Study and Application on building a Forecasting Model with Improved Grey relational analysis and Support Vector Machines
In tradition, Grey System treats any random variations as a variation in the grey value within a certain range, and the random process is treated as a time-varying grey process within a certain range. Grey System successfully utilizes accumulated generating data instead of original data to build forecasting model, which makes raw data stochastic weak, or reduces noise influence to a certain extent. However, only one factor has been considered in the conventional model. In most cases, prediction problems usually consist of more than one factor. Therefore, a grey relational analysis with Support Vector Machine (GASVM) is proposed in this study to deal with series problems with multi-factor. In this study, an admixture is presented based on Grey System and Support Vector Machines. Pretreatment modules which grey relational analysis attribution reduction algorithm course endow different weight to each influencing factors. In addition, the new influencing factors were regarded as input factors. At last, the predictive performance is checked. The prediction results prove that this regression module help to improve the prediction precision.
Yao-jin Lin Shun-xiang Wu
Department of Computer Science and Engineering at Zhangzhou Normal University Department, Xiamen University, Xiamen University,Xiamen, China 361005
国际会议
2009 IEEE International Conference on Grey System and Intelligent Services(2009 IEEE灰色系统与服务科学国际会议)
南京
英文
42-46
2009-10-20（万方平台首次上网日期，不代表论文的发表时间）