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Time Series Decomposition using Automatic Learning Techniques for Predictive Models

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.

Identiferoai:union.ndltd.org:PERUUPC/oai:repositorioacademico.upc.edu.pe:10757/652144
Date07 January 2020
CreatorsSilva, Jesús, Hernández Palma, Hugo, Niebles Núẽz, William, Ovallos-Gazabon, David, Varela, Noel
PublisherInstitute of Physics Publishing
Source SetsUniversidad Peruana de Ciencias Aplicadas (UPC)
LanguageEnglish
Detected LanguageEnglish
Typeinfo:eu-repo/semantics/article
Formatapplication/pdf
SourceJournal of Physics: Conference Series, 1432, 1
Rightsinfo:eu-repo/semantics/openAccess, Attribution-NonCommercial-ShareAlike 4.0 International, http://creativecommons.org/licenses/by-nc-sa/4.0/
Relationhttps://iopscience.iop.org/article/10.1088/1742-6596/1432/1/012096

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