Performance of Neural Networks in Managerial Forecasting

This paper investigates the effectiveness of a multi‐layered neural network as a tool for forecasting in a managerial time‐series setting. To handle noisy data of limited length we adopted two different neural network approaches. First, the neural network is used as a pattern classifier to automate the ARMA model‐identification process. We tested the performance of multi‐layered neural networks with two statistical feature extractors: ACF/PACF and ESACF. We found that ESACF provides better performance, although the noise in ESACF patterns still caused the classification performance to deteriorate. Therefore we adopted the noise‐filtering network as a preprocessor to the pattern‐classification network, and were able to achieve an average of about 89% classification accuracy. Second, the neural network is used as a tool for function approximation and prediction. To alleviate the overfitting problem we adopted the structure of minimal networks and recurrent networks. The experiment with three real‐world time series showed that the prediction by Elman's recurrent network outperformed those by the ARMA model and other structures of multi‐layered neural networks, especially when the time series contained significant noise.