OIPR: Evaluation for Time-series Anomaly Detection Inspired by Operator Interest
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909949880172544 |
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| author | Jing, Yuhan Wang, Jingyu Zhang, Lei Sun, Haifeng He, Bo Zhuang, Zirui Wang, Chengsen Qi, Qi Liao, Jianxin |
| author_facet | Jing, Yuhan Wang, Jingyu Zhang, Lei Sun, Haifeng He, Bo Zhuang, Zirui Wang, Chengsen Qi, Qi Liao, Jianxin |
| contents | With the growing adoption of time-series anomaly detection (TAD) technology, numerous studies have employed deep learning-based detectors to analyze time-series data in the fields of Internet services, industrial systems, and sensors. The selection and optimization of anomaly detectors strongly rely on the availability of an effective evaluation for TAD performance. Since anomalies in time-series data often manifest as a sequence of points, conventional metrics that solely consider the detection of individual points are inadequate. Existing TAD evaluators typically employ point-based or event-based metrics to capture the temporal context. However, point-based evaluators tend to overestimate detectors that excel only in detecting long anomalies, while event-based evaluators are susceptible to being misled by fragmented detection results. To address these limitations, we propose OIPR (Operator Interest-based Precision and Recall metrics), a novel TAD evaluator with area-based metrics. It models the process of operators receiving detector alarms and handling anomalies, utilizing area under the operator interest curve to evaluate TAD performance. Furthermore, we build a special scenario dataset to compare the characteristics of different evaluators. Through experiments conducted on the special scenario dataset and five real-world datasets, we demonstrate the remarkable performance of OIPR in extreme and complex scenarios. It achieves a balance between point and event perspectives, overcoming their primary limitations and offering applicability to broader situations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01260 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OIPR: Evaluation for Time-series Anomaly Detection Inspired by Operator Interest Jing, Yuhan Wang, Jingyu Zhang, Lei Sun, Haifeng He, Bo Zhuang, Zirui Wang, Chengsen Qi, Qi Liao, Jianxin Machine Learning With the growing adoption of time-series anomaly detection (TAD) technology, numerous studies have employed deep learning-based detectors to analyze time-series data in the fields of Internet services, industrial systems, and sensors. The selection and optimization of anomaly detectors strongly rely on the availability of an effective evaluation for TAD performance. Since anomalies in time-series data often manifest as a sequence of points, conventional metrics that solely consider the detection of individual points are inadequate. Existing TAD evaluators typically employ point-based or event-based metrics to capture the temporal context. However, point-based evaluators tend to overestimate detectors that excel only in detecting long anomalies, while event-based evaluators are susceptible to being misled by fragmented detection results. To address these limitations, we propose OIPR (Operator Interest-based Precision and Recall metrics), a novel TAD evaluator with area-based metrics. It models the process of operators receiving detector alarms and handling anomalies, utilizing area under the operator interest curve to evaluate TAD performance. Furthermore, we build a special scenario dataset to compare the characteristics of different evaluators. Through experiments conducted on the special scenario dataset and five real-world datasets, we demonstrate the remarkable performance of OIPR in extreme and complex scenarios. It achieves a balance between point and event perspectives, overcoming their primary limitations and offering applicability to broader situations. |
| title | OIPR: Evaluation for Time-series Anomaly Detection Inspired by Operator Interest |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.01260 |