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Multivariate time series with linear...

Gomez, Victor.

Multivariate time series with linear state space structure

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Multivariate time series with linear state space structureby Victor Gomez.
作者:	Gomez, Victor.
出版者:	Cham :Springer International Publishing :2016.
面頁冊數:	xvii, 541 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Variate difference method.
電子資源:	http://dx.doi.org/10.1007/978-3-319-28599-3
ISBN:	9783319285993$q(electronic bk.)

Multivariate time series with linear state space structure
Gomez, Victor.

Multivariate time series with linear state space structure[electronic resource] /by Victor Gomez. - Cham :Springer International Publishing :2016. - xvii, 541 p. :ill., digital ;24 cm.

Preface -- Computer Software -- Orthogonal Projection -- Linear Models -- Stationarity and Linear Time Series Models -- The State Space Model -- Time Invariant State Space Models -- Time Invariant State Space Models With Inputs -- Wiener-Kolmogorov Filtering and Smoothing -- SSMMATLAB -- Bibliography -- Author Index -- Subject Index.

This book presents a comprehensive study of multivariate time series with linear state space structure. The emphasis is put on both the clarity of the theoretical concepts and on efficient algorithms for implementing the theory. In particular, it investigates the relationship between VARMA and state space models, including canonical forms. It also highlights the relationship between Wiener-Kolmogorov and Kalman filtering both with an infinite and a finite sample. The strength of the book also lies in the numerous algorithms included for state space models that take advantage of the recursive nature of the models. Many of these algorithms can be made robust, fast, reliable and efficient. The book is accompanied by a MATLAB package called SSMMATLAB and a webpage presenting implemented algorithms with many examples and case studies. Though it lays a solid theoretical foundation, the book also focuses on practical application, and includes exercises in each chapter. It is intended for researchers and students working with linear state space models, and who are familiar with linear algebra and possess some knowledge of statistics.

ISBN: 9783319285993$q(electronic bk.)

Standard No.: 10.1007/978-3-319-28599-3doiSubjects--Topical Terms:

747903
Variate difference method.

LC Class. No.: HA30.3

Dewey Class. No.: 519.55

Multivariate time series with linear state space structure
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520 $a This book presents a comprehensive study of multivariate time series with linear state space structure. The emphasis is put on both the clarity of the theoretical concepts and on efficient algorithms for implementing the theory. In particular, it investigates the relationship between VARMA and state space models, including canonical forms. It also highlights the relationship between Wiener-Kolmogorov and Kalman filtering both with an infinite and a finite sample. The strength of the book also lies in the numerous algorithms included for state space models that take advantage of the recursive nature of the models. Many of these algorithms can be made robust, fast, reliable and efficient. The book is accompanied by a MATLAB package called SSMMATLAB and a webpage presenting implemented algorithms with many examples and case studies. Though it lays a solid theoretical foundation, the book also focuses on practical application, and includes exercises in each chapter. It is intended for researchers and students working with linear state space models, and who are familiar with linear algebra and possess some knowledge of statistics.
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