华侨大学学报（自然科学版）2018，Vol.39Issue(2)：281-285,5.DOI:10.11830/ISSN.1000-5013.201708019
KPCA-LSSVM方法在视频时间序列预测中应用
Application of KPCA-LSSVM in Video Trace and Time Series Prediction
摘要
Abstract
A prediction method based on kernel principal component analysis (KPCA) and least squares support vector machine (LSSVM) is proposed for the prediction of time series that increasing prediction precision and decreasing the computing complexity.Firstly,the input data will be mapped to high-dimensional feature space through kernel method,then the effective nonlinear principal element can be extracted in the feature space,and finally the time series model is established by LSSVM.In order to verify the validity of KPCA-LSSVM method,it is used in traffic flow and video flow prediction,and compared with single LSSVM and neural network in the same condition.The experimental results show that the model based on KPCA-LSSVM has good generalization and high identification accuracy compared with other methods.关键词
时间序列预测/交通流量/视频流量/核主成分分析/最小二乘支持向量机Key words
time series prediction/traffic flow/video flow/kernel principal component analysis/least squares support vector machine分类
信息技术与安全科学引用本文复制引用
张观东,李军..KPCA-LSSVM方法在视频时间序列预测中应用[J].华侨大学学报（自然科学版）,2018,39(2):281-285,5.基金项目
国家自然科学基金资助项目(51467008) （51467008）
