Hybrid Swarm System for Time Series Forecasting - M. Neto, Paulo S. G. de; Araújo, Ricardo de A.; Petry, Gustavo G.; Ferreira, Tiago A. E.; Vasconcelos, Germano C. (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2007)
In this paper, a hybrid swarm system is presented for time series forecasting. It consists of an intelligent hybrid model composed of an Artificial Neural Network (ANN) and a Particle Swarm Optimizer (PSO), which search the relevant time lags for a correct characterization of the time series, as well as the number of processing units in the hidden layer, the training algorithm and the modeling of ANN. The proposed method shows to be an efficient procedure to training and adjusting the ANN parameters through the use of a particle swarm optimization mechanism. An experimental analysis is conducted with the proposed method using four real world time series and the results are compared to standard MLP networks according to five performance measures.