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| 基于小波分解的害虫发生非平稳时间序列分析和预测 | |
| 引用本文： | 朱军生,翟保平,刘英智.基于小波分解的害虫发生非平稳时间序列分析和预测[J].南京农业大学学报,2011,34(3). |
| 作者姓名： | 朱军生 翟保平 刘英智 |
| 作者单位： | 1. 南京农业大学昆虫学系,江苏,南京,210095;山东省植物保护总站,山东,济南,250100 2. 南京农业大学昆虫学系,江苏,南京,210095 3. 烟台市植物保护站,山东,烟台,264500 |
| 基金项目： | 国家973计划项目(2006CB102007); 国家科技支撑计划项目(2006BAD08A01) |
| 摘 要： | 将小波分解应用于害虫发生程度非平稳时间序列的分析和预测。通过小波分解,将非平稳时间序列分离为多个平稳分量,然后采用自回归滑动平均方法对各平稳分量分别进行分析和建模,最后将所有分量的模型进行组合,从而可以得到原非平稳时间序列的预测模型。在实例分析中,利用1959年至2004年烟台市一代玉米螟发生程度数据序列建立了预测模型,利用2005年至2009年的数据对模型进行了检验。检验结果表明:5年预测准确率达到了80%,预测效果令人满意。 |
| 关 键 词： | 小波分解 多分辨率分析 非平稳时间序列 玉米螟 预测 |
Analysis and forecasting for non-stationary time series of pest occurrence degree based on wavelet decomposition |
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| ZHU Jun-sheng,ZHAI Bao-ping,LIU Ying-zhi.Analysis and forecasting for non-stationary time series of pest occurrence degree based on wavelet decomposition[J].Journal of Nanjing Agricultural University,2011,34(3). | |
| Authors: | ZHU Jun-sheng ZHAI Bao-ping LIU Ying-zhi |
| Institution: | ZHU Jun-sheng1,2,ZHAI Bao-ping1,LIU Ying-zhi3(1.Department of Entomology,Nanjing Agricultural University,Nanjing 210095,China,2.Plant Protection Station of Shandong,Jinan 250100,3.Plant Protection Station of Yantai,Yantai 264500,China) |
| Abstract: | Wavelet decomposition was applied to analyze and forecast for non-stationary time series of pest occurrence degree.The non-stationary time series was decomposed into several stationary components with wavelet decomposition.Then,every stationary component was analyzed by auto-regressive moving average method and a model was established.Finally,the models of all stationary components were combined to obtain the model of the original non-stationary time series.The occurrence degree data series of Ostrinia furn... |
| Keywords: | wavelet decomposition multi-resolution analysis non-stationary time series Ostrinia furnacalis forecasting |
| 本文献已被 CNKI 万方数据 等数据库收录！ | |