Incorporating Stock Index in a Support Vector Regression Model to Improve Short Term Load Forecasting Accuracy

2012 ◽  
Author(s):  
Cheng-Ting Lin ◽  
Li-Der Chou
Keyword(s):  
Regression Model ◽  
Support Vector Regression ◽  
Load Forecasting ◽  
Stock Index ◽  
Support Vector ◽  
Short Term ◽  
Support Vector Regression Model ◽  
Forecasting Accuracy ◽  
Short Term Load Forecasting

scholarly journals Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1093 ◽  
Author(s):  
Wei-Chiang Hong ◽  
Guo-Feng Fan
Keyword(s):  
Support Vector Regression ◽  
Load Forecasting ◽  
Support Vector ◽  
Electric Load ◽  
Short Term ◽  
Middle Term ◽  
Mode Decomposition ◽  
Random Term ◽  
Forecasting Performance ◽  
Short Term Load Forecasting

For operational management of power plants, it is desirable to possess more precise short-term load forecasting results to guarantee the power supply and load dispatch. The empirical mode decomposition (EMD) method and the particle swarm optimization (PSO) algorithm have been successfully hybridized with the support vector regression (SVR) to produce satisfactory forecasting performance in previous studies. Decomposed intrinsic mode functions (IMFs), could be further defined as three items: item A contains the random term and the middle term; item B contains the middle term and the trend (residual) term, and item C contains the middle terms only, where the random term represents the high-frequency part of the electric load data, the middle term represents the multiple-frequency part, and the trend term represents the low-frequency part. These three items would be modeled separately by the SVR-PSO model, and the final forecasting results could be calculated as A+B-C (the defined item D). Consequently, this paper proposes a novel electric load forecasting model, namely H-EMD-SVR-PSO model, by hybridizing these three defined items to improve the forecasting accuracy. Based on electric load data from the Australian electricity market, the experimental results demonstrate that the proposed H-EMD-SVR-PSO model receives more satisfied forecasting performance than other compared models.

scholarly journals ℓp-Norm Multikernel Learning Approach for Stock Market Price Forecasting

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Xigao Shao ◽  
Kun Wu ◽  
Bifeng Liao
Keyword(s):  
Regression Model ◽  
Support Vector Regression ◽  
Market Price ◽  
Stock Index ◽  
Support Vector ◽  
Optimization Strategy ◽  
Support Vector Regression Model ◽  
Stock Price Prediction ◽  
Multiple Kernel ◽  
Multiple Support

Linear multiple kernel learning model has been used for predicting financial time series. However,ℓ1-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adoptℓp-norm multiple kernel support vector regression (1≤p<∞) as a stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better thanℓ1-norm multiple support vector regression model.

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