Time Series Data Analysis with State Space Model

Kentaro Matsuura
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Kentaro Matsuura: HOXO-M Inc.

Chapter Chapter 11 in Bayesian Statistical Modeling with Stan, R, and Python, 2022, pp 237-284 from Springer

Abstract: Abstract A challenge in time series data analysis is that it is an extrapolation problem: we are interested in predicting events that are only observable in the future. For an extrapolation problem, capturing mechanisms using a model which gives persuasive interpretations, usually yields a better prediction performance than using a black box method. In this chapter, we will use state space models for time series data. State space models are known for its high interpretability, and because it can be extended easily, they have a wide range of applications.

Date: 2022
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DOI: 10.1007/978-981-19-4755-1_11

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