PSO based Neural Networks vs. Traditional Statistical Models for Seasonal Time Series Forecasting

Seasonality is a distinctive characteristic which is often observed in many practical time series. Artificial Neural Networks (ANNs) are a class of promising models for efficiently recognizing and forecasting seasonal patterns. In this paper, the Particle Swarm Optimization (PSO) approach is used to enhance the forecasting strengths of feedforward ANN (FANN) as well as Elman ANN (EANN) models for seasonal data. Three widely popular versions of the basic PSO algorithm, viz. Trelea-I, Trelea-II and Clerc-Type1 are considered here. The empirical analysis is conducted on three real-world seasonal time series. Results clearly show that each version of the PSO algorithm achieves notably better forecasting accuracies than the standard Backpropagation (BP) training method for both FANN and EANN models. The neural network forecasting results are also compared with those from the three traditional statistical models, viz. Seasonal Autoregressive Integrated Moving Average (SARIMA), Holt-Winters (HW) and Support Vector Machine (SVM). The comparison demonstrates that both PSO and BP based neural networks outperform SARIMA, HW and SVM models for all three time series datasets. The forecasting performances of ANNs are further improved through combining the outputs from the three PSO based models.Comment: 4 figures, 4 tables, 31 references, conference proceeding

Forecasting strong seasonal time series with artificial neural networks

657-666Many practical time series often exhibit trends and seasonal patterns. The traditional statistical models eliminate the effect of seasonality from a time series before making future forecasts. As a result, the computational complexities are increased together with substantial reductions in overall forecasting accuracies. This paper comprehensively explores the outstanding ability of Artificial Neural Networks (ANNs) in recognizing and forecasting strong seasonal patterns without removing them from the raw data. Six real-world time series having dominant seasonal fluctuations are used in our work. The performances of the fitted ANN for each of these time series are compared with those of three traditional models both manually as well as through a non-parametric statistical test. The empirical results show that the properly designed ANNs are remarkably efficient in directly forecasting strong seasonal variations as well as outperform each of the three statistical models for all six time series. A robust algorithm together with important practical guidelines is also suggested for ANN forecasting of strong seasonal data