A least squares support vector machine model optimized by moth-flame optimization algorithm for annual power load forecasting

2016 ◽  
Vol 45 (4) ◽  
pp. 1166-1178 ◽  
Author(s):  
Cunbin Li ◽  
Shuke Li ◽  
Yunqi Liu
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Optimization Algorithm ◽  
Support Vector Machine Model ◽  
Load Forecasting ◽  
Support Vector ◽  
Machine Model ◽  
Power Load ◽  
Power Load Forecasting ◽  
Moth Flame Optimization Algorithm

scholarly journals A Hybrid Least Square Support Vector Machine Model with Parameters Optimization for Stock Forecasting

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Jian Chai ◽  
Jiangze Du ◽  
Kin Keung Lai ◽  
Yan Pui Lee
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Support Vector Machine Model ◽  
Optimization Methods ◽  
Parameters Optimization ◽  
Least Square ◽  
Movement Direction ◽  
Support Vector ◽  
Machine Model ◽  
Mode Decomposition

This paper proposes an EMD-LSSVM (empirical mode decomposition least squares support vector machine) model to analyze the CSI 300 index. A WD-LSSVM (wavelet denoising least squares support machine) is also proposed as a benchmark to compare with the performance of EMD-LSSVM. Since parameters selection is vital to the performance of the model, different optimization methods are used, including simplex, GS (grid search), PSO (particle swarm optimization), and GA (genetic algorithm). Experimental results show that the EMD-LSSVM model with GS algorithm outperforms other methods in predicting stock market movement direction.

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