Entropy-based dynamic graph embedding for anomaly detection on multiple climate time series

Abstract Abnormal climate event is that some meteorological conditions are extreme in a certain time interval. The existing methods for detecting abnormal climate events utilize supervised learning models to learn the abnormal patterns, but they cannot detect the untrained patterns. To overcome this...

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Autores principales:	Gen Li, Jason J. Jung
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/87fe7e6aa21248538cee0035defeeb3c
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id	oai:doaj.org-article:87fe7e6aa21248538cee0035defeeb3c
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spelling	oai:doaj.org-article:87fe7e6aa21248538cee0035defeeb3c2021-12-02T15:23:07ZEntropy-based dynamic graph embedding for anomaly detection on multiple climate time series10.1038/s41598-021-92973-82045-2322https://doaj.org/article/87fe7e6aa21248538cee0035defeeb3c2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92973-8https://doaj.org/toc/2045-2322Abstract Abnormal climate event is that some meteorological conditions are extreme in a certain time interval. The existing methods for detecting abnormal climate events utilize supervised learning models to learn the abnormal patterns, but they cannot detect the untrained patterns. To overcome this problem, we construct a dynamic graph by discovering the correlation among the climate time series and propose a novel dynamic graph embedding model based on graph entropy called EDynGE to discriminate anomalies. The graph entropy measurement quantifies the information of the graphs and constructs the embedding space. We conducted experiments on synthetic datasets and real-world meteorological datasets. The results showed that EdynGE model achieved a better F1-score than the baselines by 43.2%, and the number of days of abnormal climate events has increased by 304.5 days in the past 30 years.Gen LiJason J. JungNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Gen Li Jason J. Jung Entropy-based dynamic graph embedding for anomaly detection on multiple climate time series
description	Abstract Abnormal climate event is that some meteorological conditions are extreme in a certain time interval. The existing methods for detecting abnormal climate events utilize supervised learning models to learn the abnormal patterns, but they cannot detect the untrained patterns. To overcome this problem, we construct a dynamic graph by discovering the correlation among the climate time series and propose a novel dynamic graph embedding model based on graph entropy called EDynGE to discriminate anomalies. The graph entropy measurement quantifies the information of the graphs and constructs the embedding space. We conducted experiments on synthetic datasets and real-world meteorological datasets. The results showed that EdynGE model achieved a better F1-score than the baselines by 43.2%, and the number of days of abnormal climate events has increased by 304.5 days in the past 30 years.
format	article
author	Gen Li Jason J. Jung
author_facet	Gen Li Jason J. Jung
author_sort	Gen Li
title	Entropy-based dynamic graph embedding for anomaly detection on multiple climate time series
title_short	Entropy-based dynamic graph embedding for anomaly detection on multiple climate time series
title_full	Entropy-based dynamic graph embedding for anomaly detection on multiple climate time series
title_fullStr	Entropy-based dynamic graph embedding for anomaly detection on multiple climate time series
title_full_unstemmed	Entropy-based dynamic graph embedding for anomaly detection on multiple climate time series
title_sort	entropy-based dynamic graph embedding for anomaly detection on multiple climate time series
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/87fe7e6aa21248538cee0035defeeb3c
work_keys_str_mv	AT genli entropybaseddynamicgraphembeddingforanomalydetectiononmultipleclimatetimeseries AT jasonjjung entropybaseddynamicgraphembeddingforanomalydetectiononmultipleclimatetimeseries
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Entropy-based dynamic graph embedding for anomaly detection on multiple climate time series

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