Conformal Anomaly Detection in Event Sequences

Shuai Zhang, Chuan Zhou, Yang Liu, Peng Zhang, Xixun Lin, Shirui Pan
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:77177-77193, 2025.

Abstract

Anomaly detection in continuous-time event sequences is a crucial task in safety-critical applications. While existing methods primarily focus on developing a superior test statistic, they fail to provide guarantees regarding the false positive rate (FPR), which undermines their reliability in practical deployments. In this paper, we propose CADES (Conformal Anomaly Detection in Event Sequences), a novel test procedure based on conformal inference for the studied task with finite-sample FPR control. Specifically, by using the time-rescaling theorem, we design two powerful non-conformity scores tailored to event sequences, which exhibit complementary sensitivities to different abnormal patterns. CADES combines these scores with Bonferroni correction to leverage their respective strengths and addresses non-identifiability issues of existing methods. Theoretically, we prove the validity of CADES and further provide strong guarantees on calibration-conditional FPR control. Experimental results on synthetic and real-world datasets, covering various types of anomalies, demonstrate that CADES outperforms state-of-the-art methods while maintaining FPR control.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-zhang25dn, title = {Conformal Anomaly Detection in Event Sequences}, author = {Zhang, Shuai and Zhou, Chuan and Liu, Yang and Zhang, Peng and Lin, Xixun and Pan, Shirui}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {77177--77193}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/zhang25dn/zhang25dn.pdf}, url = {https://proceedings.mlr.press/v267/zhang25dn.html}, abstract = {Anomaly detection in continuous-time event sequences is a crucial task in safety-critical applications. While existing methods primarily focus on developing a superior test statistic, they fail to provide guarantees regarding the false positive rate (FPR), which undermines their reliability in practical deployments. In this paper, we propose CADES (Conformal Anomaly Detection in Event Sequences), a novel test procedure based on conformal inference for the studied task with finite-sample FPR control. Specifically, by using the time-rescaling theorem, we design two powerful non-conformity scores tailored to event sequences, which exhibit complementary sensitivities to different abnormal patterns. CADES combines these scores with Bonferroni correction to leverage their respective strengths and addresses non-identifiability issues of existing methods. Theoretically, we prove the validity of CADES and further provide strong guarantees on calibration-conditional FPR control. Experimental results on synthetic and real-world datasets, covering various types of anomalies, demonstrate that CADES outperforms state-of-the-art methods while maintaining FPR control.} }
Endnote
%0 Conference Paper %T Conformal Anomaly Detection in Event Sequences %A Shuai Zhang %A Chuan Zhou %A Yang Liu %A Peng Zhang %A Xixun Lin %A Shirui Pan %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-zhang25dn %I PMLR %P 77177--77193 %U https://proceedings.mlr.press/v267/zhang25dn.html %V 267 %X Anomaly detection in continuous-time event sequences is a crucial task in safety-critical applications. While existing methods primarily focus on developing a superior test statistic, they fail to provide guarantees regarding the false positive rate (FPR), which undermines their reliability in practical deployments. In this paper, we propose CADES (Conformal Anomaly Detection in Event Sequences), a novel test procedure based on conformal inference for the studied task with finite-sample FPR control. Specifically, by using the time-rescaling theorem, we design two powerful non-conformity scores tailored to event sequences, which exhibit complementary sensitivities to different abnormal patterns. CADES combines these scores with Bonferroni correction to leverage their respective strengths and addresses non-identifiability issues of existing methods. Theoretically, we prove the validity of CADES and further provide strong guarantees on calibration-conditional FPR control. Experimental results on synthetic and real-world datasets, covering various types of anomalies, demonstrate that CADES outperforms state-of-the-art methods while maintaining FPR control.
APA
Zhang, S., Zhou, C., Liu, Y., Zhang, P., Lin, X. & Pan, S.. (2025). Conformal Anomaly Detection in Event Sequences. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:77177-77193 Available from https://proceedings.mlr.press/v267/zhang25dn.html.

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