A New Insight Into Prediction Modeling Systems
Journal of Integrated Design & Process Science
Expert Systems with Applications: An International Journal
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Predicting Trading Signals of Stock Market Indices Using Neural Networks
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Financial time-series analysis with rough sets
Applied Soft Computing
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Goodness-of-fit is the most popular criterion for neural network time series forecasting. In the context of financial time series forecasting, we are not only concerned at how good the forecasts fit their targets, but we are more interested in profits. 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 square error, we add a factor which contains the profit, direction, and time information to the error function. The results show that this new approach does improve the forecastability of neural network models, for the financial application domain.