Open AccessA Hybrid Particle Swarm Optimization and Support Vector Regression Model for Financial Time Series Forecasting
Author(s) -
Horng-I Hsieh,
Tsung-Pei Lee,
TianShyug Lee
Publication year - 2011
Publication title -
international journal of business administration
Language(s) - English
Resource type - Journals
eISSN - 1923-4015
pISSN - 1923-4007
DOI - 10.5430/ijba.v2n2p48
Subject(s) - particle swarm optimization , support vector machine , computer science , stock exchange , time series , series (stratigraphy) , stock market index , index (typography) , econometrics , finance , data mining , artificial intelligence , machine learning , mathematics , stock market , economics , paleontology , horse , world wide web , biology
In this paper, a time series forecasting approach by integrating particle swarm optimization (PSO) and support vector regression (SVR) is proposed. SVR has been widely applied in time series predictions. However, no general guidelines are available to choose the free parameters of an SVR model. The proposed approach uses PSO to search the optimal parameters for model selections in the hope of improving the performance of SVR. In order to evaluate the performance of the proposed approach, the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) closing cash index is used as the illustrative example. Experimental results show that the proposed model outperforms the traditional SVR model and provides an alternative in financial time series forecasting