摘要
目的:探索黄瓜霜霉病疾病指数时间序列预测方法。方法:采用黄瓜霜霉病病情指数时间序列进行研究,通过模型识别、残差方差比较、参数估计及其检验、观察参数之间相关系数矩阵、白噪声检验、模型的拟合度分析等过程。结果:建立了ARIMA(2,2,0)模型:(1+0.487 1B+0.554 7B2)(1-B)2yt=at。ARIMA(2,2,0)模型的预测值误差平方和SSE=0.001822,根均方误差RMSE=0.008 537,且验证数据的预测值与原始值吻合较好。ARIMA(2,2,0)模型为本研究获得的预测效果较好的一维时间序列模型,适合于黄瓜霜霉病的中期、后期预测。结论:通过残差方差定阶法缩小模型选择范围,再结合模型的参数估计、相关系数矩阵、白噪声检验和拟合优度检验最后确定模型的思路,有利于快速准确找到合适的模型。
Objective：To explore the forecasting method of disease index time series of cucumber downy mildew disease. Methods： Using the time series of cucumber downy mildew disease, we established an autoregressive integrated moving average model,ARIMA（2,2,0） based on model identification, comparison of residual variance, estimation and verification of parameter, observation of the correlation of the estimates matrix, autocorrelation check of the residuals, analysis of the fitting of model and so on. Results： An ARIMA model （2,2,0） was established： （1＋0. 487 1B＋0. 554 7B^2）（1-B）^2y, =α1, with the Sum of Squared Error （SSE） being 0. 001 822 and the Root of Mean Squared Error （RMSE） being 0. 008 537. The predicted values of validating date fitted well with the primary values. The established model showed satisfactory forecasting ability and was suitable for forecasting the middle stage and late stage cucumber downy mildew disease. Conclusion： Limiting the alternatives of model by residual variance, together with parameters estimation, the correlation of the estimates matrix, the autocorrelation check of the residuals and the fitting test, can help to search for suitable model quickly and accurately.
出处
《第二军医大学学报》
CAS
CSCD
北大核心
2006年第7期729-732,共4页
Academic Journal of Second Military Medical University
基金
上海市科委科技攻关计划(03DZ19314)~~
作者简介
华来庆，硕士．
Corresponding author. E-mail： xionglinping@ yahoo, com. cn
