Gaussian Process Kernels for Popular State-Space Time Series Models

Alexander Grigorievskiy; Juha Karhunen

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arXiv · 1610.08074

Gaussian Process Kernels for Popular State-Space Time Series Models

Alexander Grigorievskiy·Juha Karhunen

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Abstract

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.

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BibTeX RIS

Alexander Grigorievskiy, Juha Karhunen. 2016-10-25. Gaussian Process Kernels for Popular State-Space Time Series Models. https://arxiv.org/abs/1610.08074

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