PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916095331401728 |
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| author | Der, Audrey Yeh, Chin-Chia Michael Zheng, Yan Wang, Junpeng Zhuang, Zhongfang Wang, Liang Zhang, Wei Keogh, Eamonn J. |
| author_facet | Der, Audrey Yeh, Chin-Chia Michael Zheng, Yan Wang, Junpeng Zhuang, Zhongfang Wang, Liang Zhang, Wei Keogh, Eamonn J. |
| contents | In recent years there has been significant progress in time series anomaly detection. However, after detecting an (perhaps tentative) anomaly, can we explain it? Such explanations would be useful to triage anomalies. For example, in an oil refinery, should we respond to an anomaly by dispatching a hydraulic engineer, or an intern to replace the battery on a sensor? There have been some parallel efforts to explain anomalies, however many proposed techniques produce explanations that are indirect, and often seem more complex than the anomaly they seek to explain. Our review of the literature/checklists/user-manuals used by frontline practitioners in various domains reveals an interesting near-universal commonality. Most practitioners discuss, explain and report anomalies in the following format: The anomaly would be like normal data A, if not for the corruption B. The reader will appreciate that is a type of counterfactual explanation. In this work we introduce a domain agnostic counterfactual explanation technique to produce explanations for time series anomalies. As we will show, our method can produce both visual and text-based explanations that are objectively correct, intuitive and in many circumstances, directly actionable. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_09489 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies Der, Audrey Yeh, Chin-Chia Michael Zheng, Yan Wang, Junpeng Zhuang, Zhongfang Wang, Liang Zhang, Wei Keogh, Eamonn J. Machine Learning Artificial Intelligence In recent years there has been significant progress in time series anomaly detection. However, after detecting an (perhaps tentative) anomaly, can we explain it? Such explanations would be useful to triage anomalies. For example, in an oil refinery, should we respond to an anomaly by dispatching a hydraulic engineer, or an intern to replace the battery on a sensor? There have been some parallel efforts to explain anomalies, however many proposed techniques produce explanations that are indirect, and often seem more complex than the anomaly they seek to explain. Our review of the literature/checklists/user-manuals used by frontline practitioners in various domains reveals an interesting near-universal commonality. Most practitioners discuss, explain and report anomalies in the following format: The anomaly would be like normal data A, if not for the corruption B. The reader will appreciate that is a type of counterfactual explanation. In this work we introduce a domain agnostic counterfactual explanation technique to produce explanations for time series anomalies. As we will show, our method can produce both visual and text-based explanations that are objectively correct, intuitive and in many circumstances, directly actionable. |
| title | PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2401.09489 |