Application of Two-Directional Time Series Models to Replace Missing Data

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., t...

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Published inJournal of environmental engineering (New York, N.Y.) Vol. 136; no. 4; pp. 435 - 443
Main Authors Huo, Jinsheng, Cox, Chris D, Seaver, William L, Robinson, R. Bruce, Jiang, Yan
Format Journal Article
LanguageEnglish
Published Reston, VA American Society of Civil Engineers 01.04.2010
Subjects
Applied sciences
Exact sciences and technology
General purification processes
Pollution
Sewerage works: sewers, sewage treatment plants, outfalls
TECHNICAL PAPERS
Wastewaters
Water treatment and pollution
Activated sludge
Water treatment
Uncertainty
Statistical analysis
Time series analysis
Prediction
Time series
Wastewater management
Data processing
Modeling
Statistics
Wastewater treatment plants (WWTPs)
Wilcoxon Mann-Whitney test
Sewage treatment plant
Waste water
Exponential smoothing method
Missing data
Time series models
Activated sludge model
Water treatment plants
Seasonal variation
Trend analysis
Water quality
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Summary: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.
ISSN:0733-9372
1943-7870
DOI:10.1061/(ASCE)EE.1943-7870.0000171