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AnomalyBERT: Self-Supervised Transformer for Time Series Anomaly Detection using Data Degradation Scheme

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arxiv 2305.04468 v1 pith:OCZ4KBIX submitted 2023-05-08 cs.LG cs.AI

Yungi Jeong , Eunseok Yang , Jung Hyun Ryu , Imseong Park , Myungjoo Kang This is my paper · ORCID
classification cs.LGcs.AI
keywords dataseriestimeanomalyanomalybertdegradationschemetemporal
verification ladder T0 review T1 audit T2 compute T3 formal
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Mechanical defects in real situations affect observation values and cause abnormalities in multivariate time series, such as sensor values or network data. To perceive abnormalities in such data, it is crucial to understand the temporal context and interrelation between variables simultaneously. The anomaly detection task for time series, especially for unlabeled data, has been a challenging problem, and we address it by applying a suitable data degradation scheme to self-supervised model training. We define four types of synthetic outliers and propose the degradation scheme in which a portion of input data is replaced with one of the synthetic outliers. Inspired by the self-attention mechanism, we design a Transformer-based architecture to recognize the temporal context and detect unnatural sequences with high efficiency. Our model converts multivariate data points into temporal representations with relative position bias and yields anomaly scores from these representations. Our method, AnomalyBERT, shows a great capability of detecting anomalies contained in complex time series and surpasses previous state-of-the-art methods on five real-world benchmarks. Our code is available at https://github.com/Jhryu30/AnomalyBERT.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Robust and Explainable Detector of Time Series Anomaly via Augmenting Multiclass Pseudo-Anomalies

    cs.LG 2025-05 conditional novelty 6.0 of 10

    RedLamp detects time series anomalies by training a multiclass classifier on 11 types of augmented pseudo-anomalies, combining its predictions with reconstruction error, and using soft labels to resist contamination.

  2. Mamba Adaptive Anomaly Transformer with association discrepancy for time series

    cs.LG 2025-02 conditional novelty 5.0 of 10

    MAAT integrates sparse attention and a Mamba state-space block into the Anomaly Transformer, reporting small F1 improvements over Anomaly Transformer and DCdetector on seven datasets.

  3. Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs

    cs.LG 2026-07 conditional novelty 4.0 of 10

    A vanilla and a linear-attention Transformer were synthesized on a PYNQ-Z2 FPGA, achieving 37μs and 30μs inference latency, but the accuracy evaluation is weakened by train/validation reporting and a contradicted 'out...

  4. Attention in Motion: Secure Platooning via Transformer-based Misbehavior Detection

    cs.CR 2025-12 conditional novelty 4.0 of 10

    A transformer with global positional offsets and a false-positive-penalizing loss detects kinematic falsification attacks in simulated platoons at 0.13-0.8 ms inference.

  5. CAPMix: Robust KPI Anomaly Detection for AIOps in Noisy and Dynamic Environments

    cs.LG 2025-09 conditional novelty 4.0 of 10

    CAPMix combines CutAddPaste anomaly injection, DTW-based label revision, and dual-space mixup to improve time-series anomaly detection, reporting gains over prior methods on five benchmarks.

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