Time Series Models (Paperback, 1st ed. 2022)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Lecture Notes in Statistics, 224
Release date:	October 2022
First published:	2022
Authors:	Manfred Deistler • Wolfgang Scherrer
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	201
Edition:	1st ed. 2022
ISBN-13:	978-3-03-113212-4
Languages:	English
Subtitles:	German
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Business & Economics > Economics > Econometrics > General Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Applied optics > General Promotions
LSN:	3-03-113212-2
Barcode:	9783031132124

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