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Imputation Strategies for Missing Data in Environmental Time Series for an Unlucky Situation

  • Daria Mendola
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

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.

Keywords

Multiple Imputation ARMA Model Imputation Procedure Imputation Strategy Anomalous Period 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin · Heidelberg 2005

Authors and Affiliations

  • Daria Mendola
    • 1
  1. 1.Dipartimento di Scienze Statistiche e Matematiche “Silvio Vianelli”Universitá degli Studi di PalermoPalermoItaly

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