Adaptive methodology based on computational intelligence for time series modeling

Abstract

Time series processes are important in several sectors like marketing, transport, energy, telecommunications, etc. Time series forecasting tasks can help in operative and strategic tasks. Several conventional and non-conventional techniques as ARIMA models, artificial neural networks (ANN), support vector machines (SVM), Regression Tree Ensembles (RTE) or combinations of them have been used for time series modeling. The implementation of this type of techniques provides support in time series modeling, however, normally the trained models may lose performance due to the dynamic behavior of the phenomena. A methodology capable to assess the performance and maintenance of the models is necessary to guarantee the automatic adaptability in each case. Hence, in this research an adaptive methodology based on computational intelligence for time series modeling is proposed. In this case, an Auditor is developed, which allows identifying when a model must be retrained or updated before losing forecast performance. Furthermore, when the retrain process is not achieving a better performance, a new metric is proposed to choose which time series modeling technique is included in the knowledge base. The intelligent system allows building the time series model automatically, considering exogenous variables such as weather, calendar and statistical transformations to group and simplify the number of models required.