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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Autores principales:	Taesung Kim, Jinhee Kim, Wonho Yang, Hunjoo Lee, Jaegul Choo
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	time-series data spatio-temporal data missing value imputation interpretable deep learning air pollution Medicine R
Acceso en línea:	https://doaj.org/article/3ecc1319154346a2ac95bf42d3713e90
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id	oai:doaj.org-article:3ecc1319154346a2ac95bf42d3713e90
record_format	dspace
spelling	oai:doaj.org-article:3ecc1319154346a2ac95bf42d3713e902021-11-25T17:52:03ZMissing Value Imputation of Time-Series Air-Quality Data via Deep Neural Networks10.3390/ijerph1822122131660-46011661-7827https://doaj.org/article/3ecc1319154346a2ac95bf42d3713e902021-11-01T00:00:00Zhttps://www.mdpi.com/1660-4601/18/22/12213https://doaj.org/toc/1661-7827https://doaj.org/toc/1660-4601To 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.Taesung KimJinhee KimWonho YangHunjoo LeeJaegul ChooMDPI AGarticletime-series dataspatio-temporal datamissing value imputationinterpretable deep learningair pollutionMedicineRENInternational Journal of Environmental Research and Public Health, Vol 18, Iss 12213, p 12213 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	time-series data spatio-temporal data missing value imputation interpretable deep learning air pollution Medicine R
spellingShingle	time-series data spatio-temporal data missing value imputation interpretable deep learning air pollution Medicine R Taesung Kim Jinhee Kim Wonho Yang Hunjoo Lee Jaegul Choo Missing Value Imputation of Time-Series Air-Quality Data via Deep Neural Networks
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	article
author	Taesung Kim Jinhee Kim Wonho Yang Hunjoo Lee Jaegul Choo
author_facet	Taesung Kim Jinhee Kim Wonho Yang Hunjoo Lee Jaegul Choo
author_sort	Taesung Kim
title	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_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_sort	missing value imputation of time-series air-quality data via deep neural networks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3ecc1319154346a2ac95bf42d3713e90
work_keys_str_mv	AT taesungkim missingvalueimputationoftimeseriesairqualitydataviadeepneuralnetworks AT jinheekim missingvalueimputationoftimeseriesairqualitydataviadeepneuralnetworks AT wonhoyang missingvalueimputationoftimeseriesairqualitydataviadeepneuralnetworks AT hunjoolee missingvalueimputationoftimeseriesairqualitydataviadeepneuralnetworks AT jaegulchoo missingvalueimputationoftimeseriesairqualitydataviadeepneuralnetworks
_version_	1718411916016091136

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

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