Hybrid Neural Network Model in Forecasting Aggregate U.S. Retail Sales
Advances in Business and Management Forecasting
ISBN: 978-1-78190-331-5, eISBN: 978-1-78190-332-2
Publication date: 13 March 2013
Abstract
Retail sales usually exhibit strong trend and seasonal patterns. Practitioners have typically used seasonal autoregressive integrated moving average (ARIMA) models to predict retail sales exhibiting these patterns. Due to economic instability, recent retail sales time-series data show a higher degree of variability and nonlinearity, which makes the ARIMA model less accurate. This chapter demonstrates the feasibility and potential of applying empirical mode decomposition (EMD) in forecasting aggregate retail sales. The hybrid forecasting method of integrating EMD and neural network (EMD-NN) models was applied to two real data sets from two different time periods. The one-period ahead forecasts for both time periods show that EMD-NN outperforms the classical NN model and seasonal ARIMA. In addition, the findings also indicate that EMD-NN can significantly improve forecasting performance during the periods in which macroeconomic conditions are more volatile.
Keywords
Citation
Pan, Y., Pohlen, T. and Manago, S. (2013), "Hybrid Neural Network Model in Forecasting Aggregate U.S. Retail Sales", Lawrence, K.D. and Klimberg, R.K. (Ed.) Advances in Business and Management Forecasting (Advances in Business and Management Forecasting, Vol. 9), Emerald Group Publishing Limited, Leeds, pp. 153-170. https://doi.org/10.1108/S1477-4070(2013)0000009013
Publisher
:Emerald Group Publishing Limited
Copyright © 2013, Emerald Group Publishing Limited