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We have investigated selective learning techniques for improving the ability of back-propagation neural networks to predict large changes. We previously proposed the selective-presentation approach, in which the training data corresponding to large changes in the prediction-target time series are presented more often, and selective-learning-rate approach, in which the learning rate for training data corresponding to small changes is reduced. This paper proposes combining these two approaches to achieve fine-tuned and step-by-step selective learning of neural networks according to the degree of change. Daily stock prices are predicted as a noisy real-world problem. Combining these two approaches further improved the performance.

Combining Selective-Presentation and Selective-Learning-Rate Approaches for Neural Network Forecasting of Stock Markets