With a focus on analyzing and modeling linear dynamic systems using statistical methods, Time Series Analysis formulates various linear models, discusses their theoretical characteristics, and explores the connections among stochastic dynamic models. Emphasizing the time domain description, the author presents theorems to highlight the most

Emphasizes basic methods for modeling linear dynamic systems. This book presents an understanding of basic concepts, such as multivariate random variables, stochastic processes, and regression-based methods. It covers topics that include spectral analysis, state space models, and recursive estimation.

Preface. Introduction. Multivariate Random Variables. Regression-Based Methods. Linear Dynamic Systems. Stochastic Processes. Identification, Estimation, and Model Checking. Spectral Analysis. Linear Systems and Stochastic Processes. Multivariate Time Series. State Space Models of Dynamic Systems. Recursive Estimation. Real Life Inspired Problems. Appendices. Bibliography. Index.