Time Series Prediction Based on Support Vector Regression

Research Article

Time Series Prediction Based on Support Vector Regression

Shukuan Lin, Guoren Wang, Shaomin Zhang and Jingyin Li

This study introduces Support Vector Regression (SVR) model and predicts on time series based on SVR, proposing several new approaches which improve traditional SVR in order to enhance prediction accuracy. In terms of the feature of time series, the approaches give different weights to different history data at different times and make the punishing coefficient C and non-sensitive loss ε in optimization objective function of SVR adjustable along with different sample data. The experimental results show that the proposed approaches improve the prediction precision and testify the validity of these approaches.

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Shukuan Lin, Guoren Wang, Shaomin Zhang and Jingyin Li, 2006. Time Series Prediction Based on Support Vector Regression. Information Technology Journal, 5: 353-357.

DOI: 10.3923/itj.2006.353.357

URL: https://scialert.net/abstract/?doi=itj.2006.353.357

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Authors

Shukuan Lin

Guoren Wang

Shaomin Zhang

Jingyin Li

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Research Article

Time Series Prediction Based on Support Vector Regression

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