Evaluating Gaussian processes for sparse irregular spatio-temporal data

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.