Comparing Gaussian processes and artificial neural networks for forecasting

Abstract

We compare the use of artificial neural networks and Gaussian processes for forecasting. We show that Artificial Neural Networks have the advantage of being utilisable with greater volumes of data but Gaussian processes can more easily be utilised to deal with non-stationarity.