HEPA pretrains via horizon-conditioned JEPA on unlabeled data then fine-tunes only the predictor for event survival CDFs, outperforming PatchTST, iTransformer, MAE and Chronos-2 on at least 10 of 14 benchmarks with fixed hyperparameters, an order of magnitude fewer tuned parameters and less labeled
Towards a rigorous evaluation of time-series anomaly detection
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abstract
In recent years, proposed studies on time-series anomaly detection (TAD) report high F1 scores on benchmark TAD datasets, giving the impression of clear improvements in TAD. However, most studies apply a peculiar evaluation protocol called point adjustment (PA) before scoring. In this paper, we theoretically and experimentally reveal that the PA protocol has a great possibility of overestimating the detection performance; that is, even a random anomaly score can easily turn into a state-of-the-art TAD method. Therefore, the comparison of TAD methods after applying the PA protocol can lead to misguided rankings. Furthermore, we question the potential of existing TAD methods by showing that an untrained model obtains comparable detection performance to the existing methods even when PA is forbidden. Based on our findings, we propose a new baseline and an evaluation protocol. We expect that our study will help a rigorous evaluation of TAD and lead to further improvement in future researches.
fields
cs.LG 3years
2026 3representative citing papers
Active learning with masked reconstruction and minimax training raises AUC by 12.39% across 28 test cases on four multivariate datasets and seven unsupervised backbones.
citing papers explorer
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HEPA: A Self-Supervised Horizon-Conditioned Event Predictive Architecture for Time Series
HEPA pretrains via horizon-conditioned JEPA on unlabeled data then fine-tunes only the predictor for event survival CDFs, outperforming PatchTST, iTransformer, MAE and Chronos-2 on at least 10 of 14 benchmarks with fixed hyperparameters, an order of magnitude fewer tuned parameters and less labeled
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Detecting the Undetectable: Enhancing Unsupervised time series Anomaly Detection via Active Learning
Active learning with masked reconstruction and minimax training raises AUC by 12.39% across 28 test cases on four multivariate datasets and seven unsupervised backbones.
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