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Neural Networks for Time Series Forecasting: Practical Implications of Theoretical Results.

Research on the performance of neural networks in modeling nonlinear time series has produced mixed results. While neural networks have great potential because of their status as universal approximators (Hornik, Stinchcombe, and White 1989), their flexibility can lead to estimation problems. When Faraway and Chatfield (1998) used an autoregressive neural network to forecast airline data, they found that the neural networks they specified frequently would not converge. When they did converge, they failed to find the global minimum of the objective function. In some cases, neural networks that fit the in-sample data well performed poorly on holdout samples. In conducting the NN3 competition, a time series forecasting competition designed to showcase autoregressive neural networks and other computationally-intensive methods of forecasting, standard methods such as ARIMA models still out-performed autoregressive neural networks (Crone et