%0 Journal Article
%T Multidimensional time series analysis based on support vector regression and controlled autoregressive and its application in ecology<br>基于SVR和CAR的多维时间序列分析及其在生态学中的应用
%A ZHANG Yong-Sheng
%A YUAN Zhe-Ming
%A XIONG Jie-Yi
%A ZHOU Tie-Jun
%A <br>张永生
%A 袁哲明
%A 熊洁仪
%A 周铁军
%J 生态学报
%D 2007
%I 
%X Based on support vector regression (SVR) and controlled autoregressive (CAR), we proposed a new non-linear multidimensional time series method named SVR-CAR that can show the dynamic characteristics of sample set as well as the effect of environmental factors. To evaluate the performance of SVR-CAR, we compared its predictions with those of four other commonly-used methods, using two sets of real-world data and one-step prediction. The results showed that SVR-CAR had the highest accuracy in prediction among the five methods, and had the advantages of structural risk minimization, non-linear characteristics, avoiding over-fit, and strong capacity for generalization. SVR-CAR has the potential to be widely used for predictions involving multidimensional time series data in ecology, agricultural sciences and economics.
%K multidimensional time series
%K support vector regression
%K nonlinearity
%K forecast
%K mean square error<br>多维时间序列
%K 支持向量回归
%K 非线性
%K 预测
%K 均方差
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=90BA3D13E7F3BC869AC96FB3DA594E3FE34FBF7B8BC0E591&jid=FE163E5DB2274E5937319DE98913EC37&aid=4683477487370193&yid=A732AF04DDA03BB3&vid=DB817633AA4F79B9&iid=B31275AF3241DB2D&sid=35ACD3314F0EF900&eid=6EA56638C0C4285DEA7B963CAC96D05D&journal_id=1000-0933&journal_name=生态学报&referenced_num=0&reference_num=28