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

Authors:
Wei Huang;Yoshiteru Nakamori;Shouyang Wang;Hui Zhang
Affiliations:
School of Knowledge Science, Japan Advanced Institute of Science and Technology, Tatsunokuchi, Ishikawa, Japan and Institute of Systems Science, Academy of Mathematics and Systems Sciences, Chines ...;School of Knowledge Science, Japan Advanced Institute of Science and Technology, Tatsunokuchi, Ishikawa, Japan;Institute of Systems Science, Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing, China;School of Knowledge Science, Japan Advanced Institute of Science and Technology, Tatsunokuchi, Ishikawa, Japan
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

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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.