A PSO Boosted Ensemble of Extreme Learning Machines for Time Series Forecasting

摘要

In this work, a first approach of using the Particle Swarm Optimization (PSO) as a method for optimizing an Ensemble Model built with Extreme Learning Machines is presented. The paper focuses on the obtaining of the parameters of a weighted averaging method for a Ensemble Model, using Extreme Learning Machines as models. The main contribution of this document is the use of the heuristic algorithm PSO for searching optimum parameters of the weighted averaging method. The experiments show that PSO is suitable for computing the parameters of the ensemble, obtaining an average improvement of 68% of the error comparing with an individual model. Also other comparisons have been made with basic combining methods of Ensemble Model fulfilling the expectations.