基于Bayes准则的时间序列判别预报模式

TIME SERIES FORECAST MODEL BY DISCRIMINATORY ANALYSIS ACCORDING TO BAYES CRITERION

中文摘要:

      根据Bayes准则下的多元线性判别分析和时间序列的线性自回归模式,本文提出一种时间序列的判别预报模式.该模式采用两种不同的变量筛选方案,对于气象时间序列的数量记录,由过去的记录判别未来记录的趋势(如正负距平、旱涝等).在一定的自相关结构下,其判别效果较好.文献[1-4]曾论述用(0,1)两值时间序列建立AR(p)模式,但AR(p)模式有其局限性.将时间序列与多元判别分析结合,建立时间序列基础上的判别模式,用以往各时刻变量作为线性判别因子对未来各时刻的变量取值类型作出判别,既可保留时间序列线性模式的优点,又可利用多元逐步判别筛选因子的计算方法.从气象状况演变的物理机制来看,考虑前期状态演变比单纯考虑前期某一时刻的状态更有意义.

英文摘要:

      A discriminant prediction model of time series is presented by using of multiple linear discriminant and linear autore}ression models of time series. There are two selection rules of the discriminative predictor,that is (1) the stepwise induction method by schedular order; (2) the choice method by schedular non-order,and both types of selection rules may be used in this model. Thus,this discriminant prediction model may be applied to the digital records of weather time series and this model is effective on the time series under certain sutocorrelation structure.