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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Taesung Kim & Jinhee Kim & Wonho Yang & Hunjoo Lee & Jaegul Choo, 2021. "Missing Value Imputation of Time-Series Air-Quality Data via Deep Neural Networks," IJERPH, MDPI, vol. 18(22), pages 1-8, November.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:22:p:12213-:d:684094
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    References listed on IDEAS

    as
    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Honaker, James & King, Gary & Blackwell, Matthew, 2011. "Amelia II: A Program for Missing Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i07).
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    1. Filip Arnaut & Vladimir Đurđević & Aleksandra Kolarski & Vladimir A. Srećković & Sreten Jevremović, 2024. "Improving Air Quality Data Reliability through Bi-Directional Univariate Imputation with the Random Forest Algorithm," Sustainability, MDPI, vol. 16(17), pages 1-17, September.

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