Missing values in time series are not uncommon due to system failures or external interference during data collection. A multitude of imputation algorithms have been proposed to infer these missing values. However, existing methods often overlook the difference in sample data quality within the dataset. Specifically, training imputation algorithms on low-quality samples can lead to the generation of poor-quality data, which adversely affects the performance of downstream models. To address this issue, we propose integrating a data quality evaluation with the imputation process. The workflow involves imputing missing values using an imputation algorithm, evaluating the data quality of each sample, and then removing low-quality samples. Our experimental results demonstrate the effectiveness of this approach on improving the performance of downstream model across four datasets, seven input algorithms, four quality assessment methods, and two types of machine learning tasks. Additionally, we convert time series data into complex networks and find that network features can effectively explain the data quality of individual samples.
Huang et al. (2026) studied this question.