Construction of an informative hierarchical prior for a small sample with the help of historical data and application to electricity load forecasting
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Tristan Launay [1] ; Anne Philippe [1] ; Sophie Lamarche [2]
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[1] University of Nantes
University of Nantes
Arrondissement de Nantes, Francia
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[2] Electricité de France R&D
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- Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 24, Nº. 2, 2015, págs. 361-385
- Idioma: inglés
- DOI: 10.1007/s11749-014-0416-0
- Texto completo no disponible (Saber más ...)
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Resumen
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 an informative hierarchical Bayesian prior based on another longer dataset which is assumed to share some similarities with the original, short dataset. We illustrate the performance of our prior on simulated datasets from two standard models. We then apply the methodology to a working model for the electricity load forecasting on real datasets, where it leads to a substantial improvement of the quality of the predictions.
