摘要
在实际的预测工作中,搜集到的数据,通常不是等时间间距的而是残缺的,怎样利用这种不完整的数据,是预测工作中的难题。本文分析了残缺数据对预测精度的影响并就如何有效地利用MAGNE模型进行了论述。
Frequently a time series contains missing observations in stead of equally space observations. So, how to use the information is a difficult problem in forcasting. This paper analyses the missing observations' influence on the results of forcasting. It presents two modified methods to handle missing data within the smoothing methods.
出处
《五邑大学学报(社会科学版)》
1991年第3期48-53,共6页
Journal of Wuyi University(Social Sciences Edition)