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ARIMA与SVM组合模型在害虫预测中的应用
引用本文:向昌盛,周子英.ARIMA与SVM组合模型在害虫预测中的应用[J].昆虫学报,2010,53(9):1055-1060.
作者姓名:向昌盛  周子英
基金项目:湖南省教育厅科学研究资助项目
摘    要:害虫发生是一种复杂、 动态时间序列数据, 单一预测模型都是基于线性或非线性数据, 不能同时捕捉害虫发生的线性和非线性规律, 很难达到理想的预测精度。本研究首先采用差分自回归移动平均模型对昆虫发生时间序列进行线性建模, 然后采用支持向量机对非线性部分进行建模, 最后得到两种模型的组合预测结果。将组合模型应用到松毛虫Dendrolimus punctatus发生面积的预测, 实验结果表明组合模型的预测精度明显优于单一模型, 发挥了两种模型各自的优势。组合模型是一种切实可行的害虫预测预报方法。

关 键 词:害虫  松毛虫  发生预测  时间序列  支持向量机  差分自回归移动平均模型  

Application of ARIMA and SVM hybrid model in pest forecast
XIANG Chang-Sheng,ZHOU Zi-Ying.Application of ARIMA and SVM hybrid model in pest forecast[J].Acta Entomologica Sinica,2010,53(9):1055-1060.
Authors:XIANG Chang-Sheng  ZHOU Zi-Ying
Abstract:The data of pest occurrence are complicated and unpredictable time series. The linear or nonlinear features of pest time series can not be captured based on single prediction model. A new hybrid forecasting model based on autoregressive integrating moving average (ARIMA) and support vector machine (SVM) is proposed in this paper. ARIMA model was used to predict the linear component while SVM model was used for the nonlinear residual component of pest time series, and then the hybrid forecasting results were obtained. The prediction performances of the method were tested on Dendrolimus punctatus occurrence area. The results show that the hybrid model, which combines the respective advantages of both linear and nonlinear models, has better accuracy than any single model. Hybrid model is a good and effective method for pest forecasting.
Keywords:Pest insects  Dendrolimus punctatus  occurrence forecast  time series  SVM  ARIMA model
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