Time Series Decomposition using Automatic Learning Techniques for Predictive Models

• Autores: Silva, Jesús, Hernández Palma, Hugo, Niebles Núẽz, William, Ovallos-Gazabon, David, Varela, Noel
• Formato: artículo
• Fecha de Publicación: 2020
• Institución: Universidad Peruana de Ciencias Aplicadas
• Repositorio: UPC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorioacademico.upc.edu.pe:10757/652144
• Enlace del recurso: http://hdl.handle.net/10757/652144
• Nivel de acceso: acceso abierto
• Materia: Time series; Agricultural sector; Automatic-learning; Decomposition methods; Predictive modeling; Predictive models; Predictive performance; Production prediction; Time series decomposition; Learning systems

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.