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.
| Identifer | oai:union.ndltd.org:PERUUPC/oai:repositorioacademico.upc.edu.pe:10757/652144 |
| Date | 07 January 2020 |
| Creators | Silva, Jesús, Hernández Palma, Hugo, Niebles Núẽz, William, Ovallos-Gazabon, David, Varela, Noel |
| Publisher | Institute of Physics Publishing |
| Source Sets | Universidad Peruana de Ciencias Aplicadas (UPC) |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/article |
| Format | application/pdf |
| Source | Journal of Physics: Conference Series, 1432, 1 |
| Rights | info:eu-repo/semantics/openAccess, Attribution-NonCommercial-ShareAlike 4.0 International, http://creativecommons.org/licenses/by-nc-sa/4.0/ |
| Relation | https://iopscience.iop.org/article/10.1088/1742-6596/1432/1/012096 |
Page generated in 0.0054 seconds