Junichiro Hagiwara
Time Series Analysis for the State-Space Model with R/Stan
Time Series Analysis for the State-Space Model with R/Stan
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Excellent- More about Time Series Analysis for the State-Space Model with R/Stan
This book offers a comprehensive and concrete illustration of time series analysis, focusing on the state-space model, with a consistent Bayesian treatment and practical exercise using R/Stan. It covers introductory and exploratory methods to advanced topics and real-time structural change detection.
Format: Hardback
Length: 347 pages
Publication date: 31 August 2021
Publisher: Springer Verlag, Singapore
This comprehensive book delves into the realm of time series analysis, specifically focusing on the state-space model, which has garnered increasing attention across various fields. Its standout feature lies in its unwavering commitment to a Bayesian approach, encompassing both batch and sequential solutions for linear Gaussian and general state-space models. Through the use of MCMC and Kalman/particle filters, the reader gains valuable insights into flexible modeling techniques in modern time series analysis.
The book's main topics span a wide range, encompassing introductory and exploratory methods to the most advanced topics, such as real-time structural change detection. Furthermore, a practical exercise utilizing R/Stan, based on real data, fosters understanding and enhances the readers' analytical skills.
By presenting a thorough and practical exploration of the state-space model, this book serves as an invaluable resource for researchers, practitioners, and students seeking to deepen their understanding of time series analysis and its applications in diverse fields.
Weight: 711g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811607103
Edition number: 1st ed. 2021
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