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本文基于2005年1月~2015年10月月度植物疫情截获量数据和相应外贸进口额数据,利用回归分析和时间序列建模方法分析其时序特征并进行预测。结果表明,进口额每增长1亿美元,疫情截获量约增加47种次,进口额增长率每增长1个百分点,疫情截获量约增长2个百分点。疫情截获量时序存在显著的季节特征和指数型增长趋势,SARIMA模型和残差自回归模型可以较好地拟合疫情截获量时间序列并进行短期预测。最后根据研究结论提出了相关工作建议。
Based on the monthly plant epidemic interception data and the corresponding foreign trade import data from January 2005 to October 2015, the paper analyzed the characteristics of the time series and predicted them by regression analysis and time series modeling. The results showed that for each increase of 100 million U.S. dollars in imports, the number of intercepted cases increased by 47 kinds and the rate of increase of imports by 1 percentage point per year increased by 2 percentage points. The intercepted seizure timing has significant seasonal characteristics and exponential growth trend. SARIMA model and residual autoregressive model can well fit the interception time series of epidemic situation and make short-term prediction. Finally, according to the conclusions of the study put forward relevant work proposals.