PSO based neural network for time series forecasting

Abstract

Artificial neural networks are being widely used for time series forecasting. In recent years much effort has been made for the development of particle swarm algorithm for the optimization of neural networks. In this paper, the performance of two variants of particle swarm optimization algorithm (Trelea I and Trelea II) for training neural network has been examined with a real data for financial time series forecasting. Results clearly indicated the superiority of swarm based algorithms over the standard backpropagation training algorithm with respect to common performance measures across three forecasting horizons. In particular, with the Trelea II trained model, we obtained 92.48 %, 56.64 %, and 44.66 % decrease in terms of MSE over the standard back-propagation trained neural network for 10 days, 30 days and 60 days ahead forecasts respectively.