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Application of data mining technology on particle swarm optimization and support vector regression in shareprice prediction
- Source :
- 2010 2nd IEEE International Conference on Information Management and Engineering.
- Publication Year :
- 2010
- Publisher :
- IEEE, 2010.
-
Abstract
- Precise forecasting for shareprice is very important to investment and financing. Support vector regression, called as SVR, is a novel learning algorithm based on statistical learning theory, which has greater generalization ability than traditional neural networks. In order to select the appropriate parameters of SVR, particle swarm optimization is introduced to choose the user-determined parameters of SVR here. Therefore, data mining technology on particle swarm optimization and support vector regression is presented to shareprice prediction. Closingprice of 23 trading days of routon electronic is applied to testify the feasibility of the proposed method in the shareprice forecasting. The experiment results demonstrate that the proposed algorithm is better than the traditional shareprice forecasting algorithm.
- Subjects :
- Artificial neural network
Computer science
business.industry
Generalization
Particle swarm optimization
Regression analysis
computer.software_genre
Machine learning
Support vector machine
Kernel (linear algebra)
Statistical learning theory
Data mining
Artificial intelligence
Multi-swarm optimization
business
computer
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2010 2nd IEEE International Conference on Information Management and Engineering
- Accession number :
- edsair.doi...........a0d6f3603ff82842032bfa166f95c8cd