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Author: Raman K. Mehra Publisher: ISBN: Category : Languages : en Pages : 141
Book Description
Research is reported on adaptive time series methods for detecting and tracking both abrupt and slow changes in both structure and parameters of dynamic systems. The methods are based on a unified statistical framework which is motivated by statistical inferences and entropy arguments. The method yields estimates of multivariate input/output dynamics and noise statistics. It also gives estimate of system order that is optimal in the sense of an information theoretic criterion. The integrated approach is known as CVA-AIC. Many theoretical issues have been explored under the scope of this project. The relationship between this technique and another powerful framework for estimation known as E-M algorithmic approach has been established. It the CVA-AIC technique is embedded properly in an E-M framework, it leads to maximum likelihood estimates and recursive algorithms for system identification. (jhd).
Author: Raman K. Mehra Publisher: ISBN: Category : Languages : en Pages : 141
Book Description
Research is reported on adaptive time series methods for detecting and tracking both abrupt and slow changes in both structure and parameters of dynamic systems. The methods are based on a unified statistical framework which is motivated by statistical inferences and entropy arguments. The method yields estimates of multivariate input/output dynamics and noise statistics. It also gives estimate of system order that is optimal in the sense of an information theoretic criterion. The integrated approach is known as CVA-AIC. Many theoretical issues have been explored under the scope of this project. The relationship between this technique and another powerful framework for estimation known as E-M algorithmic approach has been established. It the CVA-AIC technique is embedded properly in an E-M framework, it leads to maximum likelihood estimates and recursive algorithms for system identification. (jhd).
Author: Wallace E. Larimore Publisher: ISBN: Category : Languages : en Pages : 75
Book Description
In this Phase I study funded under the Small Business Innovation Research (SBIR) program, statistical methods are developed using the predictive inference and entropy approach. Previous recent research has derived entropy as the natural measure of model approximation error from the fundamental statistical principles of sufficiency and repeated sampling. In this study, the areas of nonnested multiple comparison, multivariable time series analysis, adaptive time series analysis of changing processes, and optimal small sample inference are investigated. Constrained maximum likelihood methods are developed for general nonnested multiple comparison. For the asymptotic optimality of these methods, a condition on the Fisher information and Hessian matrices must be satisfied. Applying these results to multivariate time series analysis, lower bounds are derived for the achievable accuracy of the estimated transfer function and spectral matrices. Markov and canonical variate analysis (CVA) provide a means of numerically and statistically stable model fitting of multivariable time series, and these methods provide a basis for modeling fitting time varying models of changing processes.
Author: Harald Atmanspacher Publisher: Springer Science & Business Media ISBN: 1489923055 Category : Technology & Engineering Languages : en Pages : 362
Book Description
Proceedings of a NATO ASI held in Irsee/Kaufbeuren, Germany, June 15--26, 1990
Author: Andreas S. Weigend Publisher: Routledge ISBN: 042997227X Category : Social Science Languages : en Pages : 665
Book Description
The book is a summary of a time series forecasting competition that was held a number of years ago. It aims to provide a snapshot of the range of new techniques that are used to study time series, both as a reference for experts and as a guide for novices.
Author: Olga Valenzuela Publisher: Springer Nature ISBN: 3030260364 Category : Business & Economics Languages : en Pages : 380
Book Description
This book presents selected peer-reviewed contributions from the International Conference on Time Series and Forecasting, ITISE 2018, held in Granada, Spain, on September 19-21, 2018. The first three parts of the book focus on the theory of time series analysis and forecasting, and discuss statistical methods, modern computational intelligence methodologies, econometric models, financial forecasting, and risk analysis. In turn, the last three parts are dedicated to applied topics and include papers on time series analysis in the earth sciences, energy time series forecasting, and time series analysis and prediction in other real-world problems. The book offers readers valuable insights into the different aspects of time series analysis and forecasting, allowing them to benefit both from its sophisticated and powerful theory, and from its practical applications, which address real-world problems in a range of disciplines. The ITISE conference series provides a valuable forum for scientists, engineers, educators and students to discuss the latest advances and implementations in the field of time series analysis and forecasting. It focuses on interdisciplinary and multidisciplinary research encompassing computer science, mathematics, statistics and econometrics.
Author: Raman K. Mehra
Author: Raman K. Mehra
Author: Wallace E. Larimore
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Author: Harald Atmanspacher
Author: Andreas S. Weigend
Author: Olga Valenzuela
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