  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.
