scholarly journals The Combination of Autoregressive Integrated Moving Average (ARIMA) and Support Vector Machines (SVM) for Daily Rubber Price Forecasting

2020 ◽  
Vol 917 ◽  
pp. 012044
Author(s):  
Lai Jing Jong ◽  
Shuhaida Ismail ◽  
Aida Mustapha ◽  
Mohd Helmy Abd Wahab ◽  
Syed Zulkarnain Syed Idrus
Keyword(s):  
Support Vector Machines ◽  
Moving Average ◽  
Support Vector ◽  
Price Forecasting ◽  
Autoregressive Integrated Moving Average ◽  
Vector Machines

scholarly journals Forecasting Electric Load by Support Vector Machines with Genetic Algorithms

2005 ◽  
Vol 9 (2) ◽  
pp. 134-141 ◽  
Author(s):  
Ping-Feng Pai ◽  
◽  
Wei-Chiang Hong ◽  
Chih-Shen Lin ◽  
◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Support Vector Machines ◽  
Moving Average ◽  
Predictive Performance ◽  
Support Vector ◽  
Electric Load ◽  
Power Company ◽  
Svm Model ◽  
Vector Machines ◽  
General Regression Neural Networks

Support vector machines (SVMs) have been successfully used in solving nonlinear regression and time series problems. However, the application of SVMs to load forecasting is very rare. Therefore, the purpose of this paper is to examine the feasibility of SVMs in forecasting electric load. In addition, the genetic algorithms are applied in the parameter selection of SVM model. Forecasting results compared with other two models, namely autoregressive integrated moving average (ARIMA) and general regression neural networks (GRNN), are provided. The experimental data are borrowed from the Taiwan Power Company. The numerical results indicate that the SVM model with genetic algorithms (SVMG) results in better predictive performance than the other two approaches.

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