Currency exchange rate forecasting by error backpropagation

The paper describes a neural network system for forecasting time series and its application to a non-trivial task in forecasting currency exchange rates. The architecture consists of a two-layer backpropagation network with a fixed number of inputs modelling a window moving along the time series in fixed steps to capture the regularities in the underlying data. Several network configurations are described and the results are analysed. The effect of varying the window and step size is also discussed as are the effects of overtraining. The error backpropagation network was trained with currency exchange data for the period 1988-9 on hourly updates. The first 200 trading days were used as the training set and the following three months as the test set. The network is evaluated both for long term forecasting without feedback (i.e. only the forecast prices are used for the remaining trading days) and for short term forecasting with hourly feedback. By careful network design and analysis of the training set, the backpropagation learning procedure is an active way of forecasting time series. The network learns the training set near perfect and shows accurate prediction, making at least 20% profit on the last 60 trading days of 1989.<
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