DC Field | Value | Language |
---|---|---|
dc.contributor.author | Nam, Youngeun | ko |
dc.contributor.author | Trirat, Patara | ko |
dc.contributor.author | Kim, Taeyoon | ko |
dc.contributor.author | Lee, Youngseop | ko |
dc.contributor.author | Lee, Jae-Gil | ko |
dc.date.accessioned | 2023-11-07T07:00:59Z | - |
dc.date.available | 2023-11-07T07:00:59Z | - |
dc.date.created | 2023-11-07 | - |
dc.date.issued | 2023-09-19 | - |
dc.identifier.citation | European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023, pp.330 - 345 | - |
dc.identifier.uri | http://hdl.handle.net/10203/314368 | - |
dc.description.abstract | Detecting anomalies in time series has become increasingly challenging as data collection technology develops, especially in real-world communication services, which require contextual information for precise prediction. To address this challenge, researchers usually use time-series decomposition to reveal underlying patterns, e.g., trends and seasonality. However, existing decomposition-based anomaly detectors do not explicitly consider such contextual information, limiting their ability to correctly detect contextual cases. This paper proposes Time-CAD, a new context-aware deep time-series decomposition framework to detect anomalies for a more practical scenario in real-world businesses. We verify the effectiveness of the novel design for integrating contextual information into deep time-series decomposition through extensive experiments on four real-world benchmarks, demonstrating improvements of up to in time-series aware score on average. | - |
dc.language | English | - |
dc.publisher | Springer Science and Business Media Deutschland GmbH | - |
dc.title | Context-Aware Deep Time-Series Decomposition for Anomaly Detection in Businesses | - |
dc.type | Conference | - |
dc.identifier.scopusid | 2-s2.0-85174437407 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 330 | - |
dc.citation.endingpage | 345 | - |
dc.citation.publicationname | European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023 | - |
dc.identifier.conferencecountry | IT | - |
dc.identifier.conferencelocation | Turin | - |
dc.identifier.doi | 10.1007/978-3-031-43427-3_20 | - |
dc.contributor.localauthor | Lee, Jae-Gil | - |
dc.contributor.nonIdAuthor | Kim, Taeyoon | - |
dc.contributor.nonIdAuthor | Lee, Youngseop | - |
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