Missing Value Imputation of Time-Series Air-Quality Data via Deep Neural Networks

To prevent severe air pollution, it is important to analyze time-series air quality data, but this is often challenging as the time-series data is usually partially missing, especially when it is collected from multiple locations simultaneously. To solve this problem, various deep-learning-based mis...

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Bibliographic Details
Published in:International journal of environmental research and public health 2021-11, Vol.18 (22), p.12213
Main Authors: Kim, Taesung, Kim, Jinhee, Yang, Wonho, Lee, Hunjoo, Choo, Jaegul
Format: Article
Language:English
Subjects:
Air pollution
Air quality
Bias
Data collection
Datasets
Deep learning
Experiments
Neural networks
Outdoor air quality
Qualitative analysis
Seasonal variations
Time series
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Summary:To prevent severe air pollution, it is important to analyze time-series air quality data, but this is often challenging as the time-series data is usually partially missing, especially when it is collected from multiple locations simultaneously. To solve this problem, various deep-learning-based missing value imputation models have been proposed. However, often they are barely interpretable, which makes it difficult to analyze the imputed data. Thus, we propose a novel deep learning-based imputation model that achieves high interpretability as well as shows great performance in missing value imputation for spatio-temporal data. We verify the effectiveness of our method through quantitative and qualitative results on a publicly available air-quality dataset.
ISSN:1660-4601
1661-7827
1660-4601
DOI:10.3390/ijerph182212213