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

Beschreibung

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.  
Provides a comprehensive and concrete illustration for the state-space model
Covers whole solutions through a consistent Bayesian approach: the batch method by MCMC using Stan and sequential ones by Kalman/particle filter using R
Presents advanced topics such as real-time structural change detection with the horseshoe prior

Provides a comprehensive and concrete illustration for the state-space model Covers whole solutions through a consistent Bayesian approach: the batch method by MCMC using Stan and sequential ones by Kalman/particle filter using R Presents advanced topics such as real-time structural change detection with the horseshoe prior

Autor*in

Junichiro Hagiwara

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Time Series Analysis State-Space Model Kalman Filter MCMC Particle Filter Baysian Inference

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