Imputation strategies for missing data in environmental time series for an unlucky situation

被引：1
|
作者：
Mendola, D [1 ]
机构：
[1] Univ Palermo, Dipartimento Sci Stat & Matemat Silvio Vianelli, I-90128 Palermo, Italy
来源：
INNOVATIONS IN CLASSIFICATION, DATA SCIENCE, AND INFORMATION SYSTEMS | 2005年
关键词：
D O I：
10.1007/3-540-26981-9_32
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
After a detailed review of the main specific solutions for treatment of missing data in environmental time series, this paper deals with the unlucky situation in which, in an hourly series, missing data immediately follow an absolutely anomalous period, for which we do not have any similar period to use for imputation. A tentative multivariate and multiple imputation is put forward and evaluated; it is based on the possibility, typical of environmental time series, to resort to correlations or physical laws that characterize relationships between air pollutants.
引用
收藏
页码：275 / 282
页数：8
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