A novel auto-regressive fractionally integrated moving average–least-squares support vector machine model for electricity spot prices prediction

2013 ◽  
Vol 41 (3) ◽  
pp. 635-651 ◽  
Author(s):  
Najeh Chaâbane
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Support Vector Machine Model ◽  
Moving Average ◽  
Support Vector ◽  
Machine Model ◽  
Spot Prices ◽  
Auto Regressive

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.

Accuracy improvement of iron ore analysis using laser-induced breakdown spectroscopy with a hybrid sparse partial least squares and least-squares support vector machine model

2018 ◽  
Vol 33 (8) ◽  
pp. 1330-1335 ◽  
Author(s):  
Y. M. Guo ◽  
L. B. Guo ◽  
Z. Q. Hao ◽  
Y. Tang ◽  
S. X. Ma ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Partial Least Squares ◽  
Iron Ore ◽  
Support Vector Machine Model ◽  
Support Vector ◽  
Accuracy Improvement ◽  
Machine Model ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

A hybrid sparse partial least squares and least-squares support vector machine model was proposed to improve the accuracy of iron ore analysis using LIBS.

A mixed effects least squares support vector machine model for classification of longitudinal data

2012 ◽  
Vol 56 (3) ◽  
pp. 611-628 ◽  
Author(s):  
Jan Luts ◽  
Geert Molenberghs ◽  
Geert Verbeke ◽  
Sabine Van Huffel ◽  
Johan A.K. Suykens
Keyword(s):  
Support Vector Machine ◽  
Longitudinal Data ◽  
Least Squares ◽  
Support Vector Machine Model ◽  
Mixed Effects ◽  
Support Vector ◽  
Machine Model

scholarly journals Correction: Accuracy improvement of iron ore analysis using laser-induced breakdown spectroscopy with a hybrid sparse partial least squares and least-squares support vector machine model

2020 ◽  
Vol 35 (7) ◽  
pp. 1487-1487
Author(s):  
Y. M. Guo ◽  
L. B. Guo ◽  
Z. Q. Hao ◽  
Y. Tang ◽  
S. X. Ma ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Iron Ore ◽  
Support Vector Machine Model ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Accuracy Improvement ◽  
Machine Model ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Correction for ‘Accuracy improvement of iron ore analysis using laser-induced breakdown spectroscopy with a hybrid sparse partial least squares and least-squares support vector machine model’ by Y. M. Guo et al., J. Anal. At. Spectrom., 2018, 33, 1330–1335, DOI: 10.1039/C8JA00119G.

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