- 专利标题: Robust anomaly and change detection utilizing sparse decomposition
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申请号: US16904249申请日: 2020-06-17
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公开(公告)号: US11095544B1公开(公告)日: 2021-08-17
- 发明人: Aishwarya Asesh , Sunav Choudhary , Shiv Kumar Saini , Chris Challis
- 申请人: Adobe Inc.
- 申请人地址: US CA San Jose
- 专利权人: Adobe Inc.
- 当前专利权人: Adobe Inc.
- 当前专利权人地址: US CA San Jose
- 代理机构: Keller Jolley Preece
- 主分类号: G06F16/2458
- IPC分类号: G06F16/2458 ; G06F16/248 ; G06F7/00 ; H04L12/26
摘要:
The present disclosure describes systems, non-transitory computer-readable media, and methods for determining latent components of a metrics time series and identifying anomalous data within the metrics time series based on one or both of spikes/dips and level changes from the latent components satisfying significance thresholds. To identify such latent components, in some cases, the disclosed systems account for a range of value types by intelligently subjecting real values to a latent-component constraint for decomposing the time series and intelligently excluding non-real values from the latent-component constraint. The disclosed systems can further identify significant anomalous data values from latent components of the metrics time series by jointly determining whether one or both of a subseries of a spike-component series and a level change from a level-component series satisfy significance thresholds.
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