期刊论文

中文

湖南农业大学学报(自然科学版)

1007-1032

2010

36

4

430-433

10.3724/SP.J.1238.2010.00430

国家自然科学基金项目（30570351）；

本校为第一机构

提出一种基于ARIMA和动态ε支持向量机（ε-DSVM）的组合预测模型（ARIMA-ε-DSVM）,预测松毛虫发生面积.先采用ARIMA模型进行时间序列线性趋势建模,为非线性部分确定输入阶数,根据确定的输入阶数进行时间序列样本重构,再采用ε-DSVM模型进行时间序列非线性特征建模,将这两模型预测值相加得到组合模型预测值.对辽宁省朝阳市松毛虫时间序列进行仿真试验,结果表明,ARIMA-ε-DSVM模型预测精确度比单一模型ARIMA和SVM及简单组合模型ARIMA-SVM要高,ARIMA-ε-DSVM模型大幅度改善预测效果,显著地减少预测误差,泛化能力强.

A novel forecasting model combinating autoregressive integrating moving average（ARIMA） with dynamic ε-insensitive cost function support vector machine（ε-DSVM）was brought forth, which showed the complicated and dynamic characteristics of Dendrolimus punctatus occurrence. ARIMA model was used to capture the linear feature of the time series and ε-DSVM model to fit the nonlinear component of the time series to obtain the ensemble forecasting result by adding ARIMA to ε-DSVM. The prediction performances of the method was tested by Dendrolim...