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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Huang, W., Nakamori, Y., Wang, S., Zhang, H. (2005). Select the Size of Training Set for Financial Forecasting with Neural Networks. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_141
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DOI: https://doi.org/10.1007/11427445_141
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
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