Select the size of training set for financial forecasting with neural networks

Abstract

The performance of financial forecasting with neural networks dependents on the particular training set. We design mean-change-point test to divide the original dataset into different training sets. The experiment results show that the larger training set does not necessarily produce better forecasting performance. Although the original datasets are different, the change-points to produce the optimal training sets are close to each other. We can select the suitable size of training set for financial forecasting with neural networks based on the mean-change-point test. © Springer-Verlag Berlin Heidelberg 2005.