Like many other economic time series, US aggregate retail sales have strong trend and seasonal patterns. How to best model and forecast these patterns has been a long-standing issue in time-series analysis. This article compares artificial neural networks and traditional methods including Winters exponential smoothing, Box–Jenkins ARIMA model, and multivariate regression. The results indicate that on average ANNs fare favorably in relation to the more traditional statistical methods, followed by the Box–Jenkins model. Despite its simplicity, the Winters model was shown to be a viable method for multiple-step forecasting under relatively stable economic conditions. The derivative analysis shows that the neural network model is able to capture the dynamic nonlinear trend and seasonal patterns, as well as the interactions between them.
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