A Hybrid Approach for Modeling
Financial Time Series

Alina Barbulescu and Elena Bautu
 Faculty of Mathematics and Computer Science, Ovidius University, Constanta, Romania

 

Abstract: The problem we tackle concerns forecasting time series in financial markets. AutoRegressive Moving-Average (ARMA) methods and computational intelligence have also been used to tackle this problem. We propose a novel method for time series forecasting based on a hybrid combination of ARMA and Gene Expression Programming (GEP) induced models. Time series from financial domains often encapsulate different linear and non-linear patterns. ARMA models, although flexible, assume a linear form for the models. GEP evolves models adapting to the data without any restrictions with respect to the form of the model or its coefficients. Our approach benefits from the capability of ARMA to identify linear trends as well as GEP’s ability to obtain models that capture nonlinear patterns from data. Investigations are performed on real data sets. They show a definite improvement in the accuracy of forecasts of the hybrid method over pure ARMA and GEP used separately. Experimental results are analyzed and discussed. Conclusions and some directions for further research end the paper.

Keywords: Financial time series, forecasting, ARMA, GEP, and hybrid methodology.

 

  

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