High-quality data play a paramount role in monitoring

control

and prediction of wastewater treatment process (WWTP) and can effectively ensure the efficient and stable operation of system. Missing values seriously degrade the accuracy

reliability and completeness of the data quality due to network collapses

connection errors and data transformation failures. In these cases

it is infeasible to recover missing data depending on the correlation with other variables. To tackle this issue

a univariate imputation method (UIM) is proposed for WWTP integrating decomposition method and imputation algorithms. First

the seasonal-trend decomposition based on loess method is utilized to decompose the original time series into the seasonal

trend and remainder components to deal with the nonstationary characteristics of WWTP data. Second

the support vector regression is used to approximate the nonlinearity of the trend and remainder components respectively to provide estimates of its missing values. A self-similarity decomposition is conducted to fill the seasonal component based on its periodic pattern. Third

all the imputed results are merged to obtain the imputation result. Finally

six time series of WWTP are used to evaluate the imputation performance of the proposed UIM by comparing with existing seven methods based on two indicators. The experimental results illustrate that the proposed UIM is effective for WWTP time series under different missing ratios. Therefore

the proposed UIM is a promising method to impute WWTP time series.