Forecasting strong seasonal time series with artificial neural networks

Many 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.