A Study on Training Criteria for Financial Time Series Forecasting
نویسنده
چکیده
Traditional backpropagation neural networks training criterion is based on goodness-of-fit which is also the most popular criterion forecasting. How ever, in the context of financial time series forecasting, we are not only concerned at how good the forecasts fit their target. In order to increase the forecastability in terms of profit earning, we propose a profit based adjusted weight factor for backpropagation network training. Instead of using the traditional least squares error, we add a factor which contains the profit, direction, and time information to the error function. This article reports the analysis on the performance of several neural network training criteria. The results show that the new approach does improve the forecastability of neural network models, for the financial application domain
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