Time Series Analysis for the State-Space Model with R/Stan

Hagiwara, Junichiro

Springer Verlag, Singapore（2022/09発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 347 p.
• 言語 ENG
• 商品コード 9789811607134
• DDC分類 519.55

Full Description

This book provides a comprehensive and concrete illustration of time series analysis focusing on the state-space model, which has recently attracted increasing attention in a broad range of fields. The major feature of the book lies in its consistent Bayesian treatment regarding whole combinations of batch and sequential solutions for linear Gaussian and general state-space models: MCMC and Kalman/particle filter. The reader is given insight on flexible modeling in modern time series analysis. The main topics of the book deal with the state-space model, covering extensively, from introductory and exploratory methods to the latest advanced topics such as real-time structural change detection. Additionally, a practical exercise using R/Stan based on real data promotes understanding and enhances the reader's analytical capability.  

Contents

Introduction.- Fundamental of probability and statistics.- Fundamentals of handling time series data with R.- Quick tour of time series analysis.- State-space model.- State estimation in the state-space model.- Batch solution for linear Gaussian state-space model.- Sequential solution for linear Gaussian state-space model.- Introduction and analysis examples of a well-known component model.- Batch solution for general state-space model.- Sequential solution for general state-space model.- Example of applied analysis in general state-space model.