Conformalized Time Series Anomaly Thresholding with Latent Space Features

Nancy Xu, Henrik Boström
Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR 329:676-688, 2026.

Abstract

Time series anomaly detection is an important task applicable to a wide range of fields. Many methods have been proposed to address this task, but evaluation often suffers leakage from flawed thresholding methods. In this paper we explore leveraging latent space features in a conformal framework to automatically determine alarm thresholds without the risk of leakage and reducing false alarm rates. We show that our method is able to match or surpass the performance of a popular problematic thresholding method while using only information from the calibration set.

Cite this Paper

BibTeX
@InProceedings{pmlr-v329-xu26a, title = {Conformalized Time Series Anomaly Thresholding with Latent Space Features}, author = {Xu, Nancy and Bostr{\"o}m, Henrik}, booktitle = {Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications}, pages = {676--688}, year = {2026}, editor = {Ahlberg, Ernst and Johansson, Ulf and Boström, Henrik and Carlevaro, Alberto and Hallberg Szabadváry, Johan and Carlsson, Lars}, volume = {329}, series = {Proceedings of Machine Learning Research}, month = {02--04 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v329/main/assets/xu26a/xu26a.pdf}, url = {https://proceedings.mlr.press/v329/xu26a.html}, abstract = {Time series anomaly detection is an important task applicable to a wide range of fields. Many methods have been proposed to address this task, but evaluation often suffers leakage from flawed thresholding methods. In this paper we explore leveraging latent space features in a conformal framework to automatically determine alarm thresholds without the risk of leakage and reducing false alarm rates. We show that our method is able to match or surpass the performance of a popular problematic thresholding method while using only information from the calibration set.} }
Endnote
%0 Conference Paper %T Conformalized Time Series Anomaly Thresholding with Latent Space Features %A Nancy Xu %A Henrik Boström %B Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications %C Proceedings of Machine Learning Research %D 2026 %E Ernst Ahlberg %E Ulf Johansson %E Henrik Boström %E Alberto Carlevaro %E Johan Hallberg Szabadváry %E Lars Carlsson %F pmlr-v329-xu26a %I PMLR %P 676--688 %U https://proceedings.mlr.press/v329/xu26a.html %V 329 %X Time series anomaly detection is an important task applicable to a wide range of fields. Many methods have been proposed to address this task, but evaluation often suffers leakage from flawed thresholding methods. In this paper we explore leveraging latent space features in a conformal framework to automatically determine alarm thresholds without the risk of leakage and reducing false alarm rates. We show that our method is able to match or surpass the performance of a popular problematic thresholding method while using only information from the calibration set.
APA
Xu, N. & Boström, H.. (2026). Conformalized Time Series Anomaly Thresholding with Latent Space Features. Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications, in Proceedings of Machine Learning Research 329:676-688 Available from https://proceedings.mlr.press/v329/xu26a.html.

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