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＜電子ブック＞
Time Series Models / by Manfred Deistler, Wolfgang Scherrer
(Lecture Notes in Statistics. ISSN:21977186 ; 224)

版	1st ed. 2022.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2022
本文言語	英語
大きさ	XIV, 201 p. 13 illus., 10 illus. in color : online resource
著者標目	*Deistler, Manfred author Scherrer, Wolfgang author SpringerLink (Online service)
件　名	LCSH:Time-series analysis LCSH:Stochastic processes LCSH:Econometrics LCSH:Statistics  LCSH:Signal processing FREE:Time Series Analysis FREE:Stochastic Processes FREE:Econometrics FREE:Statistical Theory and Methods FREE:Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences FREE:Signal, Speech and Image Processing
一般注記	Preface -- 1 Time Series and Stationary Processes -- 2 Prediction -- 3 Spectral Representation -- 4 Filter -- 5 Autoregressive Processes -- 6 ARMA Systems and ARMA Processes -- 7 State-Space Systems -- 8 Models with Exogenous Variables -- 9 Granger Causality -- 10 Dynamic Factor Models -- 10 ARCH and GARCH Models -- Index This textbook provides a self-contained presentation of the theory and models of time series analysis. Putting an emphasis on weakly stationary processes and linear dynamic models, it describes the basic concepts, ideas, methods and results in a mathematically well-founded form and includes numerous examples and exercises. The first part presents the theory of weakly stationary processes in time and frequency domain, including prediction and filtering. The second part deals with multivariate AR, ARMA and state space models, which are the most important model classes for stationary processes, and addresses the structure of AR, ARMA and state space systems, Yule-Walker equations, factorization of rational spectral densities and Kalman filtering. Finally, there is a discussion of Granger causality, linear dynamic factor models and (G)ARCH models. The book provides a solid basis for advanced mathematics students and researchers in fields such as data-driven modeling, forecasting and filtering, which are important in statistics, control engineering, financial mathematics, econometrics and signal processing, among other subjects HTTP:URL=https://doi.org/10.1007/978-3-031-13213-1

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