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Crude Oil Price Prediction Based on a Dynamic Correcting Support Vector Regression Machine
Joint Authors
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-03-18
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
A new accurate method on predicting crude oil price is presented, which is based on ε-support vector regression (ε-SVR) machine with dynamic correction factor correcting forecasting errors.
We also propose the hybrid RNA genetic algorithm (HRGA) with the position displacement idea of bare bones particle swarm optimization (PSO) changing the mutation operator.
The validity of the algorithm is tested by using three benchmark functions.
From the comparison of the results obtained by using HRGA and standard RNA genetic algorithm (RGA), respectively, the accuracy of HRGA is much better than that of RGA.
In the end, to make the forecasting result more accurate, the HRGA is applied to the optimize parameters of ε-SVR.
The predicting result is very good.
The method proposed in this paper can be easily used to predict crude oil price in our life.
American Psychological Association (APA)
Shu-rong, Li& Yu-lei, Ge. 2013. Crude Oil Price Prediction Based on a Dynamic Correcting Support Vector Regression Machine. Abstract and Applied Analysis،Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-478923
Modern Language Association (MLA)
Shu-rong, Li& Yu-lei, Ge. Crude Oil Price Prediction Based on a Dynamic Correcting Support Vector Regression Machine. Abstract and Applied Analysis No. 2013 (2013), pp.1-7.
https://search.emarefa.net/detail/BIM-478923
American Medical Association (AMA)
Shu-rong, Li& Yu-lei, Ge. Crude Oil Price Prediction Based on a Dynamic Correcting Support Vector Regression Machine. Abstract and Applied Analysis. 2013. Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-478923
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-478923