计算机工程与应用Issue(3)：13-17,5.DOI:10.3778/j.issn.1002-8331.1306-0339
相空间重构和SVR联合优化的短时交通流预测
Short-term traffic flow prediction model of phase space reconstruction and Support Vector Regression with combination optimization
摘要
Abstract
Predicting short time traffic flow needs phase reconstruction at first, and then the traffic flow is computed with prediction model of time series. Support Vector Regression(SVR)is a popular forecasting model that has better generality with more powerful theoretical base. However, the embedding dimensions and time delay of phase reconstruction and the parameters of SVR are computed independently, which is difficult to get the optimal parameters in the meanwhile so that accuracy of prediction is not good. In order to improve the accuracy of prediction, a method of short time prediction is proposed which uses combination of phase reconstruction and Support Vector Regression. In this model, the parameters of phase reconstruction and Support Vector Regression are optimized with combination using the PSO. The experiment conducted by traffic dataset has shown that the new method improves the performance of short-term traffic flow forecasting model.关键词
短时交通流/预测模型/相空间重构/支持向量回归机Key words
short-term traffic flow/prediction model/phase space reconstruction/Supporting Vector Regression(SVR)分类
信息技术与安全科学引用本文复制引用
刘建华..相空间重构和SVR联合优化的短时交通流预测[J].计算机工程与应用,2014,(3):13-17,5.基金项目
福建省自然科学基金（No.2012J01246）；福建省科技厅重点项目（No.2012H0002）；福建省资助省属高校基金（No.JK2011035）。 （）
