Title: Financial time series prediction by a hybrid memetic computation-based support vector regression (MA-SVR) method
Authors: Mohammad Baboli; Mohammad Saniee Abadeh
Addresses: Department of Electrical, Computer and Biomedical Engineering, Qazvin Branch, Islamic Azad University, Nokhbegan Blvd., P.O. Box 34185-1416, Qazvin, Iran ' Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Jalal Ale Ahmad Highway, P.O. Box 14115-111, Tehran, Iran
Abstract: Always being aware of future economic and investment is important and significant for investors, companies, politicians and public people, in order to come to know the future economic perspective and accordingly how particular policies perform. There are different models in the field of prediction of financial time series that we can perform investment with lower risk by using them. Whatever prediction quantity is more accurate, the investment performs with lower risk. The proposed method of this paper is MA-SVR, which is a combination of memetic algorithm and support vector regression. We use memetic computation to estimate support vector regression method parameters and perform prediction with optimisation of SVR method parameters by using memetic algorithm. We evaluate the proposed MA-SVR method by use of prediction error with RMSE criterion, and compare it with other effective algorithms and we show that the proposed method causes improvement in the result from the view point of closeness of predicted quantities to reality.
Keywords: financial time series; support vector regression; SVR; memetics; local search; time series prediction; optimisation; memetic computation.
International Journal of Operational Research, 2015 Vol.23 No.3, pp.321 - 339
Received: 10 Jan 2013
Accepted: 09 Aug 2013
Published online: 29 May 2015 *