| Título: | Time Series Decomposition using Automatic Learning Techniques for Predictive Models |
| Autores: | Silva, Jesus ; Hernández Palma, Hugo ; Niebles Nú?z, William ; Ovallos-Gazabon, David ; Varela, Noel |
| Tipo de documento: | texto impreso |
| Editorial: | Institute of Physics Publishing, 2020-07-02T17:08:29Z |
| Dimensiones: | application/pdf |
| Nota general: |
Journal of Physics: Conference Series 1432 1 info:eu-repo/semantics/openAccess Attribution-NonCommercial-ShareAlike 4.0 International http://creativecommons.org/licenses/by-nc-sa/4.0/ |
| Idiomas: | Inglés |
| Palabras clave: | Facultad de Negocios , Pregrado , Artículos científicos , Administración y Finanzas |
| 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. |
| En línea: | 17426588 |
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