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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8618081/ https://www.ncbi.nlm.nih.gov/pubmed/34831969 http://dx.doi.org/10.3390/ijerph182212213 |
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author | Kim, Taesung Kim, Jinhee Yang, Wonho Lee, Hunjoo Choo, Jaegul |
author_facet | Kim, Taesung Kim, Jinhee Yang, Wonho Lee, Hunjoo Choo, Jaegul |
author_sort | Kim, Taesung |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8618081 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-86180812021-11-27 Missing Value Imputation of Time-Series Air-Quality Data via Deep Neural Networks Kim, Taesung Kim, Jinhee Yang, Wonho Lee, Hunjoo Choo, Jaegul Int J Environ Res Public Health Article 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. MDPI 2021-11-20 /pmc/articles/PMC8618081/ /pubmed/34831969 http://dx.doi.org/10.3390/ijerph182212213 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kim, Taesung Kim, Jinhee Yang, Wonho Lee, Hunjoo Choo, Jaegul Missing Value Imputation of Time-Series Air-Quality Data via Deep Neural Networks |
title | Missing Value Imputation of Time-Series Air-Quality Data via Deep Neural Networks |
title_full | Missing Value Imputation of Time-Series Air-Quality Data via Deep Neural Networks |
title_fullStr | Missing Value Imputation of Time-Series Air-Quality Data via Deep Neural Networks |
title_full_unstemmed | Missing Value Imputation of Time-Series Air-Quality Data via Deep Neural Networks |
title_short | Missing Value Imputation of Time-Series Air-Quality Data via Deep Neural Networks |
title_sort | missing value imputation of time-series air-quality data via deep neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8618081/ https://www.ncbi.nlm.nih.gov/pubmed/34831969 http://dx.doi.org/10.3390/ijerph182212213 |
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