Effectiveness of PSO Based Neural Network for Seasonal Time Series Forecasting

Recently, the Particle Swarm Optimization (PSO) technique has gained much attention in the field of time series forecasting. Although PSO trained Artificial Neural Networks (ANNs) performed reasonably well in stationary time series forecasting, their effectiveness in tracking the structure of non-stationary data (especially those which contain trends or seasonal patterns) is yet to be justified. In this paper, we have trained neural networks with two types of PSO (Trelea1 and Trelea2) for forecasting seasonal time series data. To assess their performances, experiments are conducted on three well-known real world seasonal time series. Obtained forecast errors in terms of three common performance measures, viz. MSE, MAE and MAPE for each dataset are compared with those obtained by the Seasonal ANN (SANN) model, trained with a standard backpropagation algorithm. Comparisons demonstrate that training with PSO-Trelea1 and PSO-Trelea2 produced significantly better results than the standard backpropagation rule.