A hybrid ARIMA and support vector machines model in stock price forecasting

Traditionally, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Support vector machines (SVMs), a novel neural network technique, have been...

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Bibliographic Details
Published inOmega (Oxford) Vol. 33; no. 6; pp. 497 - 505
Main Authors Pai, Ping-Feng, Lin, Chih-Sheng
Format Journal Article
LanguageEnglish
Published Exeter Elsevier Ltd 01.12.2005
Elsevier
Elsevier Science Publishers
Pergamon Press Inc
SeriesOmega
Subjects
Applied sciences
ARIMA
Artificial neural networks
Artificial neural networks ARIMA Support vector machines Time series forecasting Stock prices
Exact sciences and technology
Forecasting
Linear models (Statistics)
Linear regression models
Mathematical models
Neural networks
Operational research and scientific management
Operational research. Management science
Portfolio theory
Regression analysis
Stock price forecasting
Stock prices
Studies
Support vector machines
Time series forecasting
United States
Support vector machines
Stock prices
Time series forecasting
ARIMA
Artificial neural networks
ARMA model
Statistical analysis
Non linear regression
Time series
Linear time
Nonlinear problems
Regression analysis
Neural network
Modeling
Forecasting
Capture
Linear model
Autoregressive integrated moving average model
Vector support machine
Problem solving
Occupation time
Strength
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Summary:Traditionally, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Support vector machines (SVMs), a novel neural network technique, have been successfully applied in solving nonlinear regression estimation problems. Therefore, this investigation proposes a hybrid methodology that exploits the unique strength of the ARIMA model and the SVMs model in forecasting stock prices problems. Real data sets of stock prices were used to examine the forecasting accuracy of the proposed model. The results of computational tests are very promising.
ISSN:0305-0483
1873-5274
DOI:10.1016/j.omega.2004.07.024