A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates

Authors:
Lean Yu;Shouyang Wang;K. K. Lai
Affiliations:
Institute of Systems Science, Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing 100080, China and Graduate School of Chinese Academy of Sciences, Beijing 100080, Ch ...;Institute of Systems Science, Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing 100080, China and Institute of Policy and Planning, University of Tsukuba, Japan;Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China
Venue:
Computers and Operations Research
Year:
2005

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Abstract

In this study, we propose a novel nonlinear ensemble forecasting model integrating generalized linear autoregression (GLAR) with artificial neural networks (ANN) in order to obtain accurate prediction results and ameliorate forecasting performances. We compare the new model's performance with the two individual forecasting models--GLAR and ANN--as well as with the hybrid model and the linear combination models. Empirical results obtained reveal that the prediction using the nonlinear ensemble model is generally better than those obtained using the other models presented in this study in terms of the same evaluation measurements. Our findings reveal that the nonlinear ensemble model proposed here can be used as an alternative forecasting tool for exchange rates to achieve greater forecasting accuracy and improve prediction quality further.