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

Junichiro Hagiwara (Autor)

Buch | Hardcover

347 Seiten

2021 | 1st ed. 2021
Springer Verlag, Singapore
978-981-16-0710-3 (ISBN)

Artikel merken

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.

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.

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.

Erscheinungsdatum	03.09.2021
Zusatzinfo	216 Illustrations, black and white; XIII, 347 p. 216 illus.
Verlagsort	Singapore
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Mathematik / Informatik ► Mathematik ► Computerprogramme / Computeralgebra
	Mathematik / Informatik ► Mathematik ► Statistik
	Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
Schlagworte	Baysian Inference • Kalman Filter • MCMC • Particle filter • State-space model • Time Series Analysis
ISBN-10	981-16-0710-9 / 9811607109
ISBN-13	978-981-16-0710-3 / 9789811607103
Zustand	Neuware