Time Series Decomposition using Automatic Learning Techniques for Predictive Models

Silva J.; Hernández Palma H.; Niebles Núẽz W.; Ovallos-Gazabon D.; Varela N. · Journal of Physics: Conference Series (2020) · DOI: 10.1088/1742-6596/1432/1/012096

Resumen

This paper proposes an innovative way to address real cases of production prediction. This approach consists in the decomposition of original time series into time sub-series according to a group of factors in order to generate a predictive model from the partial predictive models of the sub-series. The adjustment of the models is carried out by means of a set of statistic techniques and Automatic Learning. This method was compared to an intuitive method consisting of a direct prediction of time series. The results show that this approach achieves better predictive performance than the direct way, so applying a decomposition method is more appropriate for this problem than non-decomposition. The agricultural sector will be used as the study subject. © Published under licence by IOP Publishing Ltd.

Instituciones: Universidad Peruana de Ciencias Aplicadas