Construction of an informative hierarchical prior distribution. Application to electricity load forecasting

In this paper, we are interested in the estimation and prediction of a parametric model on a short dataset upon which it is expected to overfit and perform badly. To overcome the lack of data (relatively to the dimension of the model) we propose the construction of a hierarchical informative Bayesian prior based upon another longer dataset which is assumed to share some similarities with the original, short dataset. We apply the methodology to a basic model for the electricity load forecasting on both simulated and real datasets, where it leads to a substantial improvement of the quality of the predictions.