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

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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.
Publisher
MDPI
Issue Date
2021-11
Language
English
Article Type
Article
Citation

INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, v.18, no.22

ISSN
1661-7827
DOI
10.3390/ijerph182212213
URI
http://hdl.handle.net/10203/290798
Appears in Collection
AI-Journal Papers(저널논문)
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