Application of Two-Directional Time Series Models to Replace Missing Data
Publication: Journal of Environmental Engineering
Volume 136, Issue 4
Abstract
Missing data commonly exist in operational records of wastewater treatment plants, such as influent and effluent water quality data. To deal with missing data, time series models that characterize trend, lag, and seasonality may be applied. In this paper, two-time series model-based methods, i.e., the two-directional exponential smoothing (TES) and TES with white noise (TESWN) added methods, are developed to replace missing data. Comparisons with traditional missing-data-replacement methods are also evaluated in the context of predicting missing values from influent data and the subsequent effect when the resulting influent time series are used as an input to process simulation models. The TES method is shown to be most appropriate when the goal is to minimize the average error associated with the missing value. The TESWN method is shown to be better suited for characterizing the amount of uncertainty that may be associated with the missing values.
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Acknowledgments
This research is partially sponsored by WERF research project (Project No. UNSPECIFIEDWERF-00-CTS-3). Thanks are due to Mr. Ken Glass from the Oak Ridge WWTP and Mr. Tom Heikkinen from the Seneca WWTP.
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Information & Authors
Information
Published In
Journal of Environmental Engineering
Volume 136 • Issue 4 • April 2010
Pages: 435 - 443
Copyright
© 2010 ASCE.
History
Received: Jan 21, 2009
Accepted: Oct 5, 2009
Published online: Oct 6, 2009
Published in print: Apr 2010
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