信息替换的均生函数主分量多步预测

提出随着时间的推移，用新信息取代旧信息的“限定记忆”的时间序列数据处理方法。且仅对均生函数外延矩阵的前Ｌ阶方阵作主分量分析，用数目较少又包含主要信息量的主分量因子对时间序列建模。该方案用于四川省２８个县市的年平均气温的多步预测值与实况值相对误差均在±４％以内，表明该方案用于气温多步预测是有效的。

Multi-Steps Forecast Model with Principal Component of Mean Generating Function by Replacement of Information

method of data analysis of temproal sequences,based on the replacement of new-old information of restrictive memory with lapse of time is developed. This appraoch is based on the modelling of the principal component analysis for the prior L square matrix of period extrapolation matrix Of mean generating function only,with a few principal components inclouding main information of temporal sequence.The scheme is applied to the multi-step forecast of annual meantemperatures of 28 cities in Sichuan,and the relative errors between forecast and real values are not more than 14%,and it is shown that the scheme is effective for the multi-step forecast of temperature.