Time series forecasting using neural networks: should the data be deseasonalized first?

Abstract

This research investigates whether prior statistical deseasonalization of data is necessary to produce more accurate neural network forecasts. Neural networks trained with deseasonalized data from Hill et al. (1996) were compared with neural networks estimated without prior deseasonalization. Both sets of neural networks produced forecasts for the 68 monthly time series from the M-competition (Makridakis et al., 1982). Results indicate that when there was seasonality in the time series, forecasts from neural networks estimated on deseasonalized data were significantly more accurate than the forecasts produced by neural networks that were estimated using data which were not deseasonalized. The mixed results from past studies may be due to inconsistent handling of seasonality. Our findings give guidance to both practitioners and researchers developing neural networks. Copyright © 1999 John Wiley & Sons, Ltd.