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

Taesung Kim, Jinhee Kim, Wonho Yang, Hunjoo Lee and Jaegul Choo
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Taesung Kim: Kim Jaechul Graduate School of Artificial Intelligence, KAIST, Daehak-ro 291, Yuseong-gu, Daejeon 34141, Korea
Jinhee Kim: Kim Jaechul Graduate School of Artificial Intelligence, KAIST, Daehak-ro 291, Yuseong-gu, Daejeon 34141, Korea
Wonho Yang: Department of Occupation Health, Daegu Catholic University, Gyeongbuk 38430, Korea
Hunjoo Lee: Department of Environmental Big Data, CHEM. I. NET, Ltd., Seoul 07964, Korea
Jaegul Choo: Kim Jaechul Graduate School of Artificial Intelligence, KAIST, Daehak-ro 291, Yuseong-gu, Daejeon 34141, Korea

IJERPH, 2021, vol. 18, issue 22, 1-8

Abstract: 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.

Keywords: time-series data; spatio-temporal data; missing value imputation; interpretable deep learning; air pollution (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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