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Financial time series prediction by a hybrid memetic computation-based support vector regression (MA-SVR) method

Published Online:pp 321-339

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.


financial time series, support vector regression, SVR, memetics, local search, time series prediction, optimisation, memetic computation