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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.
List of 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.
About the author
Junichiro Hagiwara received the B.E., M.E., and Ph.D. degrees from Hokkaido University, Sapporo, Japan, in 1990, 1992, and 2016, respectively. He joined the Nippon Telegraph and Telephone Corporation in April 1992 and transferred to NTT Mobile Communications Network, Inc. (currently NTT DOCOMO, INC.) in July 1992. Later, he became involved in the research and development of mobile communication systems. His current research interests are in the application of stochastic theory to the communication domain. He is currently a visiting professor at Hokkaido University.
