Evaluating Gaussian processes for sparse irregular spatio-temporal data

Mehmet Süzen; Abed Ajraou

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arXiv · 1611.02978

Evaluating Gaussian processes for sparse irregular spatio-temporal data

Mehmet Süzen·Abed Ajraou

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Abstract

A practical approach to evaluate performance of a Gaussian process regression models (GPR) for irregularly sampled sparse time-series is introduced. The approach entails construction of a secondary autoregressive model using the fine scale predictions to forecast a future observation used in GPR. We build different GPR models for Ornstein-Uhlenbeck and Fractional processes for simulated toy data with different sparsity levels to assess the utility of the approach.

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Mehmet Süzen, Abed Ajraou. 2016-11-06. Evaluating Gaussian processes for sparse irregular spatio-temporal data. https://arxiv.org/abs/1611.02978

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