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

1. Front Matter

   Pages i-xiii

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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

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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.  

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

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

  Junichiro Hagiwara

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.

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