The Experts below are selected from a list of 120 Experts worldwide ranked by ideXlab platform
Anders Rygh Swensen - One of the best experts on this subject based on the ideXlab platform.
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forecasting key macroeconomic variables from a large number of predictors a state space approach
Journal of Forecasting, 2010Co-Authors: Arvid Raknerud, Terje Skjerpen, Anders Rygh SwensenAbstract:We use state space Methods to estimate a large dynamic factor model for the Norwegian economy involving 93 variables for 1978Q2–2005Q4. The model is used to obtain forecasts for 22 key variables that can be derived from the original variables by aggregation. To investigate the potential gain in using such a large information set, we compare the forecasting properties of the dynamic factor model with those of univariate benchmark models. We find that there is an overall gain in using the dynamic factor model, but that the gain is notable only for a few of the key variables. Copyright © 2009 John Wiley & Sons, Ltd.
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forecasting key macroeconomic variables from a large number of predictors a state space approach
2007Co-Authors: Arvid Raknerud, Terje Skjerpen, Anders Rygh SwensenAbstract:We use state space Methods to estimate a large dynamic factor model for the Norwegian economy involving 93 variables for 1978Q2-2005Q4. The model is used to obtain forecasts for 22 key variables that can be derived from the original variables by aggregation. To investigate the potential gain in using such a large information set, we compare the forecasting properties of the dynamic factor model with those of univariate benchmark models. We find that there is an overall gain in using the dynamic factor model, but that the gain is notable only for a few of the key variables.
Antonio Puliafito - One of the best experts on this subject based on the ideXlab platform.
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performance and reliability analysis of computer systems an example based approach using the sharpe software package
Performance and Reliability Analysis of Computer Systems: An Example-Based Approach Using the SHARPE Software Package, 2012Co-Authors: Robin A Sahner, Kishor S Trivedi, Antonio PuliafitoAbstract:Performance and Reliability Analysis of Computer Systems: An Example-Based Approach Using the SHARPE Software Package provides a variety of probabilistic, discrete-state models used to assess the reliability and performance of computer and communication systems. The models included are combinatorial reliability models (reliability block diagrams, fault trees and reliability graphs), directed, acyclic task precedence graphs, Markov and semi-Markov models (including Markov reward models), product-form queueing networks and generalized stochastic Petri nets. A practical approach to system modeling is followed; all of the examples described are solved and analyzed using the SHARPE tool. In structuring the book, the authors have been careful to provide the reader with a methodological approach to analytical modeling techniques. These techniques are not seen as alternatives but rather as an integral part of a single process of assessment which, by hierarchically combining results from different kinds of models, makes it possible to use State-Space Methods for those parts of a system that require them and non-State-Space Methods for the more well-behaved parts of the system. The SHARPE (Symbolic Hierarchical Automated Reliability and Performance Evaluator) package is the `toolchest' that allows the authors to specify stochastic models easily and solve them quickly, adopting model hierarchies and very efficient solution techniques. All the models described in the book are specified and solved using the SHARPE language; its syntax is described and the source code of almost all the examples discussed is provided. Audience: Suitable for use in advanced level courses covering reliability and performance of computer and communications systems and by researchers and practicing engineers whose work involves modeling of system performance and reliability.
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performance and reliability analysis of computer systems an example based approach using the sharpe software
IEEE Transactions on Reliability, 1997Co-Authors: Robin A Sahner, Kishor S Trivedi, Antonio PuliafitoAbstract:Performance and Reliability Analysis of Computer Systems: An Example-Based Approach Using the SHARPE Software Package provides a variety of probabilistic, discrete-state models used to assess the reliability and performance of computer and communication systems. The models included are combinatorial reliability models (reliability block diagrams, fault trees and reliability graphs), directed, acyclic task precedence graphs, Markov and semi-Markov models (including Markov reward models), product-form queueing networks and generalized stochastic Petri nets. A practical approach to system modeling is followed; all of the examples described are solved and analyzed using the SHARPE tool. In structuring the book, the authors have been careful to provide the reader with a methodological approach to analytical modeling techniques. These techniques are not seen as alternatives but rather as an integral part of a single process of assessment which, by hierarchically combining results from different kinds of models, makes it possible to use State-Space Methods for those parts of a system that require them and non-State-Space Methods for the more well-behaved parts of the system. The SHARPE (Symbolic Hierarchical Automated Reliability and Performance Evaluator) package is the `toolchest' that allows the authors to specify stochastic models easily and solve them quickly, adopting model hierarchies and very efficient solution techniques. All the models described in the book are specified and solved using the SHARPE language; its syntax is described and the source code of almost all the examples discussed is provided. Audience: Suitable for use in advanced level courses covering reliability and performance of computer and communications systems and by researchers and practicing engineers whose work involves modeling of system performance and reliability.
Siem Jan Koopman - One of the best experts on this subject based on the ideXlab platform.
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structural time series analyser modeller and predictor stamp 8 2
2009Co-Authors: Siem Jan Koopman, Andrew Harvey, Jurgen A Doornik, Neil ShephardAbstract:STAMP™ stands for Structural Time series Analyser, Modeller and Predictor. It is a menu-driven system designed to model, describe and predict time series. It is based on structural time series models. These models are set up in terms of components such as trends, seasonals and cycles, which have a direct interpretation. Estimation is carried out using state space Methods and Kalman filtering. STAMP 8.2 for OxMetrics 6 handles time series with missing values. Explanatory variables with time varying coefficients and interventions can be included. Version 8 includes extensions and improvements for Multivariate Models: select components by equation, select regressors and interventions by equation, separate dependence structures for each component, wide choice of variance matrices, higher order multivariate components, missing observations allowed, forecasting, exact likelihood computation, automatic outlier and break detection, fixing parameters is made easy. Among the special features of STAMP are interactive model selection, a wide range of diagnostics, easy creation of model based forecasts, spectral filters, observation weight functions, and batch facilities. The STAMP book introduces structural time series models and the way in which they can be used to model a wide range of series.
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an introduction to state space time series analysis
2007Co-Authors: Jacques J F Commandeur, Siem Jan KoopmanAbstract:Providing a practical introduction to state space Methods as applied to unobserved components time series models, also known as structural time series models, this book introduces time series analysis using state space methodology to readers who are neither familiar with time series analysis, nor with state space Methods. The only background required in order to understand the material presented in the book is a basic knowledge of classical linear regression models, of which a brief review is provided to refresh the reader's knowledge. Also, a few sections assume familiarity with matrix algebra, however, these sections may be skipped without losing the flow of the exposition. The book offers a step by step approach to the analysis of the salient features in time series such as the trend, seasonal, and irregular components. Practical problems such as forecasting and missing values are treated in some detail. This useful book will appeal to practitioners and researchers who use time series on a daily basis in areas such as the social sciences, quantitative history, biology and medicine. It also serves as an accompanying textbook for a basic time series course in econometrics and statistics, typically at an advanced undergraduate level or graduate level.
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time series analysis by state space Methods
OUP Catalogue, 2001Co-Authors: Jane Durbin, Siem Jan KoopmanAbstract:This excellent text provides a comprehensive treatment of the state space approach to time series analysis. The distinguishing feature of state space time series models is that observations are regarded as made up of distinct components such as trend, seasonal, regression elements and disturbence terms, each of which is modelled separately. The techniques that emerge from this approach are very flexible and are capable of handling a much wider range of problems than the main analytical system currently in use for time series analysis, the Box-Jenkins ARIMA system. The book provides an excellent source for the development of practical courses on time series analysis.
Jacques J F Commandeur - One of the best experts on this subject based on the ideXlab platform.
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on statistical inference in time series analysis of the evolution of road safety
Accident Analysis & Prevention, 2013Co-Authors: Jacques J F Commandeur, Frits Bijleveld, Ruth Bergelhayat, Constantinos Antoniou, George Yannis, Eleonora PapadimitriouAbstract:Data collected for building a road safety observatory usually include observations made sequentially through time. Examples of such data, called time series data, include annual (or monthly) number of road traffic accidents, traffic fatalities or vehicle kilometers driven in a country, as well as the corresponding values of safety performance indicators (e.g., data on speeding, seat belt use, alcohol use, etc.). Some commonly used statistical techniques imply assumptions that are often violated by the special properties of time series data, namely serial dependency among disturbances associated with the observations. The first objective of this paper is to demonstrate the impact of such violations to the applicability of standard Methods of statistical inference, which leads to an under or overestimation of the standard error and consequently may produce erroneous inferences. Moreover, having established the adverse consequences of ignoring serial dependency issues, the paper aims to describe rigorous statistical techniques used to overcome them. In particular, appropriate time series analysis techniques of varying complexity are employed to describe the development over time, relating the accident-occurrences to explanatory factors such as exposure measures or safety performance indicators, and forecasting the development into the near future. Traditional regression models (whether they are linear, generalized linear or nonlinear) are shown not to naturally capture the inherent dependencies in time series data. Dedicated time series analysis techniques, such as the ARMA-type and DRAG approaches are discussed next, followed by structural time series models, which are a subclass of state space Methods. The paper concludes with general recommendations and practice guidelines for the use of time series models in road safety research.
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an introduction to state space time series analysis
2007Co-Authors: Jacques J F Commandeur, Siem Jan KoopmanAbstract:Providing a practical introduction to state space Methods as applied to unobserved components time series models, also known as structural time series models, this book introduces time series analysis using state space methodology to readers who are neither familiar with time series analysis, nor with state space Methods. The only background required in order to understand the material presented in the book is a basic knowledge of classical linear regression models, of which a brief review is provided to refresh the reader's knowledge. Also, a few sections assume familiarity with matrix algebra, however, these sections may be skipped without losing the flow of the exposition. The book offers a step by step approach to the analysis of the salient features in time series such as the trend, seasonal, and irregular components. Practical problems such as forecasting and missing values are treated in some detail. This useful book will appeal to practitioners and researchers who use time series on a daily basis in areas such as the social sciences, quantitative history, biology and medicine. It also serves as an accompanying textbook for a basic time series course in econometrics and statistics, typically at an advanced undergraduate level or graduate level.
Marc Olivi Coppens - One of the best experts on this subject based on the ideXlab platform.
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time series analysis of pressure fluctuations in gas solid fluidized beds a review
International Journal of Multiphase Flow, 2011Co-Authors: Ruud J Van Ommen, Stefan Gheorghiu, John Van Der Schaaf, Filip Johnsson, Srdjan Sasic, Marc Olivi CoppensAbstract:This work reviews Methods for time-series analysis for characterization of the dynamics of gas-solid fluidized beds from in-bed pressure measurements for different fluidization regimes. The paper covers analysis in time domain, frequency domain, and in state space. It is a follow-up and an update of a similar review paper written a decade ago. We use the same pressure time-series as used by Johnsson et al. (2000). The paper updates the previous review and includes additional Methods for time-series analysis, which have been proposed to investigate dynamics of gas-solid fluidized beds. Results and underlying assumptions of the Methods are discussed. Analysis in the time domain is often the simplest approach. The standard deviation of pressure fluctuations is widely used to identify regimes in fluidized beds, but its disadvantage is that it is an indirect measure of the dynamics of the flow. The so-called average cycle time provides information about the relevant time scales of the system, making it an easy-to-calculate alternative to frequency analysis. Autoregressive Methods can be used to show an analogy between a fluidized bed and a single or a set of simple mechanical systems acting in parallel. The most common frequency domain method is the power spectrum. We show that - as an alternative to the often used non-parametric Methods to estimate the power spectrum - parametric Methods can be useful. To capture transient effects on a longer time scale (>1 s), either the transient power spectral density or wavelet analysis can be applied. For the state space analysis, the information given by the Kolmogorov entropy is equivalent to that of the average frequency, obtained in the frequency domain. However, an advantage of certain state space Methods, such as attractor comparison, is that they are more sensitive to small changes than frequency domain Methods; this feature can be used for, e.g., on-line monitoring. In general, we conclude that, over the past decade, progress has been made in understanding fluidized-bed dynamics by extracting the relevant information from pressure fluctuation data, but the picture is still incomplete.
