Combining Artificial Neural Network and Particle Swarm System for time series forecasting

Forecasting systems have been widely used for decision making and one of its most promising approaches is based on Artificial Neural Networks (ANN). In this paper, a hybrid swarm system is presented for the time series forecasting problem, which consists of an intelligent hybrid model composed of an ANN combined with Particle Swarm Optimizer (PSO). The proposed method searches 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 an efficient procedure to adjust the ANN parameters through the use of a particle swarm optimization mechanism. An experimental analysis is conducted with the proposed method using six real world time series and the results are discussed according to five performance measures.