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Feedforward versus recurrent neural networks for forecasting monthly Japanese yen exchange rates. (English) Zbl 1153.91783
Summary: Neural networks are a relatively new computer artificial intelligence method which attempt to mimic the brain’s problem solving process and can be used for predicting nonlinear economic time series. Neural networks are used to look for patterns in data, learn these patterns, and then classify new patterns and make forecasts. Feedforward neural networks pass the data forward from input to output, while recurrent networks have a feedback loop where data can be fed back into the input at some point before it is fed forward again for further processing and final output. Some have argued that since time series data may have autocorrelation or time dependence, the recurrent neural network models which take advantage of time dependence may be useful. Feedforward and recurrent neural networks are used for comparison in forecasting the Japanese yen/US dollar exchange rate. A traditional ARIMA model is used as a benchmark for comparison with the neural network models.Results for out of sample show that the feedforward model is relatively accurate in forecasting both price levels and price direction, despite being quite simple and easy to use. However, the recurrent network forecast performance was lower than that of the feedforward model. This may be because feed forward models must pass the data from back to forward as well as forward to back, and can sometimes become confused or unstable. Both the feedforward and recurrent models performed better than the ARIMA benchmark model.

91B84Economic time series analysis
91B28Finance etc. (MSC2000)
91B82Statistical methods in economics; economic indices and measures
62P05Applications of statistics to actuarial sciences and financial mathematics
92B20General theory of neural networks (mathematical biology)
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