Skip to main content
SpringerLink
Log in

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

  • Book
  • © 2021

Overview

Authors:
  1. Junichiro Hagiwara

    1. Graduate School / Faculty of Information Science and Technology, Hokkaido University, Hokkaido, Japan

    View author publications

    You can also search for this author in PubMed   Google Scholar

  • 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

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (12 chapters)

  1. Front Matter

    Pages i-xiii
    Download chapter PDF

  2. Introduction

    • Junichiro Hagiwara
    Pages 1-6

  3. Fundamentals of Probability and Statistics

    • Junichiro Hagiwara
    Pages 7-21

  4. Fundamentals of Handling Time Series Data with R

    • Junichiro Hagiwara
    Pages 23-27

  5. Quick Tour of Time Series Analysis

    • Junichiro Hagiwara
    Pages 29-58

  6. State-Space Model

    • Junichiro Hagiwara
    Pages 59-68

  7. State Estimation in the State-Space Model

    • Junichiro Hagiwara
    Pages 69-87

  8. Batch Solution for Linear Gaussian State-Space Model

    • Junichiro Hagiwara
    Pages 89-95

  9. Sequential Solution for Linear Gaussian State-Space Model

    • Junichiro Hagiwara
    Pages 97-127

  10. Introduction and Analysis Examples of a Well-Known Component Model in the Linear Gaussian State-Space Model

    • Junichiro Hagiwara
    Pages 129-177

  11. Batch Solution for General State-Space Model

    • Junichiro Hagiwara
    Pages 179-218

  12. Sequential Solution for General State-Space Model

    • Junichiro Hagiwara
    Pages 219-275

  13. Example of Applied Analysis in General State-Space Model

    • Junichiro Hagiwara
    Pages 277-301

  14. Back Matter

    Pages 303-347
    Download chapter PDF
Back to top

Keywords

About this book

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.  



Authors and Affiliations

  • Graduate School / Faculty of Information Science and Technology, Hokkaido University, Hokkaido, Japan

    Junichiro Hagiwara

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.

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation