Extreme learning machine ensemble model for time series forecasting boosted by PSO: Application to an electric consumption problem

Abstract

Ensemble Model is a tool that has attracted attention due to its capability to improve the outcome performance of emerging techniques to solve the modelling and classifying problem. However, the feasibility of applying intelligent algorithms to build the Ensemble Model presents a challenge of its own. In this work, an Extreme Learning Machine ensemble is applied to the Time Series modelling problem. We develop a thorough study of the models that will be candidates to compose the ensemble, obtaining statistical results of optimal topologies to solve each Time Series problem. The proposed method for the ensemble is the weighted averaging method, where the parameters (weights) are tuned with the Particle Swarm Optimization algorithm. Lastly, the ensemble is tested first in the well known Santa Fe Time Series Competition benchmark. Given the obtained satisfactory results, the ensemble is secondly tested in a real problem of Spain’s electric consumption forecasting. It is demonstrated that the PSO is a suitable algorithm to optimize Extreme Learning Machine ensemble and that the proposed strategy can obtain good results in both Time Series problems.