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Time series analysis for the state-space model with R/Stan

By:

Hagiwara, Junichiro

Material type: Text

TextPublication details: Springer Switzerland 2021Description: xiii, 247 pISBN:

9789811607134

Subject(s):

Time Series Analysis

DDC classification:

519.550243 HAG

Summary: 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.

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Operations Management & Quantitative Techniques	519.550243 HAG (Browse shelf(Opens below))	1	Available		005557

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Previous	519.542 LON Statistics for making decisions	519.542 WAT Mathematical theory of bayesian statistics	519.544 FRO Hypothesis testing: an intuitive guide for making data driven decisions	519.550243 HAG Time series analysis for the state-space model with R/Stan	519.550243 MCD Interrupted time series analysis	519.6 ANT Practical optimization: algorithms and engineering applications	519.6 FOX Nonlinear optimization:	Next

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.

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