A Strategy to Complete a Time Series with Missing Observations

Conference paper
Part of the Lecture Notes in Statistics book series (LNS, volume 25)


The problem introduced in this paper originated from a water pollution study of the Menomenee River in Milwaukee, Wisconsin. The final goal of the study was to estimate the total pollutant load deposited in Lake Michigan, for a given season. However, the information on the concentration of pollutants was not complete. Concentration data were available for only 20% of the days in the study period. Complete data were available on two related series: river flow rate and water equivalent of precipitation. Those series were used along with the pollutant concentration data to create a model for pollutant behavior (for the different pollutants and years), to estimate the missing observations and to estimate total pollutant loading for several seasons. (See Miller, et al (1980).)


Time Series Conditional Expectation Time Series Model ARMA Model Stationary Time Series 
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Copyright information

© Springer-Verlag Berlin Heidelberg 1984

Authors and Affiliations

  1. 1.University of WisconsinUSA

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