-Term Systems Change (LTSC) is grounded in conceptual frameworks in the literature on systems change and systems thinking. The NECTAC conceptual framework uses a logic model approach to change developed specifically for states' infant and…
2012-05-11
... COMMISSION Model Safety Evaluation for Plant-Specific Adoption of Technical Specifications Task Force... availability. SUMMARY: The U.S. Nuclear Regulatory Commission (NRC) is announcing the availability of the model safety evaluation (SE) for plant-specific adoption of Technical Specifications (TSs) Task Force...
Final Technical Report Advanced Solar Resource Modeling and Analysis.
Energy Technology Data Exchange (ETDEWEB)
Hansen, Clifford [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2015-12-01
The SunShot Initiative coordinates research, development, demonstration, and deployment activities aimed at dramatically reducing the total installed cost of solar power. The SunShot Initiative focuses on removing critical technical and non-technical barriers to installing and integrating solar energy into the electricity grid. Uncertainty in projected power and energy production from solar power systems contributes to these barriers by increasing financial risks to photovoltaic (PV) deployment and by exacerbating the technical challenges to integration of solar power on the electricity grid.
GROUP GUIDANCE SERVICES MANAGEMENT OF BEHAVIORAL TECHNIC HOMEWORK MODEL
Directory of Open Access Journals (Sweden)
Juhri A M.
2013-09-01
Full Text Available Abstract: This simple paper describes the implementation of management guidance service groups using the model home visits behavioral techniques (behavior technic homework. The ideas outlined in this paper are intended to add insight for counselors in the management of the implementation of counseling services group that carried out effectively. This simple paper is expected to be used as reference studies in theoretical matters relating to the management guidance services group, for counselors to students both need guidance services and those who passively as they face various problems difficulties martial jar and obstacles in the achievement of learning , In general, this study aims to provide insight in particular in the development of social skills for students, especially the ability to communicate with the participants of the service (students more While specifically to encourage the development of feelings, thoughts, perceptions, insights and attitudes that support embodiments behavior Iebih creative and effective in improving communication skills both verbal and non-verbal for students. Keyword: counselor, counseling, group, student
Auto-Regressive Models of Non-Stationary Time Series with Finite Length
Institute of Scientific and Technical Information of China (English)
FEI Wanchun; BAI Lun
2005-01-01
To analyze and simulate non-stationary time series with finite length, the statistical characteristics and auto-regressive (AR) models of non-stationary time series with finite length are discussed and studied. A new AR model called the time varying parameter AR model is proposed for solution of non-stationary time series with finite length. The auto-covariances of time series simulated by means of several AR models are analyzed. The result shows that the new AR model can be used to simulate and generate a new time series with the auto-covariance same as the original time series. The size curves of cocoon filaments regarded as non-stationary time series with finite length are experimentally simulated. The simulation results are significantly better than those obtained so far, and illustrate the availability of the time varying parameter AR model. The results are useful for analyzing and simulating non-stationary time series with finite length.
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.
Testing coeffcients of AR and bilinear time series models by a graphical approach
Institute of Scientific and Technical Information of China (English)
IP; WaiCheung
2008-01-01
AR and bilinear time series models are expressed as time series chain graphical models, based on which, it is shown that the coefficients of AR and bilinear models are the conditional correlation coefficients conditioned on the other components of the time series. Then a graphically based procedure is proposed to test the significance of the coeffcients of AR and bilinear time series. Simulations show that our procedure performs well both in sizes and powers.
Formal modelling and analysis of socio-technical systems
DEFF Research Database (Denmark)
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....
Modelling socio-technical aspects of organisational security
Ivanova, Marieta G.
2016-01-01
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 on o
INDUSTRIAL PRODUCTION IN GERMANY AND AUSTRIA: A CASE STUDY IN STRUCTURAL TIME SERIES MODELLING
Institute of Scientific and Technical Information of China (English)
Gerhard THURY
2003-01-01
Industrial production series are volatile and often cyclical. Time series models can be used to establish certain stylized facts, such as trends and cycles, which may be present in these series. In certain situations, it is also possible that common factors, which may have an interesting interpretation, can be detected in production series. Series from two neighboring countries with close economic relationships, such as Germany and Austria, are especially likely to exhibit such joint stylized facts.
Directory of Open Access Journals (Sweden)
Entin Hidayah
2011-02-01
Full Text Available Disaggregation of hourly rainfall data is very important to fulfil the input of continual rainfall-runoff model, when the availability of automatic rainfall records are limited. Continual rainfall-runoff modeling requires rainfall data in form of series of hourly. Such specification can be obtained by temporal disaggregation in single site. The paper attempts to generate single-site rainfall model based upon time series (AR1 model by adjusting and establishing dummy procedure. Estimated with Bayesian Markov Chain Monte Carlo (MCMC the objective variable is hourly rainfall depth. Performance of model has been evaluated by comparison of history data and model prediction. The result shows that the model has a good performance for dry interval periods. The performance of the model good represented by smaller number of MAE by 0.21 respectively.
Elmore, Donald E.; Guayasamin, Ryann C.; Kieffer, Madeleine E.
2010-01-01
As computational modeling plays an increasingly central role in biochemical research, it is important to provide students with exposure to common modeling methods in their undergraduate curriculum. This article describes a series of computer labs designed to introduce undergraduate students to energy minimization, molecular dynamics simulations,…
Elmore, Donald E.; Guayasamin, Ryann C.; Kieffer, Madeleine E.
2010-01-01
As computational modeling plays an increasingly central role in biochemical research, it is important to provide students with exposure to common modeling methods in their undergraduate curriculum. This article describes a series of computer labs designed to introduce undergraduate students to energy minimization, molecular dynamics simulations,…
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).
Quinn, David W.; Quinn, Nancy W.
The Boston, Massachusetts, school district requires that its 9th grade students pass both the Boston Public Schools Math Benchmark Assessment (BPS Math) and the Scholastic Reading Inventory (SRI) before entering 10th grade. At Madison Park Technical-Vocational High School in June 2000, 349 students failed either the mathematics or reading test or…
Office of Education (DHEW), Washington, DC.
This curriculum guide is for administrators and their advisors to use in meeting local, state, and regional needs in training architectual and building construction technicians at the post-high school level. It was developed by a technical education specialist at the national level. The guide provides: (1) a suggested curriculum plan, (2) course…
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 o...... it. We validate our approach using scenarios from IPTV and Cloud Infrastructure case studies....... on organisations are far from being purely technical. After all, organisations consist of employees. Often the human factor appears to be the weakest point in the security of organisations. It may be easier to break through a system using a social engineering attack rather than a pure technological one. The Stux......Net attack is only one of the many examples showing that vulnerabilities of organisations are increasingly exploited on different levels including the human factor. There is an urgent need for integration between the technical and social aspects of systems in assessing their security. Such an integration...
Model of a synthetic wind speed time series generator
DEFF Research Database (Denmark)
Negra, N.B.; Holmstrøm, O.; Bak-Jensen, B.
2008-01-01
of possible wind conditions. If these information are not available, synthetic wind speed time series may be a useful tool as well, but their generator must preserve statistical and stochastic features of the phenomenon. This paper deals with this issue: a generator for synthetic wind speed time series...
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.
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…
Fan, Jiang-Ping
2006-01-01
In this article, the author demonstrates that the semiotic model proposed by Charles Morris enables us to optimize our understanding of technical communication practices and provides a good point of inquiry. To illustrate this point, the author exemplifies the semiotic approaches by scholars in technical communication and elaborates Morris's model…
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
H.P.G. Pennings (Enrico); L. Sereno (Luigi)
2010-01-01
textabstractThis study sets up a compound option approach for evaluating pharmaceutical R&D investment projects in the presence of technical and economic uncertainties. Technical uncertainty is modeled as a Poisson jump that allows for failure and thus abandonment of the drug development. Economic u
H.P.G. Pennings (Enrico); L. Sereno (Luigi)
2010-01-01
textabstractThis study sets up a compound option approach for evaluating pharmaceutical R&D investment projects in the presence of technical and economic uncertainties. Technical uncertainty is modeled as a Poisson jump that allows for failure and thus abandonment of the drug development. Economic
Forecasting Financial Time-Series using Artificial Market Models
Gupta, N; Johnson, N F; Gupta, Nachi; Hauser, Raphael; Johnson, Neil F.
2005-01-01
We discuss the theoretical machinery involved in predicting financial market movements using an artificial market model which has been trained on real financial data. This approach to market prediction - in particular, forecasting financial time-series by training a third-party or 'black box' game on the financial data itself -- was discussed by Johnson et al. in cond-mat/0105303 and cond-mat/0105258 and was based on some encouraging preliminary investigations of the dollar-yen exchange rate, various individual stocks, and stock market indices. However, the initial attempts lacked a clear formal methodology. Here we present a detailed methodology, using optimization techniques to build an estimate of the strategy distribution across the multi-trader population. In contrast to earlier attempts, we are able to present a systematic method for identifying 'pockets of predictability' in real-world markets. We find that as each pocket closes up, the black-box system needs to be 'reset' - which is equivalent to sayi...
Time-varying parameter auto-regressive models for autocovariance nonstationary time series
Institute of Scientific and Technical Information of China (English)
FEI WanChun; BAI Lun
2009-01-01
In this paper,autocovariance nonstationary time series is clearly defined on a family of time series.We propose three types of TVPAR (time-varying parameter auto-regressive) models:the full order TVPAR model,the time-unvarying order TVPAR model and the time-varying order TVPAR model for autocovariance nonstationary time series.Related minimum AIC (Akaike information criterion) estimations are carried out.
Time-varying parameter auto-regressive models for autocovariance nonstationary time series
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
In this paper, autocovariance nonstationary time series is clearly defined on a family of time series. We propose three types of TVPAR (time-varying parameter auto-regressive) models: the full order TVPAR model, the time-unvarying order TVPAR model and the time-varying order TV-PAR model for autocovariance nonstationary time series. Related minimum AIC (Akaike information criterion) estimations are carried out.
A Learning Model for Updating Older Technical and Professional Persons.
Dubin, Samuel S.
Technical and professional persons are especially threatened by the potentiality of becoming outdated in their skills and their knowledge. It is not enough for workers in these fields to maintain the competence acquired in the years of formal education. Their information bank is anything but static; the norm is perpetual change. Psychologists,…
Modelling social-technical attacks with timed automata
David, Nicolas; David, Alexandre; Hansen, René Rydhof; Larsen, Kim G.; Legay, Axel; Olesen, Mads Chr.; Probst, Christian W.
2015-01-01
Attacks on a system often exploit vulnerabilities that arise from human behaviour or other human activity. Attacks of this type, so-called socio-technical attacks, cover everything from social engineering to insider attacks, and they can have a devastating impact on an unprepared organisation. In th
Directory of Open Access Journals (Sweden)
Kennedy Curtis E
2011-10-01
Full Text Available Abstract Background 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. Methods 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. Results 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
Hanzer, Florian; Marke, Thomas; Strasser, Ulrich
2016-04-01
In this presentation, a module for simulating technical snow production in ski areas coupled to the spatially distributed physically based hydroclimatological model AMUNDSEN is presented. The module explicitly considers individual snow guns and distributes the produced snow along the slopes. The amount of snow produced by each device is a function of its type, of wet-bulb temperature at the location, of ski area infrastructure (in terms of water supply and pumping capacity), and of snow demand. An empirical rule in the modeling for snow production, derived from common snowmaking practices, splits the winter season into a period of maximum snowmaking and a successive period of selective on-demand snowmaking. The model is exemplarily set up for a ski area in the Schladming region (Austrian Alps) using actual snowmaking infrastructure data. Integration of these data as model variables, as well as stakeholder-defined indicators and thresholds, have been implemented as defined interfaces in a coupled component model architecture. Comparison of the model results with recordings of snowmaking operation and satellite-derived snow cover maps indicate that the model is capable of accurately simulating the real-world snowmaking practice, and the combined natural and technical snow conditions on the slopes. The explicit consideration of individual snow guns and ski area infrastructure makes the model a valuable tool for scenario applications, e.g. to assess the effects of different ski area management strategies and changes in snowmaking infrastructure for climate change impact studies.
Richly parameterized linear models additive, time series, and spatial models using random effects
Hodges, James S
2013-01-01
A First Step toward a Unified Theory of Richly Parameterized Linear ModelsUsing mixed linear models to analyze data often leads to results that are mysterious, inconvenient, or wrong. Further compounding the problem, statisticians lack a cohesive resource to acquire a systematic, theory-based understanding of models with random effects.Richly Parameterized Linear Models: Additive, Time Series, and Spatial Models Using Random Effects takes a first step in developing a full theory of richly parameterized models, which would allow statisticians to better understand their analysis results. The aut
Prediction and interpolation of time series by state space models
Helske, Jouni
2015-01-01
A large amount of data collected today is in the form of a time series. In order to make realistic inferences based on time series forecasts, in addition to point predictions, prediction intervals or other measures of uncertainty should be presented. Multiple sources of uncertainty are often ignored due to the complexities involved in accounting them correctly. In this dissertation, some of these problems are reviewed and some new solutions are presented. A state space approach...
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...
Modular System Modeling for Quantitative Reliability Evaluation of Technical Systems
Directory of Open Access Journals (Sweden)
Stephan Neumann
2016-01-01
Full Text Available In modern times, it is necessary to offer reliable products to match the statutory directives concerning product liability and the high expectations of customers for durable devices. Furthermore, to maintain a high competitiveness, engineers need to know as accurately as possible how long their product will last and how to influence the life expectancy without expensive and time-consuming testing. As the components of a system are responsible for the system reliability, this paper introduces and evaluates calculation methods for life expectancy of common machine elements in technical systems. Subsequently, a method for the quantitative evaluation of the reliability of technical systems is proposed and applied to a heavy-duty power shift transmission.
Why technical trading may be successful? A lesson from the agent-based modeling
Schmidt, Anatoly B.
2002-01-01
It is shown using a simple agent-based market dynamics model that if the technical traders are able to affect the market liquidity, their concerted actions can move the market price in the direction favorable to their strategy.
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. Presented at the IADIS m-learning 2008 Conference. April, 11-13, 2008, Carvoeiro, Portugal.
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…
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…
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…
Technical review of the dispersion and dose models used in the MILDOS computer program
Energy Technology Data Exchange (ETDEWEB)
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.
Time series, correlation matrices and random matrix models
Energy Technology Data Exchange (ETDEWEB)
Vinayak [Instituto de Ciencias Físicas, Universidad Nacional Autónoma de México, C.P. 62210 Cuernavaca (Mexico); Seligman, Thomas H. [Instituto de Ciencias Físicas, Universidad Nacional Autónoma de México, C.P. 62210 Cuernavaca, México and Centro Internacional de Ciencias, C.P. 62210 Cuernavaca (Mexico)
2014-01-08
In this set of five lectures the authors have presented techniques to analyze open classical and quantum systems using correlation matrices. For diverse reasons we shall see that random matrices play an important role to describe a null hypothesis or a minimum information hypothesis for the description of a quantum system or subsystem. In the former case various forms of correlation matrices of time series associated with the classical observables of some system. The fact that such series are necessarily finite, inevitably introduces noise and this finite time influence lead to a random or stochastic component in these time series. By consequence random correlation matrices have a random component, and corresponding ensembles are used. In the latter we use random matrices to describe high temperature environment or uncontrolled perturbations, ensembles of differing chaotic systems etc. The common theme of the lectures is thus the importance of random matrix theory in a wide range of fields in and around physics.
Shipman, Virginia C.; And Others
The Massad Mimicry Test is an individually administered task for 3-1/2--4-1/2 year-old children. Part I evaluates the child's ability to reproduce phonemes in 30 nonsense words upon hearing each no more than three times from a tape recorded model. Similarly, Part II assesses the child's ability to reproduce meaningful words and phonemes as they…
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.
Time series count data models: an empirical application to traffic accidents.
Quddus, Mohammed A
2008-09-01
Count data are primarily categorised as cross-sectional, time series, and panel. Over the past decade, Poisson and Negative Binomial (NB) models have been used widely to analyse cross-sectional and time series count data, and random effect and fixed effect Poisson and NB models have been used to analyse panel count data. However, recent literature suggests that although the underlying distributional assumptions of these models are appropriate for cross-sectional count data, they are not capable of taking into account the effect of serial correlation often found in pure time series count data. Real-valued time series models, such as the autoregressive integrated moving average (ARIMA) model, introduced by Box and Jenkins have been used in many applications over the last few decades. However, when modelling non-negative integer-valued data such as traffic accidents at a junction over time, Box and Jenkins models may be inappropriate. This is mainly due to the normality assumption of errors in the ARIMA model. Over the last few years, a new class of time series models known as integer-valued autoregressive (INAR) Poisson models, has been studied by many authors. This class of models is particularly applicable to the analysis of time series count data as these models hold the properties of Poisson regression and able to deal with serial correlation, and therefore offers an alternative to the real-valued time series models. The primary objective of this paper is to introduce the class of INAR models for the time series analysis of traffic accidents in Great Britain. Different types of time series count data are considered: aggregated time series data where both the spatial and temporal units of observation are relatively large (e.g., Great Britain and years) and disaggregated time series data where both the spatial and temporal units are relatively small (e.g., congestion charging zone and months). The performance of the INAR models is compared with the class of Box and
Financial-Economic Time Series Modeling and Prediction Techniques – Review
2014-01-01
Financial-economic time series distinguishes from other time series because they contain a portion of uncertainity. Because of this, statistical theory and methods play important role in their analysis. Moreover, external influence of various parameters on the values in time series makes them non-linear, which on the other hand suggests employment of more complex techniques for ther modeling. To cope with this challenging problem many researchers and scientists have developed various models a...
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.
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.
Energy Technology Data Exchange (ETDEWEB)
Weise, D.R.; Gelobter, A.; Haase, S.M.; Sackett, S.S.
1997-03-01
Fuels and stand inventory data are presented for giant sequoia by using 18 different photos located in giant sequoia/mixed conifer stands in the Sierra Nevada of California. Total fuel loading ranges from 7 to 72 tons/acre. The stands have been subjected to a variety of disturbances including timbers harvesting, wildfire, prescribed fire, and recreational use. Fire behavior predictions were made by using 10th, 50th, and 90th percentile weather conditions and the inventoried fuels information. The long-term visual impacts of the various management activities can also be partially assessed with this photo series.
2013-12-10
... part of the type certification basis for Cessna Model 680 Series airplanes. System Security Protection... Federal Aviation Administration 14 CFR Part 25 Special Conditions: Cessna Model 680 Series Airplanes; Aircraft Electronic System Security Protection From Unauthorized External Access AGENCY: Federal Aviation...
Multivariate nonlinear time series modeling of exposure and risk in road safety research
Bijleveld, F.; Commandeur, J.; Montfort, van K.; Koopman, S.J.
2010-01-01
A multivariate non-linear time series model for road safety data is presented. The model is applied in a case-study into the development of a yearly time series of numbers of fatal accidents (inside and outside urban areas) and numbers of kilometres driven by motor vehicles in the Netherlands betwee
2012-03-15
... COMMISSION Model Safety Evaluation for Plant-Specific Adoption of Technical Specifications Task Force... Regulatory Commission (NRC) is announcing the availability of the model safety evaluation (SE) for plant..., Revision 1, is available in ADAMS under Accession No. ML111650552; the model application is available...
2012-09-20
... COMMISSION Model Safety Evaluation for Plant-Specific Adoption of Technical Specifications Task Force...-415- 4737, or by email to pdr.resource@nrc.gov . TSTF-522, Revision 0, includes a model application and is available in ADAMS under Accession No. ML100890316. The model safety evaluation (SE) of...
Technical description of the RIVM/KNMI PUFF dispersion model. Version 4.0
van Pul WAJ
1992-01-01
This report provides a technical description of the RIVM/KNMI PUFF model. The model may be used to calculate, given wind and rain field data, the dispersion of components emitted following an accident, emergency or calamity; the model area may be freely chosen to match the area of concern. The re
Model-Coupled Autoencoder for Time Series Visualisation
Gianniotis, Nikolaos; Tiňo, Peter; Polsterer, Kai L
2016-01-01
We present an approach for the visualisation of a set of time series that combines an echo state network with an autoencoder. For each time series in the dataset we train an echo state network, using a common and fixed reservoir of hidden neurons, and use the optimised readout weights as the new representation. Dimensionality reduction is then performed via an autoencoder on the readout weight representations. The crux of the work is to equip the autoencoder with a loss function that correctly interprets the reconstructed readout weights by associating them with a reconstruction error measured in the data space of sequences. This essentially amounts to measuring the predictive performance that the reconstructed readout weights exhibit on their corresponding sequences when plugged back into the echo state network with the same fixed reservoir. We demonstrate that the proposed visualisation framework can deal both with real valued sequences as well as binary sequences. We derive magnification factors in order t...
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.
Travel cost inference from sparse, spatio-temporally correlated time series using markov models
DEFF Research Database (Denmark)
Yang, B.; Guo, C.; Jensen, C.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......The monitoring of a system can yield a set of measurements that can be modeled as a collection of time series. These time series are often sparse, due to missing measurements, and spatiotemporally correlated, meaning that spatially close time series exhibit temporal correlation. The analysis...... 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...
Learning restricted Boolean network model by time-series data
2014-01-01
Restricted Boolean networks are simplified Boolean networks that are required for either negative or positive regulations between genes. Higa et al. (BMC Proc 5:S5, 2011) proposed a three-rule algorithm to infer a restricted Boolean network from time-series data. However, the algorithm suffers from a major drawback, namely, it is very sensitive to noise. In this paper, we systematically analyze the regulatory relationships between genes based on the state switch of the target gene and propose an algorithm with which restricted Boolean networks may be inferred from time-series data. We compare the proposed algorithm with the three-rule algorithm and the best-fit algorithm based on both synthetic networks and a well-studied budding yeast cell cycle network. The performance of the algorithms is evaluated by three distance metrics: the normalized-edge Hamming distance μhame, the normalized Hamming distance of state transition μhamst, and the steady-state distribution distance μssd. Results show that the proposed algorithm outperforms the others according to both μhame and μhamst, whereas its performance according to μssd is intermediate between best-fit and the three-rule algorithms. Thus, our new algorithm is more appropriate for inferring interactions between genes from time-series data. PMID:25093019
Learning restricted Boolean network model by time-series data.
Ouyang, Hongjia; Fang, Jie; Shen, Liangzhong; Dougherty, Edward R; Liu, Wenbin
2014-01-01
Restricted Boolean networks are simplified Boolean networks that are required for either negative or positive regulations between genes. Higa et al. (BMC Proc 5:S5, 2011) proposed a three-rule algorithm to infer a restricted Boolean network from time-series data. However, the algorithm suffers from a major drawback, namely, it is very sensitive to noise. In this paper, we systematically analyze the regulatory relationships between genes based on the state switch of the target gene and propose an algorithm with which restricted Boolean networks may be inferred from time-series data. We compare the proposed algorithm with the three-rule algorithm and the best-fit algorithm based on both synthetic networks and a well-studied budding yeast cell cycle network. The performance of the algorithms is evaluated by three distance metrics: the normalized-edge Hamming distance [Formula: see text], the normalized Hamming distance of state transition [Formula: see text], and the steady-state distribution distance μ (ssd). Results show that the proposed algorithm outperforms the others according to both [Formula: see text] and [Formula: see text], whereas its performance according to μ (ssd) is intermediate between best-fit and the three-rule algorithms. Thus, our new algorithm is more appropriate for inferring interactions between genes from time-series data.
The modified Yule-Walker method for α-stable time series models
Kruczek, Piotr; Wyłomańska, Agnieszka; Teuerle, Marek; Gajda, Janusz
2017-03-01
This paper discusses the problem of parameters estimation for stable periodic autoregressive (PAR) time series. Considered models generalize popular and widely accepted autoregressive (AR) time series. By examining measures of dependence for α-stable processes, first we introduce new empirical estimator of autocovariation for α-stable sequences. Based on this approach we generalize Yule-Walker method for estimation of parameter for PAR time series. Thus we fill a gap in estimation methods for non-Gaussian models. We test proposed procedure and show its consistency. Moreover, we use our approach to model real empirical data thus showing usefulness of heavy tailed models in statistical modelling.
2013-12-12
... September 10, 2010, Cessna applied for a change to Type Certificate No. T00007WI in the digital systems architecture in the Cessna Model 750 series airplanes. The Model 750 is a twin-engine pressurized executive...
Directory of Open Access Journals (Sweden)
S. Cauvy-Fraunié
2013-04-01
Full Text Available Worldwide, the rapid shrinking of glaciers in response to ongoing climate change is currently modifying the glacial meltwater contribution to hydrosystems in glacierized catchments. Assessing the contribution of glacier run-off to stream discharge is therefore of critical importance to evaluate potential impact of glacier retreat on water quality and aquatic biota. This task has challenged both glacier hydrologists and ecologists over the last 20 yr due to both structural and functional complexity of the glacier-stream system interface. Here we propose a new methodological approach based on wavelet analyses on water depth time series to determine the glacial influence in glacierized catchments. We performed water depth measurement using water pressure loggers over ten months in 15 stream sites in two glacier-fed catchments in the Ecuadorian Andes (> 4000 m. We determined the global wavelet spectrum of each time series and defined the Wavelet Glacier Signal (WGS as the ratio between the global wavelet power spectrum value at a 24 h-scale and its corresponding significance value. To test the relevance of the WGS we compared it with the percentage of the glacier cover in the catchments, a metric of glacier influence often used in the literature. We then tested whether one month data could be sufficient to reliably determine the glacial influence. As expected we found that the WGS of glacier-fed streams decreased downstream with the increasing of non-glacial tributaries. We also found that the WGS and the percentage of the glacier cover in the catchment were significantly positively correlated and that one month data was sufficient to identify and compare the glacial influence between two sites, provided that the water level time series were acquired over the same period. Furthermore, we found that our method permits to detect glacial signal in supposedly non-glacial sites, thereby evidencing glacial meltwater infiltrations. While we specifically
Targets IMage Energy Regional (TIMER) Model, Technical Documentation
Vries B de; Vuuren D van; Elzen M den; Janssen M; MNV
2002-01-01
The Targets IMage Energy Regional simulation model, TIMER, is described in detail. This model was developed and used in close connection with the Integrated Model to Assess the Global Environment (IMAGE) 2.2. The system-dynamics TIMER model simulates the global energy system at an intermediate level
An Exponential Model for the Spectrum of a Scalar Time Series
A new class of parametric models for the spectrum of a scalar time series is proposed, in which the logarithm of the spectral density function is represented by a finite Fourier series. Two alternative parameter estimation procedures are described, and the use of a fitted model to provide forecasts of future values is discussed. The model has been compared with the more conventional autoregressive/moving-average model, and the results of their comparison are given.
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.
HIGH ORDER FUZZY TIME SERIES MODEL AND ITS APLICATION TO IMKB
Directory of Open Access Journals (Sweden)
Çağdaş Hakan ALADAĞ
2010-12-01
Full Text Available The observations of some real time series such as temperature and stock market can take different values in a day. Instead of representing the observations of these time series by real numbers, employing linguistic values or fuzzy sets can be more appropriate. In recent years, many approaches have been introduced to analyze time series consisting of observations which are fuzzy sets and such time series are called fuzzy time series. In this study, a novel approach is proposed to analyze high order fuzzy time series model. The proposed method is applied to IMKB data and the obtained results are discussed. IMKB data is also analyzed by using some other fuzzy time series methods available in the literature and obtained results are compared to results obtained from the proposed method. As a result of the comparison, it is seen that the proposed method produce accurate forecasts.
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)
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......, 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...... waste LCA models. This review infers that some of the differences in waste LCA models are inherent to the time they were developed. It is expected that models developed later, benefit from past modelling assumptions and knowledge and issues. Models developed in different countries furthermore rely...
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...
A technical study and analysis on fuzzy similarity based models for text classification
Puri, Shalini; 10.5121/ijdkp.2012.2201
2012-01-01
In this new and current era of technology, advancements and techniques, efficient and effective text document classification is becoming a challenging and highly required area to capably categorize text documents into mutually exclusive categories. Fuzzy similarity provides a way to find the similarity of features among various documents. In this paper, a technical review on various fuzzy similarity based models is given. These models are discussed and compared to frame out their use and necessity. A tour of different methodologies is provided which is based upon fuzzy similarity related concerns. It shows that how text and web documents are categorized efficiently into different categories. Various experimental results of these models are also discussed. The technical comparisons among each model's parameters are shown in the form of a 3-D chart. Such study and technical review provide a strong base of research work done on fuzzy similarity based text document categorization.
Markov Model of Wind Power Time Series UsingBayesian Inference of Transition Matrix
DEFF Research Database (Denmark)
Chen, Peiyuan; Berthelsen, Kasper Klitgaard; Bak-Jensen, Birgitte
2009-01-01
This paper proposes to use Bayesian inference of transition matrix when developing a discrete Markov model of a wind speed/power time series and 95% credible interval for the model verification. The Dirichlet distribution is used as a conjugate prior for the transition matrix. Three discrete Markov...... models are compared, i.e. the basic Markov model, the Bayesian Markov model and the birth-and-death Markov model. The proposed Bayesian Markov model shows the best accuracy in modeling the autocorrelation of the wind power time series....
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 refractive metasurfaces in series as a single metasurface
Toor, Fatima; Guneratne, Ananda C.
2016-03-01
Metasurfaces are boundaries between two media that are engineered to induce an abrupt phase shift in propagating light over a distance comparable to the wavelength of the light. Metasurface applications exploit this rapid phase shift to allow for precise control of wavefronts. The phase gradient is used to compute the angle at which light is refracted using the generalized Snell's Law. [1] In practice, refractive metasurfaces are designed using a relatively small number of phaseshifting elements such that the phase gradient is discrete rather than continuous. Designing such a metasurface requires finding phase-shifting elements that cover a full range of phases (a phase range) from 0 to 360 degrees. We demonstrate an analytical technique to calculate the refraction angle due to multiple metasurfaces arranged in series without needing to account for the effect of each individual metasurface. The phase gradients of refractive metasurfaces in series may be summed to obtain the phase gradient of a single equivalent refractive metasurface. This result is relevant to any application that requires a system with multiple metasurfaces, such as biomedical imaging [2], wavefront correctors [3], and beam shaping [4].
Targets IMage Energy Regional (TIMER) Model, Technical Documentation
Vries B de; Vuuren D van; Elzen M den; Janssen M; MNV
2002-01-01
Er wordt een gedetailleerde beschrijving gegeven van het Targets IMage Energy Regional (TIMER) simulatiemodel. Het model is ontwikkeld en toegepast in nauwe relatie met het Integrated Model to Assess the Global Environment (IMAGE) 2.1-2.2. . Het TIMER model is een systeem-dynamisch simulatiemode
Targets IMage Energy Regional (TIMER) Model, Technical Documentation
Vries B de; Vuuren D van; Elzen M den; Janssen M; MNV
2002-01-01
Er wordt een gedetailleerde beschrijving gegeven van het Targets IMage Energy Regional (TIMER) simulatiemodel. Het model is ontwikkeld en toegepast in nauwe relatie met het Integrated Model to Assess the Global Environment (IMAGE) 2.1-2.2. . Het TIMER model is een systeem-dynamisch
On the Practice of Bayesian Inference in Basic Economic Time Series Models using Gibbs Sampling
M.D. de Pooter (Michiel); R. Segers (René); H.K. van Dijk (Herman)
2006-01-01
textabstractSeveral lessons learned from a Bayesian analysis of basic economic time series models by means of the Gibbs sampling algorithm are presented. Models include the Cochrane-Orcutt model for serial correlation, the Koyck distributed lag model, the Unit Root model, the Instrumental Variables
Evaluating a technical university's placement test using the Rasch measurement model
Salleh, Tuan Salwani; Bakri, Norhayati; Zin, Zalhan Mohd
2016-10-01
This study discusses the process of validating a mathematics placement test at a technical university. The main objective is to produce a valid and reliable test to measure students' prerequisite knowledge to learn engineering technology mathematics. It is crucial to have a valid and reliable test as the results will be used in a critical decision making to assign students into different groups of Technical Mathematics 1. The placement test which consists of 50 mathematics questions were tested on 82 new diplomas in engineering technology students at a technical university. This study employed rasch measurement model to analyze the data through the Winsteps software. The results revealed that there are ten test questions lower than less able students' ability. Nevertheless, all the ten questions satisfied infit and outfit standard values. Thus, all the questions can be reused in the future placement test at the technical university.
Electric fish as natural models for technical sensor systems
von der Emde, Gerhard; Bousack, Herbert; Huck, Christina; Mayekar, Kavita; Pabst, Michael; Zhang, Yi
2009-05-01
Instead of vision, many animals use alternative senses for object detection. Weakly electric fish employ "active electrolocation", during which they discharge an electric organ emitting electrical current pulses (electric organ discharges, EOD). Local EODs are sensed by electroreceptors in the fish's skin, which respond to changes of the signal caused by nearby objects. Fish can gain information about attributes of an object, such as size, shape, distance, and complex impedance. When close to the fish, each object projects an 'electric image' onto the fish's skin. In order to get information about an object, the fish has to analyze the object's electric image by sampling its voltage distribution with the electroreceptors. We now know a great deal about the mechanisms the fish use to gain information about objects in their environment. Inspired by the remarkable capabilities of weakly electric fish in detecting and recognizing objects with their electric sense, we are designing technical sensor systems that can solve similar sensing problems. We applied the principles of active electrolocation to devices that produce electrical current pulses in water and simultaneously sense local current densities. Depending on the specific task, sensors can be designed which detect an object, localize it in space, determine its distance, and measure certain object properties such as material properties, thickness, or material faults. We present first experiments and FEM simulations on the optimal sensor arrangement regarding the sensor requirements e. g. localization of objects or distance measurements. Different methods of the sensor read-out and signal processing are compared.
An endogenous growth model with embodied energy-saving technical change
van Zon, A; Yetkiner, IH
2003-01-01
In this paper, we extend the Romer [Journal of Political Economy 98 (Part 2) (1990) S271] model in two ways. First we include energy consumption of intermediates. Second, intermediates become heterogeneous due to endogenous energy-saving technical change. We show that the resulting model can still g
van der Heijden, Sven; Callau Poduje, Ana; Müller, Hannes; Shehu, Bora; Haberlandt, Uwe; Lorenz, Manuel; Wagner, Sven; Kunstmann, Harald; Müller, Thomas; Mosthaf, Tobias; Bárdossy, András
2015-04-01
For the design and operation of urban drainage systems with numerical simulation models, long, continuous precipitation time series with high temporal resolution are necessary. Suitable observed time series are rare. As a result, intelligent design concepts often use uncertain or unsuitable precipitation data, which renders them uneconomic or unsustainable. An expedient alternative to observed data is the use of long, synthetic rainfall time series as input for the simulation models. Within the project SYNOPSE, several different methods to generate synthetic precipitation data for urban drainage modelling are advanced, tested, and compared. The presented study compares four different approaches of precipitation models regarding their ability to reproduce rainfall and runoff characteristics. These include one parametric stochastic model (alternating renewal approach), one non-parametric stochastic model (resampling approach), one downscaling approach from a regional climate model, and one disaggregation approach based on daily precipitation measurements. All four models produce long precipitation time series with a temporal resolution of five minutes. The synthetic time series are first compared to observed rainfall reference time series. Comparison criteria include event based statistics like mean dry spell and wet spell duration, wet spell amount and intensity, long term means of precipitation sum and number of events, and extreme value distributions for different durations. Then they are compared regarding simulated discharge characteristics using an urban hydrological model on a fictitious sewage network. First results show a principal suitability of all rainfall models but with different strengths and weaknesses regarding the different rainfall and runoff characteristics considered.
A flexible coefficient smooth transition time series model.
Medeiros, Marcelo C; Veiga, Alvaro
2005-01-01
In this paper, we consider a flexible smooth transition autoregressive (STAR) model with multiple regimes and multiple transition variables. This formulation can be interpreted as a time varying linear model where the coefficients are the outputs of a single hidden layer feedforward neural network. This proposal has the major advantage of nesting several nonlinear models, such as, the self-exciting threshold autoregressive (SETAR), the autoregressive neural network (AR-NN), and the logistic STAR models. Furthermore, if the neural network is interpreted as a nonparametric universal approximation to any Borel measurable function, our formulation is directly comparable to the functional coefficient autoregressive (FAR) and the single-index coefficient regression models. A model building procedure is developed based on statistical inference arguments. A Monte Carlo experiment showed that the procedure works in small samples, and its performance improves, as it should, in medium size samples. Several real examples are also addressed.
Technical Note: How to use Winbugs to infer animal models
DEFF Research Database (Denmark)
Damgaard, Lars Holm
2007-01-01
. 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...
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 < 0.01), and experimentally generated error terms of ±1.9% for any predicted individual value of δ(13) Cprotein . This model was tested using isotopic data from Formative Period individuals from northern Chile's Atacama Desert. The model presented here appears to hold significant potential for the prediction of the carbon isotope signature of dietary protein using only such data as is routinely generated in the course of stable isotope 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.
Modelling socio-technical transition patterns and pathways
N. Bergman (Noam); A. Haxeltine (Alex); L. Whitmarsh (Lorraine); J. Köhler (Jonathan); M.P. Schilperoord (Michel); J. Rotmans (Jan)
2008-01-01
textabstractWe report on research that is developing a simulation model for assessing systemic innovations, or 'transitions', of societal systems towards a more sustainable development. Our overall aim is to outline design principles for models that can offer new insights into tackling persistent
Calibration of transient groundwater models using time series analysis and moment matching
Bakker, M.; Maas, K.; Von Asmuth, J.R.
2008-01-01
A comprehensive and efficient approach is presented for the calibration of transient groundwater models. The approach starts with the time series analysis of the measured heads in observation wells using all active stresses as input series, which may include rainfall, evaporation, surface water leve
Design considerations for case series models with exposure onset measurement error.
Mohammed, Sandra M; Dalrymple, Lorien S; Sentürk, Damla; Nguyen, Danh V
2013-02-28
The case series model allows for estimation of the relative incidence of events, such as cardiovascular events, within a pre-specified time window after an exposure, such as an infection. The method requires only cases (individuals with events) and controls for all fixed/time-invariant confounders. The measurement error case series model extends the original case series model to handle imperfect data, where the timing of an infection (exposure) is not known precisely. In this work, we propose a method for power/sample size determination for the measurement error case series model. Extensive simulation studies are used to assess the accuracy of the proposed sample size formulas. We also examine the magnitude of the relative loss of power due to exposure onset measurement error, compared with the ideal situation where the time of exposure is measured precisely. To facilitate the design of case series studies, we provide publicly available web-based tools for determining power/sample size for both the measurement error case series model as well as the standard case series model.
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.
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
Technical Security Metrics Model in Compliance with ISO/IEC 27001 Standard
Directory of Open Access Journals (Sweden)
M. Azuwa
2015-05-01
Full Text Available Technical security metrics provide measurements in ensuring the effectiveness of technical security controls or technology devices/objects that are used in protecting the information systems. However, lack of understanding and method to develop the technical security metrics may lead to unachievable security control objectives and inefficient implementation. This paper proposes a model of technical security metrics to measure the effectiveness of network security management. The measurement is based on the security performance for (1 network security controls such as firewall, Intrusion Detection Prevention System (IDPS, switch, wireless access point and network architecture; and (2 network services such as Hypertext Transfer Protocol Secure (HTTPS and virtual private network (VPN. The methodology used is Plan-Do-Check-Act process model. The proposed technical security metrics provide guidance for organizations in complying with requirements of ISO/IEC 27001 Information Security Management System (ISMS standard. The proposed model should also be able to provide a comprehensive measurement and guide to use ISO/IEC 27004 ISMS Measurement standard.
Beyond icebergs: modeling globalization as biased technical change
2004-01-01
We propose a new approach to model costly international trade, which includes the standard approach, the “iceberg” transport cost, as a special case. The key idea is to make the technologies of supplying the good depend on the destination of the good. To demonstrate our approach, we extend the Ricardian model with a continuum of goods, due to Dornbusch, Fischer and Samuelson (1977), by introducing multiple factors of production and by making each industry consist of the domestic division, whi...
Center for Extended Magnetohydrodynamics Modeling - Final Technical Report
Energy Technology Data Exchange (ETDEWEB)
Parker, Scott [Univ. of Colorado, Boulder, CO (United States)
2016-02-14
This project funding supported approximately 74 percent of a Ph.D. graduate student, not including costs of travel and supplies. We had a highly successful research project including the development of a second-order implicit electromagnetic kinetic ion hybrid model [Cheng 2013, Sturdevant 2016], direct comparisons with the extended MHD NIMROD code and kinetic simulation [Schnack 2013], modeling of slab tearing modes using the fully kinetic ion hybrid model and finally, modeling global tearing modes in cylindrical geometry using gyrokinetic simulation [Chen 2015, Chen 2016]. We developed an electromagnetic second-order implicit kinetic ion fluid electron hybrid model [Cheng 2013]. As a first step, we assumed isothermal electrons, but have included drift-kinetic electrons in similar models [Chen 2011]. We used this simulation to study the nonlinear evolution of the tearing mode in slab geometry, including nonlinear evolution and saturation [Cheng 2013]. Later, we compared this model directly to extended MHD calculations using the NIMROD code [Schnack 2013]. In this study, we investigated the ion-temperature-gradient instability with an extended MHD code for the first time and got reasonable agreement with the kinetic calculation in terms of linear frequency, growth rate and mode structure. We then extended this model to include orbit averaging and sub-cycling of the ions and compared directly to gyrokinetic theory [Sturdevant 2016]. This work was highlighted in an Invited Talk at the International Conference on the Numerical Simulation of Plasmas in 2015. The orbit averaging sub-cycling multi-scale algorithm is amenable to hybrid architectures with GPUS or math co-processors. Additionally, our participation in the Center for Extend Magnetohydrodynamics motivated our research on developing the capability for gyrokinetic simulation to model a global tearing mode. We did this in cylindrical geometry where the results could be benchmarked with existing eigenmode
Application of uncertainty reasoning based on cloud model in time series prediction
Institute of Scientific and Technical Information of China (English)
张锦春; 胡谷雨
2003-01-01
Time series prediction has been successfully used in several application areas, such as meteoro-logical forecasting, market prediction, network traffic forecasting, etc. , and a number of techniques have been developed for modeling and predicting time series. In the traditional exponential smoothing method, a fixed weight is assigned to data history, and the trend changes of time series are ignored. In this paper, an uncertainty reasoning method, based on cloud model, is employed in time series prediction, which uses cloud logic controller to adjust the smoothing coefficient of the simple exponential smoothing method dynamically to fit the current trend of the time series. The validity of this solution was proved by experiments on various data sets.
Application of uncertainty reasoning based on cloud model in time series prediction
Institute of Scientific and Technical Information of China (English)
张锦春; 胡谷雨
2003-01-01
Time series prediction has been successfully used in several application areas, such as meteorological forecasting, market prediction, network traffic forecasting, etc., and a number of techniques have been developed for modeling and predicting time series. In the traditional exponential smoothing method, a fixed weight is assigned to data history, and the trend changes of time series are ignored. In this paper, an uncertainty reasoning method, based on cloud model, is employed in time series prediction, which uses cloud logic controller to adjust the smoothing coefficient of the simple exponential smoothing method dynamically to fit the current trend of the time series. The validity of this solution was proved by experiments on various data sets.
Principles of the Concept-Oriented Data Model : technical report
Savinov, AlexandrInstitute of Mathematics and Computer Science, Academy of Sciences of Moldova
2004-01-01
In the paper a new approach to data representation and manipulation is described, which is called the concept-oriented data model (CODM). It is supposed that items represent data units, which are stored in concepts. A concept is a combination of superconcepts, which determine the concept's dimensionality or properties. An item is a combination of superitems taken by one from all the superconcepts. An item stores a combination of references to its superitems. The references implement inclusion relation or attribute-value relation among items. A concept-oriented database is defined by its concept structure called syntax or schema and its item structure called semantics. The model defines formal transformations of syntax and semantics including the canonical semantics where all concepts are merged and the data semantics is represented by one set of items. The concept-oriented data model treats relations as subconcepts where items are instances of the relations. Multi-valued attributes are defined via subconcepts...
Model technical and tactical training karate «game» manner of conducting a duel
Directory of Open Access Journals (Sweden)
Natalya Boychenko
2015-04-01
Full Text Available Purpose: optimization of technical and tactical training karate «gaming» the manner of conducting a duel. Material and Methods: analysis and compilation of scientific and methodological literature, interviews with coaches for shock combat sports, video analysis techniques, teacher observations. Results: the model of technical and tactical training karate «game» manner of conducting a duel. Selection was done complexes jobs matching techniques to improve athletes 'game' in the manner of conducting a duel «Kyokushin» karate. Conclusion: the model of technical and tactical training fighters "game" manner of conducting a duel, which reveals the particular combination technique karate style «Kyokushin». Selection was done complexes jobs matching techniques to improve athletes 'game' in the manner of conducting a duel «Kyokushin» karate, aimed at improving the combinations with the action on the response of the enemy.
Institute of Scientific and Technical Information of China (English)
Cai Zhonghua
2008-01-01
The relationship between the emission of pollutant and economic growth has attracted a lot of attention in the environmental debate of the recent decades.Based on some theoretical and empirical research on environmental Kuznets curve(EKC),this paper introduces the environmental technical innovation and environmental investmen into Solow growth model to discuss the relationship between GDP per capital and the emission of pollutant.By the dvnamic simulation and parameters analysis,the results of the model indicate(1) when "green"technical progress and environmental investment are.fixed,the relationship between GDP per capital and the emission shows the linear elationship;(2)"green"technical progress can lead to the positive growth rates with a decreasing level of emision,which is compatible with an EKC;(3) the proportion of the environmental investment can lead the different growth rates and level of emission.These results can explain that developing countries are"too poor to be green".
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.
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 i
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...
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.
Multilayer stock forecasting model using fuzzy time series.
Javedani Sadaei, Hossein; Lee, Muhammad Hisyam
2014-01-01
After reviewing the vast body of literature on using FTS in stock market forecasting, certain deficiencies are distinguished in the hybridization of findings. In addition, the lack of constructive systematic framework, which can be helpful to indicate direction of growth in entire FTS forecasting systems, is outstanding. In this study, we propose a multilayer model for stock market forecasting including five logical significant layers. Every single layer has its detailed concern to assist forecast development by reconciling certain problems exclusively. To verify the model, a set of huge data containing Taiwan Stock Index (TAIEX), National Association of Securities Dealers Automated Quotations (NASDAQ), Dow Jones Industrial Average (DJI), and S&P 500 have been chosen as experimental datasets. The results indicate that the proposed methodology has the potential to be accepted as a framework for model development in stock market forecasts using FTS.
Nonlinear Time Series Model for Shape Classification Using Neural Networks
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
A complex nonlinear exponential autoregressive (CNEAR) model for invariant feature extraction is developed for recognizing arbitrary shapes on a plane. A neural network is used to calculate the CNEAR coefficients. The coefficients, which constitute the feature set, are proven to be invariant to boundary transformations such as translation, rotation, scale and choice of starting point in tracing the boundary. The feature set is then used as the input to a complex multilayer perceptron (C-MLP) network for learning and classification. Experimental results show that complicated shapes can be accurately recognized even with the low-order model and that the classification method has good fault tolerance when noise is present.
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.
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...
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...
Technical documentation of HGSYSTEM/UF{sub 6} model
Energy Technology Data Exchange (ETDEWEB)
Hanna, S.R.; Chang, J.C.; Zhang, J.X. [Earth Technology Corp., Concord, MA (United States)
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{sub 6} to the atmosphere at these plants. The HGSYSTEM model has been chosen as the basis for evaluating UF{sub 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{sub 6} with water vapor in air. Objective of this project was to incorporate additional capability into HGSYSTEM: UF{sub 6} chemistry and thermodynamics, plume lift-off algorithms, and wet and dry deposition. The HGSYSTEM modules are discussed. The hybrid HGSYSTEM/UF{sub 6} model has been evaluated in three ways.
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.
Prerequisites for modeling price and return data series for the Bucharest Stock Exchange
Directory of Open Access Journals (Sweden)
Andrei TINCA
2013-11-01
Full Text Available Time series data from the capital market exhibits certain qualities which invalidate the hypotheses necessary for obtaining meaningful results from statistical modeling. This paper presents some of these qualities by looking at the time series for prices and returns on the Romanian Stock Exchange. The examples are based on the price time series and return time series of the Antibiotice securities and the BET-C index. The choice of a certain security and of the stock exchange index has been made with the intention of analyzing, in the future, the correlation between these two variables, and drawing significant conclusions which can be used for forecasts.Firstly, we will identify the empirical proprieties of the capital market, as they are described in the field research. Secondly, we will investigate the prerequisites for modeling chronological data series; these are stationary mean and variance. In the paper, the three methods are used: graphical representation, autocorrelation and the ADF test (Augmented Dickey-Fuller. For the frequent cases where the mean is not stationary, we will present the time series differentiation method, which can be used to obtain stationary values.Lastly, we will investigate the normality of the time series through the skewness and kurtosis methods, and through the Jarque-Bera statistic. We find out a characteristic for the capital market, in that the majority of the time series for securities have non-normal distributions.
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 C...... 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....
Technical Assistance Model for Long-Term Systems Change: Three State Examples. Executive Summary
Kasprzak, Christina; Goode, Sue; Hurth, Joicey; Lucas, Anne; Marshall, Jacqueline; Terrell, Adriane; Jones, Elizabeth
2010-01-01
The NECTAC Technical Assistance (TA) Model for Long-Term Systems Change (LTSC) recognizes that components of a state system are highly interactive and changes at one level are not likely to be sustained without supportive changes at all related levels. Improved child and family outcomes require: intervention practices that are research-based,…
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.
ONE-DIMENSIONAL HYDRODYNAMIC/SEDIMENT TRANSPORT MODEL FOR STREAM NETWORKS: TECHNICAL REPORT
This technical report describes a new sediment transport model and the supporting post-processor, and sampling procedures for sediments in streams. Specifically, the following items are described herein: EFDC1D - This is a new one-dimensional hydrodynamic and sediment tr...
Technical performance of percutaneous leads for spinal cord stimulation: a modeling study
Manola, L.; Holsheimer, J.; Veltink, Petrus H.
Objective. To compare the technical performance of different percutaneous lead types for spinal cord stimulation. Methods. Using the UT-SCS software (University of Twente's spinal cord stimulation), lead models having similar characteristics such as the 3487A PISCES-Quad (PQ), 3887 PISCES-Quad
Research on power grid loss prediction model based on Granger causality property of time series
Energy Technology Data Exchange (ETDEWEB)
Wang, J. [North China Electric Power Univ., Beijing (China); State Grid Corp., Beijing (China); Yan, W.P.; Yuan, J. [North China Electric Power Univ., Beijing (China); Xu, H.M.; Wang, X.L. [State Grid Information and Telecommunications Corp., Beijing (China)
2009-03-11
This paper described a method of predicting power transmission line losses using the Granger causality property of time series. The stable property of the time series was investigated using unit root tests. The Granger causality relationship between line losses and other variables was then determined. Granger-caused time series were then used to create the following 3 prediction models: (1) a model based on line loss binomials that used electricity sales to predict variables, (2) a model that considered both power sales and grid capacity, and (3) a model based on autoregressive distributed lag (ARDL) approaches that incorporated both power sales and the square of power sales as variables. A case study of data from China's electric power grid between 1980 and 2008 was used to evaluate model performance. Results of the study showed that the model error rates ranged between 2.7 and 3.9 percent. 6 refs., 3 tabs., 1 fig.
2000-09-30
Mine Burial Assessment State-of the Art in Prediction and Modeling Workshop and Initiation of Technical Program Richard H. Bennett SEAPROBE, Inc 501...Assessment State-of the Art in Prediction and Modeling Workshop and Initiation of Technical Program 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM... Technical Program , Agenda, Background, and References, Bennett and Wilkens, 2000. d. Completed Reviews of the state-of-the-art practices in Mine Burial
Exploring New Physics Beyond the Standard Model: Final Technical Report
Energy Technology Data Exchange (ETDEWEB)
Wang, Liantao [Univ. of Chicago, IL (United States)
2016-10-17
This grant in 2015 to 2016 was for support in the area of theoretical High Energy Physics. The research supported focused mainly on the energy frontier, but it also has connections to both the cosmic and intensity frontiers. Lian-Tao Wang (PI) focused mainly on signal of new physics at colliders. The year 2015 - 2016, covered by this grant, has been an exciting period of digesting the influx of LHC data, understanding its meaning, and using it to refine strategies for deeper exploration. The PI proposed new methods of searching for new physics at the LHC, such as for the compressed stops. He also investigated in detail the signal of composite Higgs models, focusing on spin-1 composite resonances in the di-boson channel. He has also considered di-photon as a probe for such models. He has also made contributions in formulating search strategies of dark matter at the LHC, resulting in two documents with recommendations. The PI has also been active in studying the physics potential of future colliders, including Higgs factories and 100 TeV pp colliders. He has given comprehensive overview of the physics potential of the high energy proton collider, and outline its luminosity targets. He has also studied the use of lepton colliders to probe fermionic Higgs portal and bottom quark couplings to the Z boson.
A solution to the problem of constructing a state space model from time series
Directory of Open Access Journals (Sweden)
David Di Ruscio
1994-01-01
Full Text Available The problem of constructing minimal realizations from arbitrary input-output time series which are only covariance stationary (not necessarily stationary is considered. An algorithm which solves this problem for a fairly nonrestrictive class of exogenous (input signals is presented. The algorithm is based upon modeling nonzero exogenous signals by linear models and including these in the total system model.
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...
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
A Stepwise Time Series Regression Procedure for Water Demand Model Identification
Miaou, Shaw-Pin
1990-09-01
Annual time series water demand has traditionally been studied through multiple linear regression analysis. Four associated model specification problems have long been recognized: (1) the length of the available time series data is relatively short, (2) a large set of candidate explanatory or "input" variables needs to be considered, (3) input variables can be highly correlated with each other (multicollinearity problem), and (4) model error series are often highly autocorrelated or even nonstationary. A step wise time series regression identification procedure is proposed to alleviate these problems. The proposed procedure adopts the sequential input variable selection concept of stepwise regression and the "three-step" time series model building strategy of Box and Jenkins. Autocorrelated model error is assumed to follow an autoregressive integrated moving average (ARIMA) process. The stepwise selection procedure begins with a univariate time series demand model with no input variables. Subsequently, input variables are selected and inserted into the equation one at a time until the last entered variable is found to be statistically insignificant. The order of insertion is determined by a statistical measure called between-variable partial correlation. This correlation measure is free from the contamination of serial autocorrelation. Three data sets from previous studies are employed to illustrate the proposed procedure. The results are then compared with those from their original studies.
Monitoring Poisson time series using multi-process models
DEFF Research Database (Denmark)
Engebjerg, Malene Dahl Skov; Lundbye-Christensen, Søren; Kjær, Birgitte B.
Surveillance of infectious diseases based on routinely collected public health data is important for at least three reasons: The early detection of an epidemic may facilitate prompt interventions and the seasonal variations and long term trend may be of general epidemiological interest. Furthermore...... 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...
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.
Technical Analysis on Mechanical Model Based Football Curveball
Directory of Open Access Journals (Sweden)
Feng Li
2013-04-01
Full Text Available In this study, from the angles the physics and biomechanics, in the case of the curveball generated by the rotation problem analysis and exposition, considering the speed, rotation, the wall, the goalkeeper, goaltender factors and football running track and the theory trajectory deviation factor, making the model as much as possible to simulate the actual effect and using the MATLAB software to draw the flight of the ball trajectory simulation. Reference designed for teaching, training and competition as well as to further deepen the awareness and understanding of football curveball. It can improve the free kick guidance and not only help the shooter to select the best shooting methods, but also for the goalkeeper has targeted to fighting with reference.
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.
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.
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.
The Hydrogen Futures Simulation Model (H[2]Sim) technical description.
Energy Technology Data Exchange (ETDEWEB)
Jones, Scott A.; Kamery, William; Baker, Arnold Barry; Drennen, Thomas E.; Lutz, Andrew E.; Rosthal, Jennifer Elizabeth
2004-10-01
Hydrogen has the potential to become an integral part of our energy transportation and heat and power sectors in the coming decades and offers a possible solution to many of the problems associated with a heavy reliance on oil and other fossil fuels. The Hydrogen Futures Simulation Model (H2Sim) was developed to provide a high level, internally consistent, strategic tool for evaluating the economic and environmental trade offs of alternative hydrogen production, storage, transport and end use options in the year 2020. Based on the model's default assumptions, estimated hydrogen production costs range from 0.68 $/kg for coal gasification to as high as 5.64 $/kg for centralized electrolysis using solar PV. Coal gasification remains the least cost option if carbon capture and sequestration costs ($0.16/kg) are added. This result is fairly robust; for example, assumed coal prices would have to more than triple or the assumed capital cost would have to increase by more than 2.5 times for natural gas reformation to become the cheaper option. Alternatively, assumed natural gas prices would have to fall below $2/MBtu to compete with coal gasification. The electrolysis results are highly sensitive to electricity costs, but electrolysis only becomes cost competitive with other options when electricity drops below 1 cent/kWhr. Delivered 2020 hydrogen costs are likely to be double the estimated production costs due to the inherent difficulties associated with storing, transporting, and dispensing hydrogen due to its low volumetric density. H2Sim estimates distribution costs ranging from 1.37 $/kg (low distance, low production) to 3.23 $/kg (long distance, high production volumes, carbon sequestration). Distributed hydrogen production options, such as on site natural gas, would avoid some of these costs. H2Sim compares the expected 2020 per mile driving costs (fuel, capital, maintenance, license, and registration) of current technology internal combustion engine (ICE
On Fire regime modelling using satellite TM time series
Oddi, F.; . Ghermandi, L.; Lanorte, A.; Lasaponara, R.
2009-04-01
Wildfires can cause an environment deterioration modifying vegetation dynamics because they have the capacity of changing vegetation diversity and physiognomy. In semiarid regions, like the northwestern Patagonia, fire disturbance is also important because it could impact on the potential productivity of the ecosystem. There is reduction plant biomass and with that reducing the animal carrying capacity and/or the forest site quality with negative economics implications. Therefore knowledge of the fires regime in a region is of great importance to understand and predict the responses of vegetation and its possible effect on the regional economy. Studies of this type at a landscape level can be addressed using GIS tools. Satellite imagery allows detect burned areas and through a temporary analysis can be determined to fire regime and detecting changes at landscape scale. The study area of work is located on the east of the city of Bariloche including the San Ramon Ranch (22,000 ha) and its environs in the ecotone formed by the sub Antarctic forest and the patagonian steppe. We worked with multiespectral Landsat TM images and Landsat ETM + 30m spatial resolution obtained at different times. For the spatial analysis we used the software Erdas Imagine 9.0 and ArcView 3.3. A discrimination of vegetation types has made and was determined areas affected by fires in different years. We determined the level of change on vegetation induced by fire. In the future the use of high spatial resolution images combined with higher spectral resolution will allows distinguish burned areas with greater precision on study area. Also the use of digital terrain models derived from satellite imagery associated with climatic variables will allows model the relationship between them and the dynamics of vegetation.
Rotary ATPases: models, machine elements and technical specifications.
Stewart, Alastair G; Sobti, Meghna; Harvey, Richard P; Stock, Daniela
2013-01-01
Rotary ATPases are molecular rotary motors involved in biological energy conversion. They either synthesize or hydrolyze the universal biological energy carrier adenosine triphosphate. Recent work has elucidated the general architecture and subunit compositions of all three sub-types of rotary ATPases. Composite models of the intact F-, V- and A-type ATPases have been constructed by fitting high-resolution X-ray structures of individual subunits or sub-complexes into low-resolution electron densities of the intact enzymes derived from electron cryo-microscopy. Electron cryo-tomography has provided new insights into the supra-molecular arrangement of eukaryotic ATP synthases within mitochondria and mass-spectrometry has started to identify specifically bound lipids presumed to be essential for function. Taken together these molecular snapshots show that nano-scale rotary engines have much in common with basic design principles of man made machines from the function of individual "machine elements" to the requirement of the right "fuel" and "oil" for different types of motors.
Stochastic modeling of Lake Van water level time series with jumps and multiple trends
Directory of Open Access Journals (Sweden)
H. Aksoy
2013-06-01
Full Text Available In the 1990s, water level in the closed-basin Lake Van located in the Eastern Anatolia, Turkey, has risen up about 2 m. Analysis of the hydrometeorological data shows that change in the water level is related to the water budget of the lake. In this study, stochastic models are proposed for simulating monthly water level data. Two models considering mono- and multiple-trend time series are developed. The models are derived after removal of trend and periodicity in the dataset. Trend observed in the lake water level time series is fitted by mono- and multiple-trend lines. In the so-called mono-trend model, the time series is treated as a whole under the hypothesis that the lake water level has an increasing trend. In the second model (so-called multiple-trend, the time series is divided into a number of segments to each a linear trend can be fitted separately. Application on the lake water level data shows that four segments, each fitted with a trend line, are meaningful. Both the mono- and multiple-trend models are used for simulation of synthetic lake water level time series under the hypothesis that the observed mono- and multiple-trend structure of the lake water level persist during the simulation period. The multiple-trend model is found better for planning the future infrastructural projects in surrounding areas of the lake as it generates higher maxima for the simulated lake water level.
A probabilistic method for constructing wave time-series at inshore locations using model scenarios
Long, Joseph W.; Plant, Nathaniel G.; Dalyander, P. Soupy; Thompson, David M.
2014-01-01
Continuous time-series of wave characteristics (height, period, and direction) are constructed using a base set of model scenarios and simple probabilistic methods. This approach utilizes an archive of computationally intensive, highly spatially resolved numerical wave model output to develop time-series of historical or future wave conditions without performing additional, continuous numerical simulations. The archive of model output contains wave simulations from a set of model scenarios derived from an offshore wave climatology. Time-series of wave height, period, direction, and associated uncertainties are constructed at locations included in the numerical model domain. The confidence limits are derived using statistical variability of oceanographic parameters contained in the wave model scenarios. The method was applied to a region in the northern Gulf of Mexico and assessed using wave observations at 12 m and 30 m water depths. Prediction skill for significant wave height is 0.58 and 0.67 at the 12 m and 30 m locations, respectively, with similar performance for wave period and direction. The skill of this simplified, probabilistic time-series construction method is comparable to existing large-scale, high-fidelity operational wave models but provides higher spatial resolution output at low computational expense. The constructed time-series can be developed to support a variety of applications including climate studies and other situations where a comprehensive survey of wave impacts on the coastal area is of interest.
THE MODEL OF TEACHING A FOREIGN LANGUAGE FOR SPECIFIC PURPOSES IN A TECHNICAL UNIVERSITY
Directory of Open Access Journals (Sweden)
Cherkashina, E.I.
2017-06-01
Full Text Available The article presents a new model of a linguistic educational process that can be implemented in the practice of teaching a foreign language in a technical university. The proposed model takes into account the characteristic features of mindset of students of technical universities and faculties, and it constitutes a matrix with a binary opposition. Filled-in matrix cells represent a structure of the language knowledge content in a visual form. Knowledge of the system organization of a language helps the students to understand "language in action" in the way that corresponds to their left hemisphere mindset. The knowledge of the dominant hemisphere cerebration peculiarities of the students of technical specializations (engineering physicists lets us model a lingvo-educational process in a non-linguistic university. A complex linking of lingvo-didactic components makes the teachers of foreign language take into consideration the results of the research in the field of functional interhemispheric asymmetry of the brain. The emphasis on the abilities of the left hemisphere dominating among the students has to change the approach of the teachers of foreign languages to the organization of the linguistic educational process in a technical university. It is also important to consider that the skills which led the life in the information age remain necessary, but they alone are no longer sufficient for personal self-realization in the new conceptual age.
Almaraz, Pablo
2005-04-01
Time-series analyses in ecology usually involve the use of autoregressive modelling through direct and/or delayed difference equations, which severely restricts the ability of the modeler to structure complex causal relationships within a multivariate frame. This is especially problematic in the field of population regulation, where the proximate and ultimate causes of fluctuations in population size have been hotly debated for decades. Here it is shown that this debate can benefit from the implementation of structural modelling with latent constructs (SEM) to time-series analysis in ecology. A nonparametric bootstrap scheme illustrates how this modelling approach can circumvent some problems posed by the climate-ecology interface. Stochastic Monte Carlo simulation is further used to assess the effects of increasing time-series length and different parameter estimation methods on the performance of several model fit indexes. Throughout, the advantages and limitations of the SEM method are highlighted.
MIMO model of an interacting series process for Robust MPC via System Identification.
Wibowo, Tri Chandra S; Saad, Nordin
2010-07-01
This paper discusses the empirical modeling using system identification technique with a focus on an interacting series process. The study is carried out experimentally using a gaseous pilot plant as the process, in which the dynamic of such a plant exhibits the typical dynamic of an interacting series process. Three practical approaches are investigated and their performances are evaluated. The models developed are also examined in real-time implementation of a linear model predictive control. The selected model is able to reproduce the main dynamic characteristics of the plant in open-loop and produces zero steady-state errors in closed-loop control system. Several issues concerning the identification process and the construction of a MIMO state space model for a series interacting process are deliberated.
Directory of Open Access Journals (Sweden)
Dr. Dhrupad Mahtur
2005-01-01
Full Text Available This article stresses on the need for an e-application like Technical and Entrepreneurial Research Information System (TERIS, which enables interaction among academia, industry and various agencies related to researchers for sustainable entrepreneurship development. The functional details of the model are also discussed. This article is based on inputs with reference to the state of Rajasthan. However, the model can very well be replicated elsewhere.
Neutron scattering and models: Iron. Nuclear data and measurements series
Energy Technology Data Exchange (ETDEWEB)
Smith, A.B. [Argonne National Lab., IL (United States)
1995-08-01
Differential elastic and inelastic neutron-scattering cross sections of elemental iron are measured from 4.5 to 10 MeV in increments of {approx} 0.5 MeV. At each incident energy the measurements are made at forty or more scattering angles distributed between {approx} 17{degrees} and 160{degrees}, with emphasis on elastic scattering and inelastic scattering due to the excitation of the yrast 2{sup +} state. The measured data is combined with earlier lower-energy results from this laboratory, with recent high-precision {approx} 9.5 {yields} 15 MeV results from the Physilalisch Technische Bundesanstalt and with selected values from the literature to provide a detailed neutron-scattering data base extending from {approx} 1.5 to 26 MeV. This data is interpreted in the context of phenomenological spherical-optical and coupled-channels (vibrational and rotational) models, and physical implications discussed. Deformation, coupling, asymmetry and dispersive effects are explored. It is shown that, particularly in a collective context, a good description of the interaction of neutrons with iron is achieved over the energy range {approx} 0 {yields} 26 MeV, avoiding the dichotomy between high and low-energy interpretations found in previous work.
Travel cost inference from sparse, spatio-temporally correlated time series using markov models
DEFF Research Database (Denmark)
Yang, B.; Guo, C.; Jensen, C.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...
Multifractal Detrended Fluctuation Analysis of Interevent Time Series in a Modified OFC Model
Institute of Scientific and Technical Information of China (English)
LIN Min; YAN Shuang-Xi; ZHAO Gang; WANG Gang
2013-01-01
We use multifractal detrended fluctuation analysis (MF-DFA) method to investigate the multifractal behavior of the interevent time series in a modified Olami-Feder-Christensen (OFC) earthquake model on assortative scale-free networks.We determine generalized Hurst exponent and singularity spectrum and find that these fluctuations have multifractal nature.Comparing the MF-DFA results for the original interevent time series with those for shuffled and surrogate series,we conclude that the origin of multifractality is due to both the broadness of probability density function and long-range correlation.
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.
Modeling lanthanide series binding sites on humic acid.
Pourret, Olivier; Martinez, Raul E
2009-02-01
Lanthanide (Ln) binding to humic acid (HA) has been investigated by combining ultrafiltration and ICP-MS techniques. A Langmuir-sorption-isotherm metal-complexation model was used in conjunction with a linear programming method (LPM) to fit experimental data representing various experimental conditions both in HA/Ln ratio (varying between 5 and 20) and in pH range (from 2 to 10) with an ionic strength of 10(-3) mol L(-1). The LPM approach, not requiring prior knowledge of surface complexation parameters, was used to solve the existing discrepancies in LnHA binding constants and site densities. The application of the LPM to experimental data revealed the presence of two discrete metal binding sites at low humic acid concentrations (5 mg L(-1)), with log metal complexation constants (logK(S,j)) of 2.65+/-0.05 and 7.00 (depending on Ln). The corresponding site densities were 2.71+/-0.57x10(-8) and 0.58+/-0.32x10(-8) mol of Ln(3+)/mg of HA (depending on Ln). Total site densities of 3.28+/-0.28x10(-8), 4.99+/-0.02x10(-8), and 5.01+/-0.01x10(-8) mol mg(-1) were obtained by LPM for humic acid, for humic acid concentrations of 5, 10, and 20 mg L(-1), respectively. These results confirm that lanthanide binding occurs mainly at weak sites (i.e., ca. 80%) and second at strong sites (i.e., ca. 20%). The first group of discrete metal binding sites may be attributed to carboxylic groups (known to be the main binding sites of Ln in HA), and the second metal binding group to phenolic moieties. Moreover, this study evidences heterogeneity in the distribution of the binding sites among Ln. Eventually, the LPM approach produced feasible and reasonable results, but it was less sensitive to error and did not require an a priori assumption of the number and concentration of binding sites.
The estimated and look-ahead model of scientific and technical capacityof region
Directory of Open Access Journals (Sweden)
Anna Vital'evna Zolotukhina
2012-03-01
Full Text Available This paper studies the impact of scientific and technical capacity of Russian regions to the possibility of their sustainable development in the modern world. At the same time clarified the concept of «sustainable development», which in the extended treatment is disclosed in dynamic, static and efficiently-factorial aspects. The essential features of sustainable regional development (economic growth and high living standards, the effectiveness of the sectoral structure of economy, solidarity and partnership between the subjects of regional cooperation, coevolution, etc. within the framework of a comprehensive, integrative approach are identified.The algorithm of an indicative estimation of scientific and technical capacity of region for the purpose of research of its influence on sustainability of the social and economic development reveals; integrated indicators of a sustainable development and the scientific and technical capacity of the several Russian regions on the basis of computation of corresponding individual and private indicators are calculated. The choice of indicators due to the proposed theoretical and methodological approach to understanding the phenomena is under consideration. Generated by means of carrying out the correlation and regression analysis the econometric model allows to predict degree of stability of regional economy at escalating of separate components of scientific and technical capacity (in particular, its productive, human and financial components, identified in the analysis of the most important from the standpoint of sustainable development in the region. Results of practical application of model are approved on an example of regions of Privolzhsky Federal District
Stochastic modeling of Lake Van water level time series with jumps and multiple trends
Directory of Open Access Journals (Sweden)
H. Aksoy
2013-02-01
Full Text Available In 1990s, water level in the closed-basin Lake Van located in the Eastern Anatolia, Turkey has risen up about 2 m. Analysis of the hydrometeorological shows that change in the water level is related to the water budget of the lake. In this study, a stochastic model is generated using the measured monthly water level data of the lake. The model is derived after removal of trend and periodicity in the data set. Trend observed in the lake water level time series is fitted by mono- and multiple-trend lines. For the multiple-trend, the time series is first divided into homogeneous segments by means of SEGMENTER, segmentation software. Four segments are found meaningful practically each fitted with a trend line. Two models considering mono- and multiple-trend time series are developed. The multiple-trend model is found better for planning future development in surrounding areas of the lake.
Fluctuation complexity of agent-based financial time series model by stochastic Potts system
Hong, Weijia; Wang, Jun
2015-03-01
Financial market is a complex evolved dynamic system with high volatilities and noises, and the modeling and analyzing of financial time series are regarded as the rather challenging tasks in financial research. In this work, by applying the Potts dynamic system, a random agent-based financial time series model is developed in an attempt to uncover the empirical laws in finance, where the Potts model is introduced to imitate the trading interactions among the investing agents. Based on the computer simulation in conjunction with the statistical analysis and the nonlinear analysis, we present numerical research to investigate the fluctuation behaviors of the proposed time series model. Furthermore, in order to get a robust conclusion, we consider the daily returns of Shanghai Composite Index and Shenzhen Component Index, and the comparison analysis of return behaviors between the simulation data and the actual data is exhibited.
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.
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.
Pietkiewicz, A.; Tollik, D.; Klaassens, J. B.
1989-08-01
A simple small-signal low-frequency model of an idealized series resonant converter employing peak capacitor voltage prediction and switching frequency control is proposed. Two different versions of the model describe all possible conversion modes. It is found that step down modes offer better dynamic characteristics over most important network functions than do the step-up modes. The dynamical model of the series resonant converter with peak capacitor voltage prediction and switching frequency programming is much simpler than such popular control stategies as frequency VCO (voltage controlled oscillators) based control, or diode conduction angle control.
A new modeling and control scheme for thyristor-controlled series capacitor
Institute of Scientific and Technical Information of China (English)
Zhizhong MAO
2009-01-01
In order to design an optimal controller for the thyristor controlled series capacitor(TCSC),a novel TCSC control model is developed.In the model,the delay angle of thyristor valves is the input,and the inductor current is chosen as the output.Theoretical analysis and simulation studies show that TCSC is a non-linear system and its parameters vary with the operating point.In consideration of the special characteristics of the TCSC,an improved model algorithmic control (IMAC) scheme is proposed to control TCSC effectively.The good performance can be observed from simulation results when IMAC is applied to a series compensated radial system.
NON-LINEAR VIBRATION MODELING WITH THE HELP OF FUNCTIONAL SERIES
Directory of Open Access Journals (Sweden)
Z. M. Ghasanov
2010-06-01
Full Text Available The algorithm of modeling the significantly nonlinear processes – «black boxes» – is offered. It uses functional series. The algorithm is described on the example of modeling of complex oscillations, which occur in acoustic flaw detection.
DEFF Research Database (Denmark)
Ørregård Nielsen, Morten
2015-01-01
This article 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...
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...
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...
An Alternative Bayesian Approach to Structural Breaks in Time Series Models
S. van den Hauwe (Sjoerd); R. Paap (Richard); D.J.C. van Dijk (Dick)
2011-01-01
textabstractWe propose a new approach to deal with structural breaks in time series models. The key contribution is an alternative dynamic stochastic specification for the model parameters which describes potential breaks. After a break new parameter values are generated from a so-called baseline pr
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.
Multi-Scale Gaussian Processes: a Novel Model for Chaotic Time Series Prediction
Institute of Scientific and Technical Information of China (English)
ZHOU Ya-Tong; ZHANG Tai-Yi; SUN Jian-Cheng
2007-01-01
@@ Based on the classical Gaussian process (GP) model, we propose a multi-scale Gaussian process (MGP) model to predict the existence of chaotic time series. The MGP employs a covariance function that is constructed by a scaling function with its different dilations and translations, ensuring that the optimal hyperparameter is easy to determine.
Kusev, Petko; van Schaik, Paul; Tsaneva-Atanasova, Krasimira; Juliusson, Asgeir; Chater, Nick
2017-04-06
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
Analyzing Multiple Multivariate Time Series Data Using Multilevel Dynamic Factor Models.
Song, Hairong; Zhang, Zhiyong
2014-01-01
Multivariate time series data offer researchers opportunities to study dynamics of various systems in social and behavioral sciences. Dynamic factor model (DFM), as an idiographic approach for studying intraindividual variability and dynamics, has typically been applied to time series data obtained from a single unit. When multivariate time series data are collected from multiple units, how to synchronize dynamical information becomes a silent issue. To address this issue, the current study presented a multilevel dynamic factor model (MDFM) that analyzes multiple multivariate time series in multilevel SEM frameworks. MDFM not only disentangles within- and between-person variability but also models dynamics of the intraindividual processes. To illustrate the uses of MDFMs, we applied lag0, lag1, and lag2 MDFMs to empirical data on affect collected from 205 dating couples who had at least 50 consecutive days of observations. We also considered a model extension where the dynamical coefficients were allowed to be randomly varying in the population. The empirical analysis yielded interesting findings regarding affect regulation and coregulation within couples, demonstrating promising uses of MDFMs in analyzing multiple multivariate time series. In the end, we discussed a number of methodological issues in the applications of MDFMs and pointed out possible directions for future research.
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.
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.
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 decomposition methods were applied to meteorological and air quality data and their numerical model estimates. Decomposition techniques express a time series as the sum of a small number of independent modes which hypothetically represent identifiable forcings, thereb...
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.
Keil, Petr; Herben, Tomás; Rosindell, James; Storch, David
2010-07-07
There has recently been increasing interest in neutral models of biodiversity and their ability to reproduce the patterns observed in nature, such as species abundance distributions. Here we investigate the ability of a neutral model to predict phenomena observed in single-population time series, a study complementary to most existing work that concentrates on snapshots in time of the whole community. We consider tests for density dependence, the dominant frequencies of population fluctuation (spectral density) and a relationship between the mean and variance of a fluctuating population (Taylor's power law). We simulated an archipelago model of a set of interconnected local communities with variable mortality rate, migration rate, speciation rate, size of local community and number of local communities. Our spectral analysis showed 'pink noise': a departure from a standard random walk dynamics in favor of the higher frequency fluctuations which is partly consistent with empirical data. We detected density dependence in local community time series but not in metacommunity time series. The slope of the Taylor's power law in the model was similar to the slopes observed in natural populations, but the fit to the power law was worse. Our observations of pink noise and density dependence can be attributed to the presence of an upper limit to community sizes and to the effect of migration which distorts temporal autocorrelation in local time series. We conclude that some of the phenomena observed in natural time series can emerge from neutral processes, as a result of random zero-sum birth, death and migration. This suggests the neutral model would be a parsimonious null model for future studies of time series data.
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…
Bayesian dynamic modeling of time series of dengue disease case counts.
Directory of Open Access Journals (Sweden)
Daniel Adyro Martínez-Bello
2017-07-01
Full Text Available 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
Estimation of time of death with a fourier series unsteady-state heat transfer model.
Smart, Jimmy L
2010-11-01
The purpose of this study was to return to fundamental principles of heat transfer and derive a suitable model to establish a firm basis for constructing a postmortem human cooling curve. A Fourier Series Model was successfully applied to unsteady heat transfer within a wooden cylinder in controlled laboratory conditions. Wood has similar thermal diffusivity properties as human tissue. By manipulation of the model, sensitivity analyses were performed to observe the impact of changes in values of input variables. Variables of initial temperature of the cylinder and ambient surrounding temperature were shown to be very sensitive and have the most impact upon predictive results of the model. The model was also used to demonstrate the existence of an initial temperature plateau, which is often the subject of controversy in estimating time of death. Finally, it was demonstrated how the Fourier Series Model can be applied to estimate time of death for humans.
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.
Modeling of signal transmitting of avionic systems based on Volterra series
Directory of Open Access Journals (Sweden)
Юрий Владимирович Пепа
2014-11-01
Full Text Available The paper deals with mathematical modeling methods for the formation and transmission of analogue and digital avionics systems using Volterra series. A mathematical model of the modulation in the presence of various initial data is developed, the computer modeling is conducted. The processes of analog modulation is simulated using MATLAB+SIMULINK, which allows you to simulate these processes, as well as explore them.
On Modelling of Nonlinear Systems and Phenomena with the Use of Volterra and Wiener Series
Directory of Open Access Journals (Sweden)
Andrzej Borys
2015-03-01
Full Text Available This is a short tutorial on Volterra and Wiener series applications to modelling of nonlinear systems and phenomena, and also a survey of the recent achievements in this area. In particular, we show here how the philosophies standing behind each of the above theories differ from each other. On the other hand, we discuss also mathematical relationships between Volterra and Wiener kernels and operators. Also, the problem of a best approximation of large-scale nonlinear systems using Volterra operators in weighted Fock spaces is described. Examples of applications considered are the following: Volterra series use in description of nonlinear distortions in satellite systems and their equalization or compensation, exploiting Wiener kernels to modelling of biological systems, the use of both Volterra and Wiener theories in description of ocean waves and in magnetic resonance spectroscopy. Moreover, connections between Volterra series and neural network models, and also input-output descriptions of quantum systems by Volterra series are discussed. Finally, we consider application of Volterra series to solving some nonlinear problems occurring in hydrology, navigation, and transportation.
A new approach to calibrate steady groundwater flow models with time series of head observations
Obergfell, C.; Bakker, M.; Maas, C.
2012-04-01
We developed a new method to calibrate aquifer parameters of steady-state well field models using measured time series of head fluctuations. Our method is an alternative to standard pumping tests and is based on time series analysis using parametric impulse response functions. First, the pumping influence is isolated from the overall groundwater fluctuation observed at monitoring wells around the well field, and response functions are determined for each individual well. Time series parameters are optimized using a quasi-Newton algorithm. For one monitoring well, time series model parameters are also optimized by means of SCEM-UA, a Markov Chain Monte Carlo algorithm, as a control on the validity of the parameters obtained by the faster quasi-Newton method. Subsequently, the drawdown corresponding to an average yearly pumping rate is calculated from the response functions determined by time series analysis. The drawdown values estimated with acceptable confidence intervals are used as calibration targets of a steady groundwater flow model. A case study is presented of the drinking water supply well field of Waalwijk (Netherlands). In this case study, a uniform aquifer transmissivity is optimized together with the conductance of ditches in the vicinity of the well field. Groundwater recharge or boundary heads do not have to be entered, which eliminates two import sources of uncertainty. The method constitutes a cost-efficient alternative to pumping tests and allows the determination of pumping influences without changes in well field operation.
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.
2013-05-30
... COMMISSION Models for Plant-Specific Adoption of Technical Specifications Task Force Traveler TSTF-426... of Technical Specifications (TSs) Task Force (TSTF) Traveler TSTF-426, Revision 5, ``Revise or Add... finds the proposed TS (Volume 1) and TS Bases (Volume 2) changes in Traveler TSTF-426 acceptable for...
2013-01-17
... From the Federal Register Online via the Government Publishing Office NUCLEAR REGULATORY COMMISSION Proposed Models for Plant-Specific Adoption of Technical Specifications Task Force Traveler TSTF... (SE) for plant- specific adoption of Technical Specifications (TS) Task Force (TSTF) Traveler TSTF-426...
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Guy J. Abel
2013-12-01
Full Text Available Background: Population forecasts are widely used for public policy purposes. Methods to quantify the uncertainty in forecasts tend to ignore model uncertainty and to be based on a single model. Objective: In this paper, we use Bayesian time series models to obtain future population estimates with associated measures of uncertainty. The models are compared based on Bayesian posterior model probabilities, which are then used to provide model-averaged forecasts. Methods: The focus is on a simple projection model with the historical data representing population change in England and Wales from 1841 to 2007. Bayesian forecasts to the year 2032 are obtained based on a range of models, including autoregression models, stochastic volatility models and random variance shift models. The computational steps to fit each of these models using the OpenBUGS software via R are illustrated. Results: We show that the Bayesian approach is adept in capturing multiple sources of uncertainty in population projections, including model uncertainty. The inclusion of non-constant variance improves the fit of the models and provides more realistic predictive uncertainty levels. The forecasting methodology is assessed through fitting the models to various truncated data series.
Technical Review of the CENWP Computational Fluid Dynamics Model of the John Day Dam Forebay
Energy Technology Data Exchange (ETDEWEB)
Rakowski, Cynthia L.; Serkowski, John A.; Richmond, Marshall C.
2010-12-01
The US Army Corps of Engineers Portland District (CENWP) has developed a computational fluid dynamics (CFD) model of the John Day forebay on the Columbia River to aid in the development and design of alternatives to improve juvenile salmon passage at the John Day Project. At the request of CENWP, Pacific Northwest National Laboratory (PNNL) Hydrology Group has conducted a technical review of CENWP's CFD model run in CFD solver software, STAR-CD. PNNL has extensive experience developing and applying 3D CFD models run in STAR-CD for Columbia River hydroelectric projects. The John Day forebay model developed by CENWP is adequately configured and validated. The model is ready for use simulating forebay hydraulics for structural and operational alternatives. The approach and method are sound, however CENWP has identified some improvements that need to be made for future models and for modifications to this existing model.
Time-series analysis with a hybrid Box-Jenkins ARIMA and neural network model
Institute of Scientific and Technical Information of China (English)
Dilli R Aryal; WANG Yao-wu(王要武)
2004-01-01
Time-series analysis is important to a wide range of disciplines transcending both the physical and social sciences for proactive policy decisions. Statistical models have sound theoretical basis and have been successfully used in a number of problem domains in time series forecasting. Due to power and flexibility, Box-Jenkins ARIMA model has gained enormous popularity in many areas and research practice for the last three decades.More recently, the neural networks have been shown to be a promising alternative tool for modeling and forecasting owing to their ability to capture the nonlinearity in the data. However, despite the popularity and the superiority of ARIMA and ANN models, the empirical forecasting performance has been rather mixed so that no single method is best in every situation. In this study, a hybrid ARIMA and neural networks model to time series forecasting is proposed. The basic idea behind the model combination is to use each model's unique features to capture different patterns in the data. With three real data sets, empirical results evidently show that the hybrid model outperforms ARIMA and ANN model noticeably in terms of forecasting accuracy used in isolation.
A Series Solution of the Cauchy Problem for Turing Reaction-diffusion Model
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L. Päivärinta
2011-12-01
Full Text Available In this paper, the series pattern solution of the Cauchy problem for Turing reaction-diffusion model is obtained by using the homotopy analysis method (HAM. Turing reaction-diffusion model is nonlinear reaction-diffusion system which usually has power-law nonlinearities or may be rewritten in the form of power-law nonlinearities. Using the HAM, it is possible to find the exact solution or an approximate solution of the problem. This technique provides a series of functions which converges rapidly to the exact solution of the problem. The efficiency of the approach will be shown by applying the procedure on two problems. Furthermore, the so-called homotopy-Pade technique (HPT is applied to enlarge the convergence region and rate of solution series given by the HAM.
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...... (2012, 2013). The latter component is modelled such that the unconditional time-varying component evolves slowly over time. Statistical inference is used for specifying the parameterization of the time-varying component by applying a sequence of Lagrange multiplier tests. The model building procedure...... 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...
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......). The latter component is modelled by incorporating smooth changes so that the unconditional variance is allowed to evolve slowly over time. Statistical inference is used for specifying the parameterization of the time-varying component by applying a sequence of Lagrange multiplier tests. The model building...... 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...
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
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......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...
Markov Chain Modelling for Short-Term NDVI Time Series Forecasting
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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 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.
Generation of Natural Runoff Monthly Series at Ungauged Sites Using a Regional Regressive Model
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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.
Prediction of altimetric sea level anomalies using time series models based on spatial correlation
Miziński, Bartłomiej; Niedzielski, Tomasz
2014-05-01
Sea level anomaly (SLA) times series, which are time-varying gridded data, can be modelled and predicted using time series methods. This approach has been shown to provide accurate forecasts within the Prognocean system, the novel infrastructure for anticipating sea level change designed and built at the University of Wrocław (Poland) which utilizes the real-time SLA data from Archiving, Validation and Interpretation of Satellite Oceanographic data (AVISO). The system runs a few models concurrently, and our ocean prediction experiment includes both uni- and multivariate time series methods. The univariate ones are: extrapolation of polynomial-harmonic model (PH), extrapolation of polynomial-harmonic model and autoregressive prediction (PH+AR), extrapolation of polynomial-harmonic model and self-exciting threshold autoregressive prediction (PH+SETAR). The following multivariate methods are used: extrapolation of polynomial-harmonic model and vector autoregressive prediction (PH+VAR), extrapolation of polynomial-harmonic model and generalized space-time autoregressive prediction (PH+GSTAR). As the aforementioned models and the corresponding forecasts are computed in real time, hence independently and in the same computational setting, we are allowed to compare the accuracies offered by the models. The objective of this work is to verify the hypothesis that the multivariate prediction techniques, which make use of cross-correlation and spatial correlation, perform better than the univariate ones. The analysis is based on the daily-fitted and updated time series models predicting the SLA data (lead time of two weeks) over several months when El Niño/Southern Oscillation (ENSO) was in its neutral state.
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
Energy Technology Data Exchange (ETDEWEB)
Nappo, C.J.; Eckman, R.M.; Rao, K.S.; Herwehe, J.A.; Gunter, L.
1998-06-01
This report summarizes a verification of the SCIPUFF model as descried in the draft report PC-SCIPUFF Version 0.2 Technical Documentation by Sykes et al. The verification included a scientific review of the model physics and parameterizations described in the report, and checks for their internal usage and consistency with current practices in atmospheric dispersion modeling. This work is intended to examine the scientific basis and defensiblity of the model for the intended application. A related task is an assessment of the model`s general capabilities and limitations. A line-by-line verification of the computer source code was not possible; however, the code was checked with a commercial code analyzer. About 47 potential coding inconsistencies were identified.
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.
Data on copula modeling of mixed discrete and continuous neural time series
Directory of Open Access Journals (Sweden)
Meng Hu
2016-06-01
Full Text Available 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.
Gil-Alana, L.A.; Moreno, A; Pérez-de-Gracia, F. (Fernando)
2011-01-01
The last 20 years have witnessed a considerable increase in the use of time series techniques in econometrics. The articles in this important set have been chosen to illustrate the main themes in time series work as it relates to econometrics. The editor has written a new concise introduction to accompany the articles. Sections covered include: Ad Hoc Forecasting Procedures, ARIMA Modelling, Structural Time Series Models, Unit Roots, Detrending and Non-stationarity, Seasonality, Seasonal Adju...
Directory of Open Access Journals (Sweden)
GHEORGHE CLAUDIU FEIES
2012-05-01
Full Text Available After studying how the operators’ management works, an influence of the specific activities of public utilities on their financial accounting system can be noticed. The asymmetry of these systems is also present, resulting from organization and specific services, which implies a close link between the financial accounting system and the specialized technical department. The research methodology consists in observing specific activities of public utility operators and their influence on information system and analysis views presented in the context of published work in some journals. It analyses the impact of technical computing models used by public utility community services on the financial statements and therefore the information provided by accounting information system stakeholders.
Point Processes Modeling of Time Series Exhibiting Power-Law Statistics
Kaulakys, B; Gontis, V
2010-01-01
We consider stochastic point processes generating time series exhibiting power laws of spectrum and distribution density (Phys. Rev. E 71, 051105 (2005)) and apply them for modeling the trading activity in the financial markets and for the frequencies of word occurrences in the language.
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...
Time series modeling of daily abandoned calls in a call centre ...
African Journals Online (AJOL)
Time series modeling of daily abandoned calls in a call centre. ... were shown to be both parsimonious and adequate using the P-P plots, Q-Q plots and residual analysis. ... The data for application were got from a GSM telephone provider.
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 a
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...
The Evolutionary Modeling and Short-range Climatic Prediction for Meteorological Element Time Series
Institute of Scientific and Technical Information of China (English)
YU Kangqing; ZHOU Yuehua; YANG Jing'an; KANG Zhuo
2005-01-01
The time series of precipitation in flood season (May-September) at Wuhan Station, which is set as an example of the kind of time series with chaos characters, is split into two parts: One includes macro climatic timescale period waves that are affected by some relatively steady climatic factors such as astronomical factors (sunspot, etc.), some other known and/or unknown factors, and the other includes micro climatic timescale period waves superimposed on the macro one. The evolutionary modeling (EM), which develops from genetic programming (GP), is supposed to be adept at simulating the former part because it creates the nonlinear ordinary differential equation (NODE) based upon the data series. The natural fractals (NF)are used to simulate the latter part. The final prediction is the sum of results from both methods, thus the model can reflect multi-time scale effects of forcing factors in the climate system. The results of this example for 2002 and 2003 are satisfactory for climatic prediction operation. The NODE can suggest that the data vary with time, which is beneficial to think over short-range climatic analysis and prediction. Comparison in principle between evolutionary modeling and linear modeling indicates that the evolutionary one is a better way to simulate the complex time series with nonlinear characteristics.
Commandeur, J.J.F. Wesemann, P. Bijleveld, F.D. Chhoun, V. & Sann, S.
2017-01-01
The authors present the methodology used for estimating forecasts for the number of road traffic fatalities in 2011—2020 in Cambodia based on observed developments in Cambodian road traffic fatalities and motor vehicle ownership in the years 1995—2009. Using the latent risk time series model
Institute of Scientific and Technical Information of China (English)
SUN Futian; SUN Liqun; YANG Guanglin
2008-01-01
Scientific and technical progress has been the driving forces of enterprises development. Milk productive enterprises are developing faster and growing better. It is very important to measure the contributive ratio of scientific and technical progress in milk productive enterprises. And the appraisement could help to develop milk productive enterprises. The model C2GS2 was established to appraise the contributive ratio of scientific and technical progress in milk productive enterprises in the research. And the appraisement on the contributive ratio of scientific and technical progress in milk productive enterprises was made by the model. In the results of appraisement, science and technology play a main role in milk productive enterprises. It is shown that our milk productive enterprises are developed by scientific and technical progress while not by input of productive factors.
Kastner, T A; Walsh, K K; Criscione, T
1997-08-01
We presented a general model of the structure and functioning of managed care and described elements (provider networks, fiscal elements, risk estimation, case-mix, management information systems, practice parameters, and quality improvement) critical to service delivery for people with developmental disabilities. A number of technical elements of managed care systems were delineated and reviewed in relation to the inclusion of people with developmental disabilities. Several managed care demonstration projects were described and, finally, a multi-year hypothetical budget model, including long-term care, was presented as a framework for considering how managed care affects specific service structures. Implications for people with developmental disabilities were discussed.
Comparison of time series models for predicting campylobacteriosis risk in New Zealand.
Al-Sakkaf, A; Jones, G
2014-05-01
Predicting campylobacteriosis cases is a matter of considerable concern in New Zealand, after the number of the notified cases was the highest among the developed countries in 2006. Thus, there is a need to develop a model or a tool to predict accurately the number of campylobacteriosis cases as the Microbial Risk Assessment Model used to predict the number of campylobacteriosis cases failed to predict accurately the number of actual cases. We explore the appropriateness of classical time series modelling approaches for predicting campylobacteriosis. Finding the most appropriate time series model for New Zealand data has additional practical considerations given a possible structural change, that is, a specific and sudden change in response to the implemented interventions. A univariate methodological approach was used to predict monthly disease cases using New Zealand surveillance data of campylobacteriosis incidence from 1998 to 2009. The data from the years 1998 to 2008 were used to model the time series with the year 2009 held out of the data set for model validation. The best two models were then fitted to the full 1998-2009 data and used to predict for each month of 2010. The Holt-Winters (multiplicative) and ARIMA (additive) intervention models were considered the best models for predicting campylobacteriosis in New Zealand. It was noticed that the prediction by an additive ARIMA with intervention was slightly better than the prediction by a Holt-Winter multiplicative method for the annual total in year 2010, the former predicting only 23 cases less than the actual reported cases. It is confirmed that classical time series techniques such as ARIMA with intervention and Holt-Winters can provide a good prediction performance for campylobacteriosis risk in New Zealand. The results reported by this study are useful to the New Zealand Health and Safety Authority's efforts in addressing the problem of the campylobacteriosis epidemic. © 2013 Blackwell Verlag GmbH.
Directory of Open Access Journals (Sweden)
Szolgayová Elena
2014-03-01
Full Text Available Short term streamflow forecasting is important for operational control and risk management in hydrology. Despite a wide range of models available, the impact of long range dependence is often neglected when considering short term forecasting. In this paper, the forecasting performance of a new model combining a long range dependent autoregressive fractionally integrated moving average (ARFIMA model with a wavelet transform used as a method of deseasonalization is examined. It is analysed, whether applying wavelets in order to model the seasonal component in a hydrological time series, is an alternative to moving average deseasonalization in combination with an ARFIMA model. The one-to-ten-steps-ahead forecasting performance of this model is compared with two other models, an ARFIMA model with moving average deseasonalization, and a multiresolution wavelet based model. All models are applied to a time series of mean daily discharge exhibiting long range dependence. For one and two day forecasting horizons, the combined wavelet - ARFIMA approach shows a similar performance as the other models tested. However, for longer forecasting horizons, the wavelet deseasonalization - ARFIMA combination outperforms the other two models. The results show that the wavelets provide an attractive alternative to the moving average deseasonalization.
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.
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.
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.
Research on Optimize Prediction Model and Algorithm about Chaotic Time Series
Institute of Scientific and Technical Information of China (English)
JIANG Wei-jin; XU Yu-sheng
2004-01-01
We put forward a chaotic estimating model.by using the parameter of the chaotic system, sensitivity of the parameter to inching and control the disturbance of the system, and estimated the parameter of the model by using the best update option.In the end, we forecast the intending series value in its mutually space.The example shows that it can increase the precision in the estimated process by selecting the best model steps.It not only conquer the abuse of using detention inlay technology alone, but also decrease blindness of using forecast error to decide the input model directly, and the result of it is better than the method of statistics and other series means.
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
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...... preferable to at-site estimation in moderately heterogeneous and homogeneous regions for large sample sizes. Modest intersite dependence has only a small effect on the performance of the regional index-flood estimator....
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.
Structural damage detection using ARMAX time series models and cepstral distances
Indian Academy of Sciences (India)
K LAKSHMI; A RAMA MOHAN RAO
2016-09-01
A novel damage detection algorithm for structural health monitoring using time series model is presented. The proposed algorithm uses output-only acceleration time series obtained from sensors on the structure which are fitted using Auto-regressive moving-average with exogenous inputs (ARMAX) model. The algorithm uses Cepstral distances between the ARMAX models of decorrelated data obtained from healthy and any other current condition of the structure as the damage indicator. A numerical model of a simply supported beam with variations due to temperature and operating conditions along with measurement noise is used to demonstrate the effectiveness of the proposed damage diagnostic technique using the ARMAX time series models and their Cepstral distances with novelty indices. The effectiveness of the proposed method is validatedusing the benchmark data of the 8-DOF system made available to public by the Engineering Institute of LANL and the simulated vibration data obtained from the FEM model of IASC-ASCE 12-DOF steel frame. The results of the studies indicate that the proposed algorithm is robust in identifying the damage from the acceleration datacontaminated with noise under varied environmental and operational conditions.
Zakynthinaki, M. S.; Stirling, J. R.
2007-01-01
Stochastic optimization is applied to the problem of optimizing the fit of a model to the time series of raw physiological (heart rate) data. The physiological response to exercise has been recently modeled as a dynamical system. Fitting the model to a set of raw physiological time series data is, however, not a trivial task. For this reason and in order to calculate the optimal values of the parameters of the model, the present study implements the powerful stochastic optimization method ALOPEX IV, an algorithm that has been proven to be fast, effective and easy to implement. The optimal parameters of the model, calculated by the optimization method for the particular athlete, are very important as they characterize the athlete's current condition. The present study applies the ALOPEX IV stochastic optimization to the modeling of a set of heart rate time series data corresponding to different exercises of constant intensity. An analysis of the optimization algorithm, together with an analytic proof of its convergence (in the absence of noise), is also presented.
Zhu, Y. K.; Yu, Y. G.; Li, L.; Jiang, T.; Wang, X. Y.; Zheng, X. J.
2016-07-01
A Timoshenko beam model combined with piezoelectric constitutive equations and an electrical model was proposed to describe the energy harvesting performances of multilayered d 15 mode PZT-51 piezoelectric bimorphs in series and parallel connections. The effect of different clamped conditions was considered for non-piezoelectric and piezoelectric layers in the theoretical model. The frequency dependences of output peak voltage and power at different load resistances and excitation voltages were studied theoretically, and the results were verified by finite element modeling (FEM) simulation and experimental measurements. Results show that the theoretical model considering different clamped conditions for non-piezoelectric and piezoelectric layers could make a reliable prediction for the energy harvesting performances of multilayered d 15 mode piezoelectric bimorphs. The multilayered d 15 mode piezoelectric bimorph in a series connection exhibits a higher output peak voltage and power than that of a parallel connection at a load resistance of 1 MΩ. A criterion for choosing a series or parallel connection for a multilayered d 15 mode piezoelectric bimorph is dependent on the comparison of applied load resistance with the critical resistance of about 55 kΩ. The proposed model may provide some useful guidelines for the design and performance optimization of d 15 mode piezoelectric energy harvesters.
High-temperature series analyses of the classical Heisenberg and XY model
Adler, J; Janke, W
1993-01-01
Although there is now a good measure of agreement between Monte Carlo and high-temperature series expansion estimates for Ising ($n=1$) models, published results for the critical temperature from series expansions up to 12{\\em th} order for the three-dimensional classical Heisenberg ($n=3$) and XY ($n=2$) model do not agree very well with recent high-precision Monte Carlo estimates. In order to clarify this discrepancy we have analyzed extended high-temperature series expansions of the susceptibility, the second correlation moment, and the second field derivative of the susceptibility, which have been derived a few years ago by L\\"uscher and Weisz for general $O(n)$ vector spin models on $D$-dimensional hypercubic lattices up to 14{\\em th} order in $K \\equiv J/k_B T$. By analyzing these series expansions in three dimensions with two different methods that allow for confluent correction terms, we obtain good agreement with the standard field theory exponent estimates and with the critical temperature estimates...
Directory of Open Access Journals (Sweden)
Trottier Helen
2006-08-01
Full Text Available Abstract The goal of this paper is to analyze the stochastic dynamics of childhood infectious disease time series. We present an univariate time series analysis of pertussis, mumps, measles and rubella based on Box-Jenkins or AutoRegressive Integrated Moving Average (ARIMA modeling. The method, which enables the dependency structure embedded in time series data to be modeled, has potential research applications in studies of infectious disease dynamics. Canadian chronological series of pertussis, mumps, measles and rubella, before and after mass vaccination, are analyzed to characterize the statistical structure of these diseases. Despite the fact that these infectious diseases are biologically different, it is found that they are all represented by simple models with the same basic statistical structure. Aside from seasonal effects, the number of new cases is given by the incidence in the previous period and by periodically recurrent random factors. It is also shown that mass vaccination does not change this stochastic dependency. We conclude that the Box-Jenkins methodology does identify the collective pattern of the dynamics, but not the specifics of the diseases at the biological individual level.
A Bayesian Surrogate Model for Rapid Time Series Analysis and Application to Exoplanet Observations
Ford, Eric B; Veras, Dimitri
2011-01-01
We present a Bayesian surrogate model for the analysis of periodic or quasi-periodic time series data. We describe a computationally efficient implementation that enables Bayesian model comparison. We apply this model to simulated and real exoplanet observations. We discuss the results and demonstrate some of the challenges for applying our surrogate model to realistic exoplanet data sets. In particular, we find that analyses of real world data should pay careful attention to the effects of uneven spacing of observations and the choice of prior for the "jitter" parameter.
Nonlinear Behaviors of Tail Dependence and Cross-Correlation of Financial Time Series Model
Directory of Open Access Journals (Sweden)
Wei Deng
2014-01-01
Full Text Available Nonlinear behaviors of tail dependence and cross-correlation of financial time series are reproduced and investigated by stochastic voter dynamic system. The voter process is a continuous-time Markov process and is one of the interacting dynamic systems. The tail dependence of return time series for pairs of Chinese stock markets and the proposed financial models is studied by copula analysis, in an attempt to detect and illustrate the existence of relevant correlation relationships. Further, the multifractality of cross-correlations for return series is studied by multifractal detrended cross-correlation analysis, which indicates the analogous cross-correlations and some fractal characters for both actual data and simulative data and provides an intuitive evidence for market inefficiency.
Neural modeling for time series: A statistical stepwise method for weight elimination.
Cottrell, M; Girard, B; Girard, Y; Mangeas, M; Muller, C
1995-01-01
Many authors use feedforward neural networks for modeling and forecasting time series. Most of these applications are mainly experimental, and it is often difficult to extract a general methodology from the published studies. In particular, the choice of architecture is a tricky problem. We try to combine the statistical techniques of linear and nonlinear time series with the connectionist approach. The asymptotical properties of the estimators lead us to propose a systematic methodology to determine which weights are nonsignificant and to eliminate them to simplify the architecture. This method (SSM or statistical stepwise method) is compared to other pruning techniques and is applied to some artificial series, to the famous Sunspots benchmark, and to daily electrical consumption data.
Generation of future high-resolution rainfall time series with a disaggregation model
Müller, Hannes; Haberlandt, Uwe
2017-04-01
High-resolution rainfall data are needed in many fields of hydrology and water resources management. For analyzes of future rainfall condition climate scenarios exist with hourly values of rainfall. However, the direct usage of these data is associated with uncertainties which can be indicated by comparisons of observations and C20 control runs. An alternative is the derivation of changes of rainfall behavior over the time from climate simulations. Conclusions about future rainfall conditions can be drawn by adding these changes to observed time series. A multiplicative cascade model is used in this investigation for the disaggregation of daily rainfall amounts to hourly values. Model parameters can be estimated by REMO rainfall time series (UBA-, BfG- and ENS-realization), based on ECHAM5. Parameter estimation is carried out for C20 period as well as near term and long term future (2021-2050 and 2071-2100). Change factors for both future periods are derived by parameter comparisons and added to the parameters estimated from observed time series. This enables the generation of hourly rainfall time series from observed daily values with respect to future changes. The investigation is carried out for rain gauges in Lower Saxony. Generated Time series are analyzed regarding statistical characteristics, e.g. extreme values, event-based (wet spell duration and amounts, dry spell duration, …) and continuum characteristics (average intensity, fraction of dry intervals,…). The generation of the time series is validated by comparing the changes in the statistical characteristics from the REMO data and from the disaggregated data.
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.
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.
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.
Series-expansion thermal tensor network approach for quantum lattice models
Chen, Bin-Bin; Liu, Yun-Jing; Chen, Ziyu; Li, Wei
2017-04-01
We propose a series-expansion thermal tensor network (SETTN) approach for efficient simulations of quantum lattice models. This continuous-time SETTN method is based on the numerically exact Taylor series expansion of the equilibrium density operator e-β H (with H the total Hamiltonian and β the imaginary time), and is thus Trotter-error free. We discover, through simulating XXZ spin chain and square-lattice quantum Ising models, that not only the Hamiltonian H , but also its powers Hn, can be efficiently expressed as matrix product operators, which enables us to calculate with high precision the equilibrium and dynamical properties of quantum lattice models at finite temperatures. Our SETTN method provides an alternative to conventional Trotter-Suzuki renormalization-group (RG) approaches, and achieves a very high standard of thermal RG simulations in terms of accuracy and flexibility.
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.
Multi-factor high-order intuitionistic fuzzy time series forecasting model
Institute of Scientific and Technical Information of China (English)
Yanan Wang; Yingjie Lei; Yang Lei; Xiaoshi Fan
2016-01-01
Fuzzy sets theory cannot describe the neutrality degree of data, which has largely limited the objectivity of fuzzy time series in uncertain data forecasting. With this regard, a multi-factor high-order intuitionistic fuzzy time series forecasting model is built. In the new model, a fuzzy clustering algorithm is used to get unequal intervals, and a more objective technique for ascertaining member-ship and non-membership functions of the intuitionistic fuzzy set is proposed. On these bases, forecast rules based on multidimen-sional intuitionistic fuzzy modus ponens inference are established. Final y, contrast experiments on the daily mean temperature of Beijing are carried out, which show that the novel model has a clear advantage of improving the forecast accuracy.
Intuitionistic Fuzzy Time Series Forecasting Model Based on Intuitionistic Fuzzy Reasoning
Directory of Open Access Journals (Sweden)
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.
Chiou, Wen-Bin; Yang, Chao-Chin
2006-01-01
In this study, modeling advantage that depicts the likelihood of a teacher model being imitated by students over other competing models in a particular class was developed to differentiate the rival modeling of two kinds of teachers (the technical teachers vs. the lecturing teachers) between college students' learning styles and occupational stereotypes in the collaborative teaching of technical courses. Results of a one-semester longitudinal study indicated that the students perceived a greater modeling advantage of the technical teachers than that of the lecturing teachers. Both the students' learning styles and occupational stereotypes were in accordance with those teachers as their role models. In general, the impact of the teachers' learning styles and occupational stereotypes on students appeared to be mediated by the teachers' modeling advantage. Administrators and curriculum designers should pay attention to the fact that the technical teachers appeared to exhibit greater modeling effects than the lecturing teachers in collaborative teaching.
High-Order Fuzzy Time Series Model Based on Generalized Fuzzy Logical Relationship
Directory of Open Access Journals (Sweden)
Wangren Qiu
2013-01-01
Full Text Available In view of techniques for constructing high-order fuzzy time series models, there are three methods which are based on advanced algorithms, computational methods, and grouping the fuzzy logical relationships, respectively. The last kind model has been widely applied and researched for the reason that it is easy to be understood by the decision makers. To improve the fuzzy time series forecasting model, this paper presents a novel high-order fuzzy time series models denoted as GTS(M,N on the basis of generalized fuzzy logical relationships. Firstly, the paper introduces some concepts of the generalized fuzzy logical relationship and an operation for combining the generalized relationships. Then, the proposed model is implemented in forecasting enrollments of the University of Alabama. As an example of in-depth research, the proposed approach is also applied to forecast the close price of Shanghai Stock Exchange Composite Index. Finally, the effects of the number of orders and hierarchies of fuzzy logical relationships on the forecasting results are discussed.
Application of Time-Series Model to Predict Groundwater Dynamic in Sanjiang Plain,Northeast China
Institute of Scientific and Technical Information of China (English)
LUAN Zhaoqing; LIU Guihua; YAN Baixing
2011-01-01
To study the groundwater dynamic in the typical region of Sanjiang Plain,long-term groundwater level observation data in the Honghe State Farm were collected and analyzed in this paper.The seasonal and long-term groundwater dynamic was explored.From 1996 to 2008,groundwater level kept declining due to intensive exploitation of groundwater resources for rice irrigation.A decline of nearly 5 m was found for almost all the monitoring wells.A time-series method was established to model the groundwater dynamic.Modeled results by time-series model showed that the groundwater level in this region would keep declining according to the current exploitation intensity.A total dropdown of 1.07 m would occur from 2009 to 2012.Time-series model can be used to model and forecast the groundwater dynamic with high accuracy.Measures including control on groundwater exploitation amount and application of water saving irrigation technique should be taken to prevent the continuing declining of groundwater in the Sanjiang Plain.
Technical note: The Lagrangian particle dispersion model FLEXPART version 6.2
Directory of Open Access Journals (Sweden)
A. Stohl
2005-01-01
Full Text Available The Lagrangian particle dispersion model FLEXPART was originally (about 8 years ago designed for calculating the long-range and mesoscale dispersion of air pollutants from point sources, such as after an accident in a nuclear power plant. In the meantime FLEXPART has evolved into a comprehensive tool for atmospheric transport modeling and analysis. Its application fields were extended from air pollution studies to other topics where atmospheric transport plays a role (e.g., exchange between the stratosphere and troposphere, or the global water cycle. It has evolved into a true community model that is now being used by at least 25 groups from 14 different countries and is seeing both operational and research applications. A user manual has been kept actual over the years and was distributed over an internet page along with the model's source code. In this note we provide a citeable technical description of FLEXPART's latest version (6.2.
Modeling self-sustained activity cascades in socio-technical networks
Piedrahíta, Pablo; Moreno, Yamir; Arenas, Alex
2013-01-01
The ability to understand and eventually predict the emergence of information and activation cascades in social networks is core to complex socio-technical systems research. However, the complexity of social interactions makes this a challenging enterprise. Previous works on cascade models assume that the emergence of this collective phenomenon is related to the activity observed in the local neighborhood of individuals, but do not consider what determines the willingness to spread information in a time-varying process. Here we present a mechanistic model that accounts for the temporal evolution of the individual state in a simplified setup. We model the activity of the individuals as a complex network of interacting integrate-and-fire oscillators. The model reproduces the statistical characteristics of the cascades in real systems, and provides a framework to study time-evolution of cascades in a state-dependent activity scenario.
Fractional Black-Scholes Model and Technical Analysis of Stock Price
Directory of Open Access Journals (Sweden)
Song Xu
2013-01-01
Full Text Available In the stock market, some popular technical analysis indicators (e.g., Bollinger bands, RSI, ROC, etc. are widely used to forecast the direction of prices. The validity is shown by observed relative frequency of certain statistics, using the daily (hourly, weekly, etc. stock prices as samples. However, those samples are not independent. In earlier research, the stationary property and the law of large numbers related to those observations under Black-Scholes stock price model and stochastic volatility model have been discussed. Since the fitness of both Black-Scholes model and short-range dependent process has been questioned, we extend the above results to fractional Black-Scholes model with Hurst parameter H>1/2, under which the stock returns follow a kind of long-range dependent process. We also obtain the rate of convergence.
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
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
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
2016-08-01
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.
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.
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.
A regional GIS-based model for reconstructing natural monthly streamflow series at ungauged sites
Pumo, Dario; Lo Conti, Francesco; Viola, Francesco; Noto, Leonardo V.
2016-04-01
Several hydrologic applications require reliable estimates of monthly runoff in river basins to face the widespread lack of data, both in time and in space. The main aim of this work is to propose a regional model for the estimation of monthly natural runoff series at ungauged sites, analyzing its applicability, reliability and limitations. A GIS (Geographic Information System) based model is here developed and applied to the entire region of Sicily (Italy). The core of this tool is a regional model for the estimation of monthly natural runoff series, based on a simple modelling structure, consisting of a regression based rainfall-runoff model with only four parameters. The monthly runoff is obtained as a function of precipitation and mean temperature at the same month and runoff at the previous month. For a given basin, the four model parameters are assessed by specific regional equations as a function of some easily measurable geomorphic and climate basins' descriptors. The model is calibrated by a "two-step" procedure applied to a number of gauged basins over the region. The first step is aimed at the identification of a set of parameters optimizing model performances at the level of single basin. Such "optimal" parameters sets, derived for each calibration basin, are successively used inside a regional regression analysis, performed at the second step, by which the regional equations for model parameters assessment are defined and calibrated. All the gauged watersheds across the Sicily have been analyzed, selecting 53 basins for model calibration and using other 6 basins exclusively for validation purposes. Model performances, quantitatively evaluated considering different statistical indexes, demonstrate a relevant model ability in capturing the observed hydrological response at both the monthly level and higher time scales (seasonal and annual). One of the key features related to the proposed methodology is its easy transferability to other arid and semiarid
Further Results on “An Endogenous Growth Model with Embodied Energy-Saving Technical Change”
Hakan Yetkiner; Adrian von Zon
2007-01-01
In this short paper we add a non-renewable resource sector to van Zon and Yetkiner (2003) that extended Romer (1990) by including energy consumption of intermediate goods in a context of endogenous and embodied technical change. Van Zon and Yetkiner (2003) showed that the growth rate depends negatively on the growth of exogenous real energy prices. In this paper, we endogenise the growth rate of real energy prices by introducing a non-renewable resource sector into the model. This allows us t...
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 ×109 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.
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.
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...
Anderson, Daniel
2012-01-01
This manuscript provides an overview of hierarchical linear modeling (HLM), as part of a series of papers covering topics relevant to consumers of educational research. HLM is tremendously flexible, allowing researchers to specify relations across multiple "levels" of the educational system (e.g., students, classrooms, schools, etc.).…
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.
Technical-economic modelling of integrated water management: wastewater reuse in a French island.
Xu, P; Valette, F; Brissaud, F; Fazio, A; Lazarova, V
2001-01-01
An integrated technical-economic model is used to address water management issues in the French island of Noirmoutier. The model simulates potable water production and supply, potable and non potable water demand and consumption, wastewater collection, treatment and disposal, water storage, transportation and reuse. A variety of water management scenarios is assessed through technical, economic and environmental evaluation. The scenarios include wastewater reclamation and reuse for agricultural and landscape irrigation as well as domestic non potable application, desalination of seawater and brackish groundwater for potable water supply. The study shows that, in Noirmoutier, wastewater reclamation and reuse for crop irrigation is the most cost-effective solution to the lack of water resources and the protection of sensitive environment. Some water management projects which are regarded as having less economic benefit in the short-term may become competitive in the future, as a result of tightened environmental policy, changed public attitudes and advanced water treatment technologies. The model provides an appropriate tool for water resources planning and management.
Indian Academy of Sciences (India)
Dilip P Ahalpara; Amit Verma; Jiterndra C Parikh; Prasanta K Panigrahi
2008-09-01
A method based on wavelet transform is developed to characterize variations at multiple scales in non-stationary time series. We consider two different financial time series, S&P CNX Nifty closing index of the National Stock Exchange (India) and Dow Jones industrial average closing values. These time series are chosen since they are known to comprise of stochastic fluctuations as well as cyclic variations at different scales. The wavelet transform isolates cyclic variations at higher scales when random fluctuations are averaged out; this corroborates correlated behaviour observed earlier in financial time series through random matrix studies. Analysis is carried out through Haar, Daubechies-4 and continuous Morlet wavelets for studying the character of fluctuations at different scales and show that cyclic variations emerge at intermediate time scales. It is found that Daubechies family of wavelets can be effectively used to capture cyclic variations since these are local in nature. To get an insight into the occurrence of cyclic variations, we then proceed to model these wavelet coefficients using genetic programming (GP) approach and using the standard embedding technique in the reconstructed phase space. It is found that the standard methods (GP as well as artificial neural networks) fail to model these variations because of poor convergence. A novel interpolation approach is developed that overcomes this difficulty. The dynamical model equations have, primarily, linear terms with additive Padé-type terms. It is seen that the emergence of cyclic variations is due to an interplay of a few important terms in the model. Very interestingly GP model captures smooth variations as well as bursty behaviour quite nicely.
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.
Water quality management using statistical analysis and time-series prediction model
Parmar, Kulwinder Singh; Bhardwaj, Rashmi
2014-12-01
This paper deals with water quality management using statistical analysis and time-series prediction model. The monthly variation of water quality standards has been used to compare statistical mean, median, mode, standard deviation, kurtosis, skewness, coefficient of variation at Yamuna River. Model validated using R-squared, root mean square error, mean absolute percentage error, maximum absolute percentage error, mean absolute error, maximum absolute error, normalized Bayesian information criterion, Ljung-Box analysis, predicted value and confidence limits. Using auto regressive integrated moving average model, future water quality parameters values have been estimated. It is observed that predictive model is useful at 95 % confidence limits and curve is platykurtic for potential of hydrogen (pH), free ammonia, total Kjeldahl nitrogen, dissolved oxygen, water temperature (WT); leptokurtic for chemical oxygen demand, biochemical oxygen demand. Also, it is observed that predicted series is close to the original series which provides a perfect fit. All parameters except pH and WT cross the prescribed limits of the World Health Organization /United States Environmental Protection Agency, and thus water is not fit for drinking, agriculture and industrial use.
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.
Wavelet time series MPARIMA modeling for power system short term load forecasting
Institute of Scientific and Technical Information of China (English)
冉启文; 单永正; 王建赜; 王骐
2003-01-01
The wavelet power system short term load forecasting(STLF) uses a mulriple periodical autoregressive integrated moving average(MPARIMA) model to model the mulriple near-periodicity, nonstationarity and nonlinearity existed in power system short term quarter-hour load time series, and can therefore accurately forecast the quarter-hour loads of weekdays and weekends, and provide more accurate results than the conventional techniques, such as artificial neural networks and autoregressive moving average(ARMA) models test results. Obtained with a power system networks in a city in Northeastern part of China confirm the validity of the approach proposed.
Time series models to simulate and forecast hourly averaged wind speeds in Quetta, Pakistan
Energy Technology Data Exchange (ETDEWEB)
Lalarukh Kamal [Balochistan University, Quetta (Pakistan). Dept. of Mathematics; Yasmin Zahra Jafri [Balochistan University, Quetta (Pakistan). Dept. of Statistics
1997-07-01
Stochastic simulation and forecast models of hourly average wind speeds are presented. Time series models take into account several basic features of wind speed data including autocorrelation, non-Gaussian distribution and diurnal nonstationarity. The positive correlation between consecutive wind speed observations is taken into account by fitting an ARMA (p,q) process to wind speed data transformed to make their distribution approximately Gaussian and standardized to remove scattering of transformed data. Diurnal variations have been taken into account to observe forecasts and its dependence on lead times. We find the ARMA (p,q) model suitable for prediction intervals and probability forecasts. (author)
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.
Modeling and Simulation of Series Compensator to Mitigate Power Quality Problems
Directory of Open Access Journals (Sweden)
S.Sadaiappan,
2010-12-01
Full Text Available Power Electronics and Advanced Control technologies have made it possible to mitigate power quality problems and maintain the operation of sensitive loads. Among power system disturbances, voltage sags, swells and harmonics are some of the severe problems to the sensitive loads. The series compensation method is best suited to protect such loads against those disturbances. The use of a series compensator (SC to improve power quality is an isolated power system is investigated. The role of the compensator is not only to mitigate the effects of voltage sag, but also to reduce the harmonic distortion due to the presence of non linear loads in the network. In this paper, a series compensator is proposed and a method of harmonic compensation is described and a method to mitigate voltage sag is investigated. The proposed series compensator consists of Energy Storage System (ESS and Voltage Source Inverter (VSI, Injection Transformer. The ESS can be a capacitor of suitable capacity. ESS would act as a buffer and generally provides the energy needed for load ride-through during voltage sag. Injection Transformer is used to inject the voltage in transmission line in appropriate level.In this way the terminal voltage of the protected sensitive load can be regulated to maintain a constant level. The modeling and imulation of the proposed series compensator was implemented in Matlab Simulink work space. Simulation results showed that the proposed series compensator was efficient in mitigating voltage sags and harmonics and thus improve the power quality of the isolated power system. This approach is different from conventional methods and provides effective solution. If this method is enhanced in future it could provide much more improved power quality.
Ahalpara, D P; Parikh, J C; Verma, A; Ahalpara, Dilip P.; Panigrahi, Prasanta K.; Parikh, Jitendra C.; Verma, Amit
2006-01-01
A method based on wavelet transform and genetic programming is proposed for characterizing and modeling variations at multiple scales in non-stationary time series. The cyclic variations, extracted by wavelets and smoothened by cubic splines, are well captured by genetic programming in the form of dynamical equations. For the purpose of illustration, we analyze two different non-stationary financial time series, S&P CNX Nifty closing index of the National Stock Exchange (India) and Dow Jones industrial average closing values through Haar, Daubechies-4 and continuous Morlet wavelets for studying the character of fluctuations at different scales, before modeling the cyclic behavior through GP. Cyclic variations emerge at intermediate time scales and the corresponding dynamical equations reveal characteristic behavior at different scales.
Prawirodirdjo, Linette; Ben-Zion, Yehuda; Bock, Yehuda
2006-02-01
We suggest that strain in the elastic part of the Earth's crust induced by surface temperature variations is a significant contributor to the seasonal variations observed in the spatially filtered daily position time series of Southern California Integrated GPS Network (SCIGN) stations. We compute the predicted thermoelastic strain from the observed local atmospheric temperature record assuming an elastically decoupled layer over a uniform elastic half-space and compare the seasonal variations in thermoelastic strain to the horizontal GPS position time series. We consider three regions (Palmdale, 29 Palms, and Idyllwild), each with one temperature station and three to six GPS stations. The temperature time series is used to compute thermoelastic strain at each station on the basis of its relative location in the temperature field. For each region we assume a wavelength for the temperature field that is related to the local topography. The depth of the decoupled layer is inferred from the phase delay between the temperature record and the GPS time series. The relative amplitude of strain variation at each GPS station, calculated to be on the order of 0.1 μstrain, is related to the relative location of that station in the temperature field. The goodness of fit between model and data is evaluated from the relative amplitudes of the seasonal signals, as well as the appropriateness of the chosen temperature field wavelength and decoupled layer depth. The analysis shows a good fit between the predicted strains and the GPS time series. This suggests that the model captures the key first-order ingredients that determine the thermoelastic strain in a given area. The results can be used to improve the signal/noise ratio in GPS data.
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...
A note on the Fourier series model for analysing line transect data.
Buckland, S T
1982-06-01
The Fourier series model offers a powerful procedure for the estimation of animal population density from line transect data. The estimate is reliable over a wide range of detection functions. In contrast, analytic confidence intervals yield, at best, 90% confidence for nominal 95% intervals. Three solutions, one using Monte Carlo techniques, another making direct use of replicate lines and the third based on the jackknife method, are discussed and compared.
Uniting Mandelbrot’s Noah and Joseph Effects in Toy Models of Natural Hazard Time Series
Credgington, D.; Watkins, N. W.; Chapman, S. C.; Rosenberg, S. J.; Sanchez, R.
2009-12-01
The forecasting of extreme events is a highly topical, cross-disciplinary problem. One aspect which is potentially tractable even when the events themselves are stochastic is the probability of a “burst” of a given size and duration, defined as the area between a time series and a constant threshold. Many natural time series depart from the simplest, Brownian, case and in the 1960s Mandelbrot developed the use of fractals to describe these departures. In particular he proposed two kinds of fractal model to capture the way in which natural data is often persistent in time (his “Joseph effect”, common in hydrology and exemplified by fractional Brownian motion) and/or prone to heavy tailed jumps (the “Noah effect”, typical of economic index time series, for which he gave Levy flights as an examplar). Much of the earlier modelling, however, has emphasised one of the Noah and Joseph parameters (the tail exponent mu and one derived from the temporal behaviour such as power spectral beta) at the other one's expense. I will describe work [1] in which we applied a simple self-affine stable model-linear fractional stable motion (LFSM)-which unifies both effects to better describe natural data, in this case from space physics. I will show how we have resolved some contradictions seen in earlier work, where purely Joseph or Noah descriptions had been sought. I will also show recent work [2] using numerical simulations of LFSM and simple analytic scaling arguments to study the problem of the area between a fractional Levy model time series and a threshold. [1] Watkins et al, Space Science Reviews [2005] [2] Watkins et al, Physical Review E [2009
Renormalized scattering series for frequency-domain waveform modelling of strong velocity contrasts
Jakobsen, M.; Wu, R. S.
2016-08-01
An improved description of scattering and inverse scattering processes in reflection seismology may be obtained on the basis of a scattering series solution to the Helmoltz equation, which allows one to separately model primary and multiple reflections. However, the popular scattering series of Born is of limited seismic modelling value, since it is only guaranteed to converge if the global contrast is relatively small. For frequency-domain waveform modelling of realistic contrasts, some kind of renormalization may be required. The concept of renormalization is normally associated with quantum field theory, where it is absolutely essential for the treatment of infinities in connection with observable quantities. However, the renormalization program is also highly relevant for classical systems, especially when there are interaction effects that act across different length scales. In the scattering series of De Wolf, a renormalization of the Green's functions is achieved by a split of the scattering potential operator into fore- and backscattering parts; which leads to an effective reorganization and partially re-summation of the different terms in the Born series, so that their order better reflects the physics of reflection seismology. It has been demonstrated that the leading (single return) term in the De Wolf series (DWS) gives much more accurate results than the corresponding Born approximation, especially for models with high contrasts that lead to a large accumulation of phase changes in the forward direction. However, the higher order terms in the DWS that are associated with internal multiples have not been studied numerically before. In this paper, we report from a systematic numerical investigation of the convergence properties of the DWS which is based on two new operator representations of the DWS. The first operator representation is relatively similar to the original scattering potential formulation, but more global and explicit in nature. The second
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 b
75 FR 3127 - Airworthiness Directives; Thrush Aircraft, Inc. Model 600 S2D and S2R Series Airplanes
2010-01-20
... wing front lower spar caps in Thrush Aircraft, Inc. Model 600 S2D and S2R (S-2R) series airplanes (type..., which applies to Thrush Aircraft, Inc. Model 600 S2D and S2R (S-2R) series airplanes (type certificate... Environmental Conditions Avenger Aircraft and Services (Avenger) states the life limits for the wing front...
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 b
2013-12-17
... Federal Aviation Administration 14 CFR Part 25 Special Conditions: Airbus, Model A350-900 Series Airplane... Model A350-900 series airplanes. These airplanes will have a novel or unusual design feature(s... sending written comments, data, or views. The most helpful comments reference a specific portion of...
Yang, Hyun-Ho; Han, Chang-Hoon; Oen Lee, Jeong; Yoon, Jun-Bo
2014-06-01
As a powerful method to reduce actuation voltage in an electrostatic micro-actuator, we propose and investigate an electrostatic micro-actuator with a pre-charged series capacitor. In contrast to a conventional electrostatic actuator, the injected pre-charges into the series capacitor can freely modulate the pull-in voltage of the proposed actuator even after the completion of fabrication. The static characteristics of the proposed actuator were investigated by first developing analytical models based on a parallel-plate capacitor model. We then successfully designed and demonstrated a micro-switch with a pre-charged series capacitor. The pull-in voltage of the fabricated micro-switch was reduced from 65.4 to 0.6 V when pre-charged with 46.3 V. The on-resistance of the fabricated micro-switch was almost the same as the initial one, even when the device was pre-charged, which was demonstrated for the first time. All results from the analytical models, finite element method simulations, and measurements were in good agreement with deviations of less than 10%. This work can be favorably adapted to electrostatic micro-switches which need a low actuation voltage without noticeable degradation of performance.
Directory of Open Access Journals (Sweden)
K. Ishioka
2000-01-01
Full Text Available Several technical suggestions to construct a high-resolution spectral model on a sphere (the T682 barotropic model are presented and their implementation of FORTRAN77 libraries is provided as a free software package ISPACK (http://www.gfd-dennou.org/arch/ispack/. A test experiment on decaying turbulence is conducted to demonstrate the ability of the model.
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
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.
Directory of Open Access Journals (Sweden)
Perepelytsya O.A.
2013-08-01
Full Text Available The possibilities of improving sportsmanship hockey on grass-based modeling-based approach. The aim is to study the dynamics of technical preparedness of highly qualified hockey players on grass under the influence of experimental summer system of development a training process. The experiment involved 21 athlete (average age - 23.7 years. Installed speaker technical training of highly qualified hockey players on grass during the annual macrocycle. The identified model parameters of technical preparedness of the players on each of the main stages of the annual training cycle. Reserves in terms of technical training are seen in increasing performance testing exercises on speed. It is recommended to eliminate the imbalance in the use of specific and nonspecific means.
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.
Neighbourhood selection for local modelling and prediction of hydrological time series
Jayawardena, A. W.; Li, W. K.; Xu, P.
2002-02-01
The prediction of a time series using the dynamical systems approach requires the knowledge of three parameters; the time delay, the embedding dimension and the number of nearest neighbours. In this paper, a new criterion, based on the generalized degrees of freedom, for the selection of the number of nearest neighbours needed for a better local model for time series prediction is presented. The validity of the proposed method is examined using time series, which are known to be chaotic under certain initial conditions (Lorenz map, Henon map and Logistic map), and real hydro meteorological time series (discharge data from Chao Phraya river in Thailand, Mekong river in Thailand and Laos, and sea surface temperature anomaly data). The predicted results are compared with observations, and with similar predictions obtained by using arbitrarily fixed numbers of neighbours. The results indicate superior predictive capability as measured by the mean square errors and coefficients of variation by the proposed approach when compared with the traditional approach of using a fixed number of neighbours.
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";}
DEFF Research Database (Denmark)
Finlay, Chris; Olsen, Nils; Tøffner-Clausen, Lars
th order spline representation with knot points spaced at 0.5 year intervals. The resulting field model is able to consistently fit data from six independent low Earth orbit satellites: Oersted, CHAMP, SAC-C and the three Swarm satellites. As an example, we present comparisons of the excellent model......Ten months of data from ESA's Swarm mission, together with recent ground observatory monthly means, are used to update the CHAOS series of geomagnetic field models with a focus on time-changes of the core field. As for previous CHAOS field models quiet-time, night-side, data selection criteria...
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
Time-series gas prediction model using LS-SVR within a Bayesian framework
Institute of Scientific and Technical Information of China (English)
Qiao Meiying; Ma Xiaoping; Lan Jianyi; Wang Ying
2011-01-01
The traditional least squares support vector regression (LS-SVR) model, using cross validation to determine the regularization parameter and kernel parameter, is time-consuming. We propose a Bayesian evidence framework to infer the LS-SVR model parameters. Three levels Bayesian inferences are used to determine the model parameters, regularization hyper-parameters and tune the nuclear parameters by model comparison. On this basis, we established Bayesian LS-SVR time-series gas forecasting models and provide steps for the algorithm. The gas outburst data of a Hebi 10th mine working face is used to validate the model. The optimal embedding dimension and delay time of the time series were obtained by the smallest differential entropy method. Finally, within a MATLAB7.1 environment, we used actual coal gas data to compare the traditional LS-SVR and the Bayesian LS-SVR with LS-SVMlab1.5 Toolbox simulation. The results show that the Bayesian framework of an LS-SVR significantly improves the speed and accuracy of the forecast
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.
Jian Ma; Yueru Ma; Yong Bai; Bing Xia
2014-01-01
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 resul...
Partitioning and interpolation based hybrid ARIMA–ANN model for time series forecasting
Indian Academy of Sciences (India)
C NARENDRA BABU; PALLAVIRAM SURE
2016-07-01
Time series data (TSD) originating from different applications have dissimilar characteristics. Hence for prediction of TSD, diversified varieties of prediction models exist. In many applications, hybrid models provide more accurate predictions than individual models. One such hybrid model, namely auto regressive integrated moving average – artificial neural network (ARIMA–ANN) is devised in many different ways in the literature. However, the prediction accuracy of hybrid ARIMA–ANN model can be further improved by devising suitable processing techniques. In this paper, a hybrid ARIMA–ANN model is proposed, which combines the concepts of the recently developed moving average (MA) filter based hybrid ARIMA–ANN model, with a processing technique involving a partitioning–interpolation (PI) step. The improved prediction accuracy of the proposed PI based hybrid ARIMA–ANN model is justified using a simulation experiment.Further, on different experimental TSD like sunspots TSD and electricity price TSD, the proposed hybrid model is applied along with four existing state-of-the-art models and it is found that the proposed model outperforms all the others, and hence is a promising model for TSD prediction
Time Series Model of Occupational Injuries Analysis in Ghanaian Mines-A Case Study
Directory of Open Access Journals (Sweden)
S.J. Aidoo
2012-02-01
Full Text Available This study has modeled occupational injuries at Gold Fields Ghana Limited (GFGL, Tarkwa Mine using time series analysis. Data was collected from the Safety and Environment Department from January 2007 to December 2010. Testing for stationarity condition using line graph from Statistical Package for Social Sciences (SPSS 17.0 edition failed, hence the use of Box-Jenkins method of differencing which tested positive after the first difference. ARIMA (1,1,1 model was then applied in modeling the stationary data and model diagnostic was done to ensure its appropriateness. The model was further used to forecast the occurrence of injuries at GFGL for two year period spanning from January 2011 to December 2012. The results show that occupational injuries for GFGL are going to have a slight upward and downward movement from January 2011 to May 2011, after which there will be stability (almost zero from June 2011 to December 2012.
A Trend-Switching Financial Time Series Model with Level-Duration Dependence
Directory of Open Access Journals (Sweden)
Qingsheng Wang
2012-01-01
overcome the difficult problem that motivates our researches in this paper. An asymmetric and nonlinear model with the change of local trend depending on local high-low turning point process is first proposed in this paper. As the point process can be decomposed into the two different processes, a high-low level process and an up-down duration process, we then establish the so-called trend-switching model which depends on both level and duration (Trend-LD. The proposed model can predict efficiently the direction and magnitude of the local trend of a time series by incorporating the local high-low turning point information. The numerical results on six indices in world stock markets show that the proposed Trend-LD model is suitable for fitting the market data and able to outperform the traditional random walk model.
Directory of Open Access Journals (Sweden)
Lihua Yang
2015-04-01
Full Text Available Export volume forecasting of fresh fruits is a complex task due to the large number of factors affecting the demand. In order to guide the fruit growers’ sales, decreasing the cultivating cost and increasing their incomes, a hybrid fresh apple export volume forecasting model is proposed. Using the actual data of fresh apple export volume, the Seasonal Decomposition (SD model of time series and Radial Basis Function (RBF model of artificial neural network are built. The predictive results are compared among the three forecasting model based on the criterion of Mean Absolute Percentage Error (MAPE. The result indicates that the proposed combined forecasting model is effective because it can improve the prediction accuracy of fresh apple export volumes.
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......, the voltage source representation of SSSC is transformed to current source, and then this current is injected at the sending and auxiliary buses. These injected currents at the terminals of SSSC are a function of the required line flow and voltage of buses. These currents can be included easily...... to the original mismatches at the terminal buses of SSSC. The developed model can be used to control active and reactive line flow together or individually. The implicit modeling of SSSC device decreases the complexity of load flow code, the modification of Jacobian matrix is avoided, the change only...
Directory of Open Access Journals (Sweden)
I MADE ARYA ANTARA
2015-02-01
Full Text Available This paper aimed to elaborates and compares the performance of Fuzzy Time Series (FTS model with Markov Chain (MC model in forecasting the Gross Regional Domestic Product (GDRP of Bali Province. Both methods were considered as forecasting methods in soft modeling domain. The data used was quarterly data of Bali’s GDRP for year 1992 through 2013 from Indonesian Bureau of Statistic at Denpasar Office. Inspite of using the original data, rate of change from two consecutive quarters was used to model. From the in-sample forecasting conducted, we got the Average Forecasting Error Rate (AFER for FTS dan MC models as much as 0,78 percent and 2,74 percent, respectively. Based-on these findings, FTS outperformed MC in in-sample forecasting for GDRP of Bali’s data.
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.
Time Series Model of Wind Speed for Multi Wind Turbines based on Mixed Copula
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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.
Samadi, Reza
Technical textiles are increasingly being engineered and used in challenging applications, in areas such as safety, biomedical devices, architecture and others, where they must meet stringent demands including excellent and predictable load bearing capabilities. They also form the bases for one of the most widespread group of composite materials, fibre reinforced polymer-matrix composites (PMCs), which comprise materials made of stiff and strong fibres generally available in textile form and selected for their structural potential, combined with a polymer matrix that gives parts their shape. Manufacturing processes for PMCs and technical textiles, as well as parts and advanced textile structures must be engineered, ideally through simulation, and therefore diverse properties of the textiles, textile reinforcements and PMC materials must be available for predictive simulation. Knowing the detailed geometry of technical textiles is essential to predicting accurately the processing and performance properties of textiles and PMC parts. In turn, the geometry taken by a textile or a reinforcement textile is linked in an intricate manner to its constitutive behaviour. This thesis proposes, investigates and validates a general numerical tool for the integrated and comprehensive analysis of textile geometry and constitutive behaviour as required toward engineering applications featuring technical textiles and textile reinforcements. The tool shall be general with regards to the textiles modelled and the loading cases applied. Specifically, the work aims at fulfilling the following objectives: 1) developing and implementing dedicated simulation software for modelling textiles subjected to various load cases; 2) providing, through simulation, geometric descriptions for different textiles subjected to different load cases namely compaction, relaxation and shear; 3) predicting the constitutive behaviour of the textiles undergoing said load cases; 4) identifying parameters
Directory of Open Access Journals (Sweden)
O. F. Shikhova
2012-01-01
Full Text Available The paper considers the research findings aimed at the developing the new quality testing technique for students assessment at Technical Higher School. The model of multilevel estimation means is provided for diagnosing the level of general cultural and professional competences of students doing a bachelor degree in technological fields. The model implies the integrative character of specialists training - the combination of both the psycho-pedagogic (invariable and engineering (variable components, as well as the qualimetric approach substantiating the system of students competence estimation and providing the most adequate assessment means. The principles of designing the multilevel estimation means are defined along with the methodology approaches to their implementation. For the reasonable selection of estimation means, the system of quality criteria is proposed by the authors, being based on the group expert assessment. The research findings can be used for designing the competence-oriented estimation means.
Technical Manual for the Geospatial Stream Flow Model (GeoSFM)
Asante, Kwabena O.; Artan, Guleid A.; Pervez, Md Shahriar; Bandaragoda, Christina; Verdin, James P.
2008-01-01
The monitoring of wide-area hydrologic events requires the use of geospatial and time series data available in near-real time. These data sets must be manipulated into information products that speak to the location and magnitude of the event. Scientists at the U.S. Geological Survey Earth Resources Observation and Science (USGS EROS) Center have implemented a hydrologic modeling system which consists of an operational data processing system and the Geospatial Stream Flow Model (GeoSFM). The data processing system generates daily forcing evapotranspiration and precipitation data from various remotely sensed and ground-based data sources. To allow for rapid implementation in data scarce environments, widely available terrain, soil, and land cover data sets are used for model setup and initial parameter estimation. GeoSFM performs geospatial preprocessing and postprocessing tasks as well as hydrologic modeling tasks within an ArcView GIS environment. The integration of GIS routines and time series processing routines is achieved seamlessly through the use of dynamically linked libraries (DLLs) embedded within Avenue scripts. GeoSFM is run operationally to identify and map wide-area streamflow anomalies. Daily model results including daily streamflow and soil water maps are disseminated through Internet map servers, flood hazard bulletins and other media.
Onken, Lisa Simon; Blaine, Jack D.
This monograph is based on the papers from a technical review. These papers are included: (1) Psychotherapy and Counseling Research in Drug Abuse Treatment: Questions, Problems, and Solutions (Lisa Onken, Jack Blaine); (2) Psychotherapy and Counseling for Methadone-Maintained Opiate Addicts: Results of Research Studies (George Woody, A. T.…
Blank, Uel; And Others
From 1979 to 1982 an extension education program provided assistance to the tourism industry in rural communities adjoining northeastern Minnesota's Boundary Waters Canoe Area (BWCA). Program activities involved needs assessment, educational and technical assistance to communities and tourism-related firms, marketing programs, grants management…
UNESCO-UNEVOC International Centre for Technical and Vocational Education and Training, 2006
2006-01-01
This discussion paper presents an overview of key concepts, trends and issues in the field of Technical and Vocational Education and Training (TVET) for sustainable development. It examines interlinkages between the world of work and environmental, social and economic aspects of sustainable development, as well as ways in which TVET can be…
United Nations Educational, Scientific, and Cultural Organization, Paris (France). Div. of Science, Technical and Environmental Education.
A new Unesco project seeks to increase the capacity of developing countries to strengthen primary school academic performance by improving children's nutrition and health status. The first technical meeting of the new project took place in Stockholm, Sweden, in April 1989. Three working groups were formed which focused on assessment, intervention,…
Interception modeling with vegetation time series derived from Landsat TM data
Polo, M. J.; Díaz-Gutiérrez, A.; González-Dugo, M. P.
2011-11-01
Rainfall interception by the vegetation may constitute a significant fraction in the water budget at local and watershed scales, especially in Mediterranean areas. Different approaches can be found to model locally the interception fraction, but a distributed analysis requires time series of vegetation along the watershed for the study period, which includes both type of vegetation and ground cover fraction. In heterogeneous watersheds, remote sensing is usually the only viable alternative to characterize medium to large size areas, but the high number of scenes necessary to capture the temporal variability during long periods, together with the sometimes extreme scarcity of data during the wet season, make it necessary to deal with a limited number of images and interpolate vegetation maps between consecutive dates. This work presents an interception model for heterogeneous watersheds which combines an interception continuous simulation derived from Gash model and their derivations, and a time series of vegetation cover fraction and type from Landsat TM data and vegetation inventories. A mountainous watershed in Southern Spain where a physical hydrological modelling had been previously calibrated was selected for this study. The dominant species distribution and their relevant characteristics regarding the interception process were analyzed from literature and digital cartography; the evolution of the vegetation cover fraction along the watershed during the study period (2002-2005) was produced by the application of a NDVI analysis on the available scenes of Landsat TM images. This model was further calibrated by field data collected in selected areas in the watershed.
A sequential approach to calibrate ecosystem models with multiple time series data
Oliveros-Ramos, Ricardo; Verley, Philippe; Echevin, Vincent; Shin, Yunne-Jai
2017-02-01
When models are aimed to support decision-making, their credibility is essential to consider. Model fitting to observed data is one major criterion to assess such credibility. However, due to the complexity of ecosystem models making their calibration more challenging, the scientific community has given more attention to the exploration of model behavior than to a rigorous comparison to observations. This work highlights some issues related to the comparison of complex ecosystem models to data and proposes a methodology for a sequential multi-phases calibration (or parameter estimation) of ecosystem models. We first propose two criteria to classify the parameters of a model: the model dependency and the time variability of the parameters. Then, these criteria and the availability of approximate initial estimates are used as decision rules to determine which parameters need to be estimated, and their precedence order in the sequential calibration process. The end-to-end (E2E) ecosystem model ROMS-PISCES-OSMOSE applied to the Northern Humboldt Current Ecosystem is used as an illustrative case study. The model is calibrated using an evolutionary algorithm and a likelihood approach to fit time series data of landings, abundance indices and catch at length distributions from 1992 to 2008. Testing different calibration schemes regarding the number of phases, the precedence of the parameters' estimation, and the consideration of time varying parameters, the results show that the multiple-phase calibration conducted under our criteria allowed to improve the model fit.
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.
Sample correlations of infinite variance time series models: an empirical and theoretical study
Directory of Open Access Journals (Sweden)
Jason Cohen
1998-01-01
Full Text Available When the elements of a stationary ergodic time series have finite variance the sample correlation function converges (with probability 1 to the theoretical correlation function. What happens in the case where the variance is infinite? In certain cases, the sample correlation function converges in probability to a constant, but not always. If within a class of heavy tailed time series the sample correlation functions do not converge to a constant, then more care must be taken in making inferences and in model selection on the basis of sample autocorrelations. We experimented with simulating various heavy tailed stationary sequences in an attempt to understand what causes the sample correlation function to converge or not to converge to a constant. In two new cases, namely the sum of two independent moving averages and a random permutation scheme, we are able to provide theoretical explanations for a random limit of the sample autocorrelation function as the sample grows.
Modeling and Simulation of Time Series Prediction Based on Dynic Neural Network
Institute of Scientific and Technical Information of China (English)
王雪松; 程玉虎; 彭光正
2004-01-01
Molding and simulation of time series prediction based on dynic neural network(NN) are studied. Prediction model for non-linear and time-varying system is proposed based on dynic Jordan NN. Aiming at the intrinsic defects of back-propagation (BP) algorithm that cannot update network weights incrementally, a hybrid algorithm combining the temporal difference (TD) method with BP algorithm to train Jordan NN is put forward. The proposed method is applied to predict the ash content of clean coal in jigging production real-time and multi-step. A practical exple is also given and its application results indicate that the method has better performance than others and also offers a beneficial reference to the prediction of nonlinear time series.
A model-free characterization of recurrences in stationary time series
Chicheportiche, Rémy; Chakraborti, Anirban
2017-05-01
Study of recurrences in earthquakes, climate, financial time-series, etc. is crucial to better forecast disasters and limit their consequences. Most of the previous phenomenological studies of recurrences have involved only a long-ranged autocorrelation function, and ignored the multi-scaling properties induced by potential higher order dependencies. We argue that copulas is a natural model-free framework to study non-linear dependencies in time series and related concepts like recurrences. Consequently, we arrive at the facts that (i) non-linear dependences do impact both the statistics and dynamics of recurrence times, and (ii) the scaling arguments for the unconditional distribution may not be applicable. Hence, fitting and/or simulating the intertemporal distribution of recurrence intervals is very much system specific, and cannot actually benefit from universal features, in contrast to the previous claims. This has important implications in epilepsy prognosis and financial risk management applications.
Directory of Open Access Journals (Sweden)
Magdy A. El-Tawil
2012-07-01
Full Text Available The homotopy analysis method (HAM is used to find approximate analytical solutions of continuous population models for single and interacting species. The homotopy analysis method contains the auxiliary parameter $hbar,$ which provides us with a simple way to adjust and control the convergence region of series solution. the solutions are compared with the numerical results obtained using NDSolve, an ordinary differential equation solver found in the Mathematica package and a good agreement is found. Also the solutions are compared with the available analytic results obtained by other methods and more accurate and convergent series solution found. The convergence region is also computed which shows the validity of the HAM solution. This method is reliable and manageable.
Chattopadhyay, Goutami; 10.1140/epjp/i2012-12043-9
2012-01-01
This study reports a statistical analysis of monthly sunspot number time series and observes non homogeneity and asymmetry within it. Using Mann-Kendall test a linear trend is revealed. After identifying stationarity within the time series we generate autoregressive AR(p) and autoregressive moving average (ARMA(p,q)). Based on minimization of AIC we find 3 and 1 as the best values of p and q respectively. In the next phase, autoregressive neural network (AR-NN(3)) is generated by training a generalized feedforward neural network (GFNN). Assessing the model performances by means of Willmott's index of second order and coefficient of determination, the performance of AR-NN(3) is identified to be better than AR(3) and ARMA(3,1).
Technical integration of hippocampus, basal ganglia and physical models for spatial navigation
Directory of Open Access Journals (Sweden)
Charles W Fox
2009-03-01
Full Text Available Computational neuroscience is increasingly moving beyond modeling individual neurons or neural systems to consider the integration of multiple models, often constructed by different research groups. We report on our preliminary technical integration of recent hippocampal formation, basal ganglia and physical environment models, together with visualisation tools, as a case study in the use of Python across the modelling tool-chain. We do not present new modeling results here. The architecture incorporates leaky-integrator and rate-coded neurons, a 3D environment with collision detection and tactile sensors, 3D graphics and 2D plots. We found Python to be a flexible platform, offering a significant reduction in development time, without a corresponding significant increase in execution time. We illustrate this by implementing a part of the model in various alternative languages and coding styles, and comparing their execution times. For very large scale system integration, communication with other languages and parallel execution may be required, which we demonstrate using the BRAHMS framework's Python bindings.
DEFF Research Database (Denmark)
Kastberg, Peter; Kampf, Constance
In order to support an explicit understanding of cultural patterns as both dynamic and structured, we will examine Hofstede?s model for stabilization of cultural patterns, and use this model to explore some cultural consequences for patterns of logic and signs that influence the effectiveness...... of technical communication across cultures. In order to demonstrate the model, we will apply it to examples from different cultures, which show different patterns of logic, terminology and conventions. In light of these examples, we propose that cross-cultural technical communication studies can be situated...
The fictionality of topic modeling: Machine reading Anthony Trollope's Barsetshire series
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Rachel Sagner Buurma
2015-12-01
Full Text Available This essay describes how using unsupervised topic modeling (specifically the latent Dirichlet allocation topic modeling algorithm in MALLET on relatively small corpuses can help scholars of literature circumvent the limitations of some existing theories of the novel. Using an example drawn from work on Victorian novelist Anthony Trollope's Barsetshire series, it argues that unsupervised topic modeling's counter-factual and retrospective reconstruction of the topics out of which a given set of novels have been created allows for a denaturalizing and unfamiliar (though crucially not “objective” or “unbiased” view. In other words, topic models are fictions, and scholars of literature should consider reading them as such. Drawing on one aspect of Stephen Ramsay's idea of algorithmic criticism, the essay emphasizes the continuities between “big data” methods and techniques and longer-standing methods of literary study.
Endo, Vitor Takashi; de Carvalho Pereira, José Carlos
2017-05-01
Material properties description and understanding are essential aspects when computational solid mechanics is applied to product development. In order to promote injected fiber reinforced thermoplastic materials for structural applications, it is very relevant to develop material characterization procedures, considering mechanical properties variation in terms of fiber orientation and loading time. Therefore, a methodology considering sample manufacturing, mechanical tests and data treatment is described in this study. The mathematical representation of the material properties was solved by a linear viscoelastic constitutive model described by Prony series, which was properly adapted to orthotropic materials. Due to the large number of proposed constitutive model coefficients, a parameter identification method was employed to define mathematical functions. This procedure promoted good correlation among experimental tests, and analytical and numerical creep models. Such results encourage the use of numerical simulations for the development of structural components with the proposed linear viscoelastic orthotropic constitutive model. A case study was presented to illustrate an industrial application of proposed methodology.
Analysis of hohlraum energetics of the SG series and the NIF experiments with energy balance model
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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.