Selection of the appropriate lag structure of foreign exchange rates forecasting based on autocorrelation coefficient

Authors:
Wei Huang;Shouyang Wang;Hui Zhang;Renbin Xiao
Affiliations:
School of Management, Huazhong University of Science and Technology, Wuhan, China;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, Ishikawa, Japan;School of Management, Huazhong University of Science and Technology, Wuhan, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

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Abstract

We propose a new criterion, called autocorrelation coefficient criterion (ACC) to select the appropriate lag structure of foreign exchange rates forecasting with neural networks, and design the corresponding algorithm. The criterion and algorithm are data-driven in that there is no prior assumption about the models for time series under study. We conduct the experiments to compare the prediction performance of the neural networks based on the different lag structures by using the different criterions. The experiment results show that ACC performs best in selecting the appropriate lag structure for foreign exchange rates forecasting with neural networks.