【摘要】 基于地统计学半变异函数发展了一种新的多维时间序列最优阶数判断方法,并结合支持向量回归建立了既反映样本集动态特征又体现环境因子影响的非线性多维时间序列分析预测模型(GS-SVR).用一步预测法对两个生态学样本集的预测结果表明,GS-SVR预测精度高,并具结构风险最小、非线性、避免过拟合、泛化推广能力强等诸多优点.更多还原

【Abstract】 A novel model order optimization method based on semivariogram of geostatistics(GS) was proposed. With the combination of this method with support vector regression(SVR) ,we constructed a new non-linear forecasting model of multidimensional time series analysis named GS-SVR that can show the dynamic characteristics of sample set as well as the effect of environmental factor. To evaluate the performance of GS-SVR,two sets of ecology data were predicted by one-step method,the results showed that GS-SVR had the highest accuracy and had the advantages of structural risk minimization,non-linear characteristics,avoiding over-fit,and strong capacity for generalization. GS-SVR has potential to be widely used for predictions involving multidimensional time series data in ecology.更多还原