Authors: Moody, John
Source: Keynote talk presented at the International Symposium on Artificial Neural Networks, Hsinchu, Taiwan, December 1995.
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Macroeconomic forecasting is a very difficult task due to the lack of an accurate, convincing model of the economy. The most accurate models for economic forecasting, ``black box'' time series models, assume little about the structure of the economy. Constructing reliable time series models is challenging due to short data series, high noise levels, nonstationarities, and nonlinear effects. This paper describes these challenges and surveys some neural network solutions to them. Important issues include balancing the bias/variance tradeoff and the noise/nonstationarity tradeoff. The methods surveyed include hyperparameter selection (regularization parameter and training window length), input variable selection and pruning, network architecture selection and pruning, new smoothing regularizers, and committee forecasts. Empirical results are presented for forecasting the U.S. Index of Industrial Production. These demonstrate that, relative to conventional linear time series and regression methods, superior performance can be obtained using state-of-the-art neural network models.
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