摘要
根据Box—Jenkins建模原理，采用ARIMA（p，d，q）动态模型对黄河流域棉花品种主要产量性状的时间序列进行了拟合及预测研究。结果显示，所建模型的残差序列均为白噪声，AIC〈274．425，模型拟合值与实际值的相关系数为0．9478～0．9767，拟合度为89．83％～95．39％，表明组建的ARIMA（p，d，q）模型结构合理、拟合效果好；两年试验结果，相对精度95．60％～99．75％，表明运用该种模型对棉花品种各产量性状时间序列的变化趋势进行拟合及预测是可行的，为育种者及时了解棉花品种的发展动向和前景提供了一种新的研究途径。
According to Box-Jenkins theory and ARIMA（p,d,q） dynamic model, the time series of main yield characters of cotton varieties in Yellow River Valley region was identified, simulated and predicted. The results showed： （1） After stationarity identification, the time series of boll weight and lint percentage appeared significantly increasing trend, which indicated that the effort of cotton breeding in the Yellow River Valley Region for improving boll weight and lint percentage achieved great success. But the time series of lint yield kept stationary and random without increasing trend, and that was correlate to that the time series of boll numbers per plant had no increasing trend.＇ According to most researchers＂ reports, boll numbers per plant contributed most to cotton yield increasing. （2） After model identification, the deviation of ARIMA（p,d,q） model were all the white noise series, and the AIC varied from -133. 894 to 274. 425, the correlation coefficient was 0. 9478～0. 9767, and the goodness of fittest was 89.83%～95.39%; The relative accuracy of prediction test for 2004 and 2005 was 95.60%～99.75%, The lint yield, boll numbers per plant, boll weight and lint percentage of varieties that joined Yellow River Valley Regional test in 2006 were predicted as 1398.05 kg · hm^-2 , 16.61, 5.96 g and 40.16%, respectively, which should to be identified in the future. This method was perfect in maths, had a high applicable value, and offered a new way for breeders to grasp the development trend and prospect of cotton varieties. （3） This method was mainly applied to make shortterm forecasting. What＇s more, like other prediction models, ARIMA （p, d, q） model was a dynamic model ,too, and it would change with data addition of time series {yt}. So in order to achieve optimum prediction result, the model should be adjusted in application.
出处
《棉花学报》
CSCD
北大核心
2007年第3期220-226,共7页
Cotton Science
基金
河北省粮棉节本增效集成技术研究(2006045001)
