Gaussian Process Kernels for Popular State-Space Time Series Models

In this paper we investigate a link between state- space models and Gaussian Processes (GP) for time series modeling and forecasting. In particular, several widely used state- space models are transformed into continuous time form and corresponding Gaussian Process kernels are derived. Experimen- ta...

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Bibliographic Details
Main Authors Grigorievskiy, Alexander, Karhunen, Juha
Format Journal Article
LanguageEnglish
Published 25.10.2016
Subjects
Statistics - Machine Learning
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Summary:In this paper we investigate a link between state- space models and Gaussian Processes (GP) for time series modeling and forecasting. In particular, several widely used state- space models are transformed into continuous time form and corresponding Gaussian Process kernels are derived. Experimen- tal results demonstrate that the derived GP kernels are correct and appropriate for Gaussian Process Regression. An experiment with a real world dataset shows that the modeling is identical with state-space models and with the proposed GP kernels. The considered connection allows the researchers to look at their models from a different angle and facilitate sharing ideas between these two different modeling approaches.
DOI:10.48550/arxiv.1610.08074