Open AccessTime Series Decomposition using Automatic Learning Techniques for Predictive Models
Author(s) -
Jesús Silva,
Hugo Hernández Palma,
William Niebles Núñez,
David Ovallos-Gazabon,
Noel Varela
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1432/1/012096
Subject(s) - decomposition , series (stratigraphy) , computer science , time series , set (abstract data type) , statistic , machine learning , artificial intelligence , decomposition method (queueing theory) , data mining , mathematics , statistics , ecology , paleontology , biology , programming language
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.