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Time series models

Deistler, M.

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Time series models

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Time series models/ by Manfred Deistler, Wolfgang Scherrer.
作者:	Deistler, M.
其他作者:	Scherrer, Wolfgang.
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	xiv, 201 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Time-series analysis. -
電子資源:	https://doi.org/10.1007/978-3-031-13213-1
ISBN:	9783031132131

Time series models
Deistler, M.

Time series models[electronic resource] /by Manfred Deistler, Wolfgang Scherrer. - Cham :Springer International Publishing :2022. - xiv, 201 p. :ill. (some col.), digital ;24 cm. - Lecture notes in statistics,v. 2242197-7186 ;. - Lecture notes in statistics ;v. 224..

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.

ISBN: 9783031132131

Standard No.: 10.1007/978-3-031-13213-1doiSubjects--Topical Terms:

532530
Time-series analysis.

LC Class. No.: QA280 / .D45 2022

Dewey Class. No.: 519.55

Time series models
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520 $a 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.
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