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arxiv: 2606.01300 · v1 · pith:USBOLEIK · submitted 2026-05-31 · cs.LG · cs.AI

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection

Uzair Khan , Luigi Capogrosso , Francesco Biondani , Michele Magno , Franco Fummi , Francesco Setti , Marco Cristani This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel 2026-06-28 17:13 UTCgrok-4.3pith:USBOLEIKrecord.jsonopen to challenge →

classification cs.LG cs.AI
keywords time series anomaly detectionfoundation modelszero-shot embeddingsBiLSTMmulti-head attentiontemporal refinementgeneralization across domains
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The pith

ChronosAD extracts zero-shot embeddings from a time series foundation model and refines them with a BiLSTM plus multi-head attention block to detect anomalies more accurately across domains.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper aims to overcome poor generalization in time series anomaly detection when anomalies are subtle or context-dependent. It establishes a two-stage approach: a foundation model produces embeddings without task-specific training, then a Temporal Block refines those embeddings to capture dependencies and salient patterns. This pipeline requires little tuning yet delivers higher detection scores on benchmarks spanning industrial, medical, cyber-physical, and automotive data. A reader would care because it points to a way of reusing large pre-trained models instead of building or retraining separate detectors for each new setting. If the claim holds, anomaly detection systems could be deployed faster on varied real-world streams.

Core claim

ChronosAD uses a time series foundation model to generate zero-shot embeddings for each input series and passes them through a Temporal Block of Bidirectional LSTM layers followed by multi-head attention; the refined representations are used for anomaly scoring, yielding an average 4.72 percent gain in AUC and 6.60 percent gain in AP over prior methods on eleven benchmarks while maintaining performance across four distinct application domains with minimal hyperparameter adjustment.

What carries the argument

Two-stage pipeline that first obtains zero-shot embeddings from a time series foundation model and then refines them inside a Temporal Block composed of BiLSTM and multi-head attention.

If this is right

  • Average performance rises by 4.72 percent AUC and 6.60 percent AP across the tested benchmarks.
  • The method needs only minimal task-specific tuning to reach those scores.
  • Robust results hold for industrial, medical, cyber-physical, and automotive time series.
  • The architecture handles both subtle and context-dependent anomalies without domain-specific redesign.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Other time series tasks such as forecasting or classification could reuse the same zero-shot embeddings plus a lightweight refinement block.
  • New datasets arriving after deployment would require only the refinement block to be retrained rather than the entire model.
  • If the foundation model is updated, the anomaly detector could be refreshed by swapping the embedding source while keeping the Temporal Block fixed.

Load-bearing premise

The foundation model's zero-shot embeddings already contain the features needed to separate normal from anomalous behavior, and the added Temporal Block improves them consistently without introducing overfitting or needing heavy per-domain tuning.

What would settle it

Evaluating the same ChronosAD pipeline on a fresh collection of eleven time series benchmarks drawn from the same four domains and finding that AUC or AP does not exceed the best prior methods, or that extensive tuning is required to reach those numbers.

Figures

Figures reproduced from arXiv: 2606.01300 by Francesco Biondani, Francesco Setti, Franco Fummi, Luigi Capogrosso, Marco Cristani, Michele Magno, Uzair Khan.

Figure 1
Figure 1. Figure 1: The ChronosAD architecture. To address this limitation, ChronosAD adopts a multi-path architecture with channel fusion, enabling the model to aggre￾gate information across multiple data streams and to model cross-channel interactions. III. METHOD In this section, we introduce ChronosAD. The overall ar￾chitecture is illustrated in [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The custom Temporal Block used in ChronosAD to [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The visualization shows the latent space of the model using t-SNE for diverse datasets. The [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
read the original abstract

Time series anomaly detection is a crucial task in various domains, including finance, healthcare, and industry. However, existing methods often struggle to generalize across different datasets, especially when anomalies are subtle or context-dependent. To solve this issue, we introduce ChronosAD, a novel architecture for anomaly detection that uses a time series foundation model as a feature extractor. Specifically, it employs a two-stage pipeline: first, it uses the foundation model to extract embeddings for each time series in a zero-shot manner. Then, a custom-developed Temporal Block, composed of Bidirectional Long Short-Term Memory (BiLSTM) and Multi-Head Attention, refines these embeddings to capture temporal dependencies and highlight salient patterns. Unlike previous approaches, our model requires minimal task-specific tuning and demonstrates robust generalization across a wide range of domains, including industrial, medical, cyber-physical, and automotive systems. Extensive experiments on 11 benchmarks show that ChronosAD outperforms existing methods by 4.72% in AUC and 6.60% in AP on average. The source code is available at https://github.com/intelligolabs/ChronosAD.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The manuscript proposes ChronosAD, a two-stage anomaly detection architecture that extracts zero-shot embeddings from the Chronos time series foundation model and refines them with a trainable Temporal Block (BiLSTM + multi-head attention). It reports average improvements of 4.72% AUC and 6.60% AP over existing methods across 11 benchmarks spanning industrial, medical, cyber-physical, and automotive domains, while requiring minimal task-specific tuning.

Significance. If the performance gains can be attributed to anomaly-relevant structure already present in the Chronos embeddings rather than to the capacity of the trainable Temporal Block, the work would provide evidence that time-series foundation models can serve as effective feature extractors for anomaly detection with limited domain adaptation. The reported cross-domain generalization would be a useful empirical result, but the current experimental design does not isolate this contribution.

major comments (2)
  1. [Experiments / Results] The experimental evaluation lacks any ablation that feeds the identical Temporal Block architecture non-Chronos features (random vectors, a non-pretrained encoder, or statistical featurizers) and measures the resulting AUC/AP. Without this control, the central claim that the zero-shot Chronos embeddings "already encode the features needed to distinguish anomalies" cannot be distinguished from the possibility that the trainable block alone produces the observed gains on each benchmark.
  2. [Abstract and Experimental Setup] The abstract and experimental description supply no information on baseline implementations, statistical significance testing, train/test splits, or whether the Chronos foundation model was frozen. These omissions prevent assessment of whether the reported 4.72% AUC / 6.60% AP margins are robust or reproducible.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive comments. Below we respond point-by-point to the major concerns and indicate the revisions we will make.

read point-by-point responses

  1. Referee: [Experiments / Results] The experimental evaluation lacks any ablation that feeds the identical Temporal Block architecture non-Chronos features (random vectors, a non-pretrained encoder, or statistical featurizers) and measures the resulting AUC/AP. Without this control, the central claim that the zero-shot Chronos embeddings "already encode the features needed to distinguish anomalies" cannot be distinguished from the possibility that the trainable block alone produces the observed gains on each benchmark.

    Authors: We agree that the current experiments do not isolate the contribution of the Chronos embeddings from the capacity of the Temporal Block. We will add the requested ablation studies (random vectors, non-pretrained encoder, and statistical featurizers as input to the identical Temporal Block) and report the resulting AUC and AP on the same 11 benchmarks. These results will be included in the revised manuscript and will allow readers to assess whether the gains are attributable to the foundation-model embeddings. revision: yes

  2. Referee: [Abstract and Experimental Setup] The abstract and experimental description supply no information on baseline implementations, statistical significance testing, train/test splits, or whether the Chronos foundation model was frozen. These omissions prevent assessment of whether the reported 4.72% AUC / 6.60% AP margins are robust or reproducible.

    Authors: We will revise both the abstract and the experimental section to supply the missing details: (i) the exact implementations and hyper-parameters of all baselines, (ii) results of statistical significance tests (paired t-tests across the 11 datasets), (iii) the precise train/test split protocol used for each benchmark, and (iv) explicit confirmation that the Chronos foundation model remained frozen during zero-shot embedding extraction. These clarifications will be added without altering the reported performance numbers. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark evaluation

full rationale

The paper reports measured AUC and AP improvements from training and testing the ChronosAD pipeline (zero-shot Chronos embeddings + trainable BiLSTM+attention block) on 11 public datasets. No equations, derivations, or self-citations reduce the reported performance numbers to quantities defined inside the paper itself. The central claims are direct experimental outcomes on standard splits; the architecture description and zero-shot embedding step do not create self-definitional or fitted-input circularity. This is a standard empirical ML paper whose results stand or fall on the experiments, not on any internal reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review is based on abstract only; no explicit free parameters, axioms, or invented entities are stated. The method re-uses a pre-trained foundation model and standard neural components (BiLSTM, attention) whose hyperparameters are not enumerated here.

pith-pipeline@v0.9.1-grok · 5747 in / 1176 out tokens · 26860 ms · 2026-06-28T17:13:35.393018+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

42 extracted references · 6 canonical work pages · 1 internal anchor

  1. [1]

    Anomaly Dtection: A Survey,

    V . Chandolaet al., “Anomaly Dtection: A Survey,”ACM Computing Surveys, vol. 41, no. 3, pp. 1–58, 2009

    2009

  2. [2]

    A Comprehensive Survey on Deep Learning-based Predictive Maintenance,

    U. Khanet al., “A Comprehensive Survey on Deep Learning-based Predictive Maintenance,”ACM Transactions on Embedded Computing Systems, vol. 25, no. 2, pp. 1–43, 2026. Figure 3: The visualization shows the latent space of the model using t-SNE for diverse datasets. The red shows the anomalies while the green shows the normal time series

    2026

  3. [3]

    GRU-Based Interpretable Multivariate Time Series Anomaly Detection in Industrial Control System,

    C. Tanget al., “GRU-Based Interpretable Multivariate Time Series Anomaly Detection in Industrial Control System,”Computers & Se- curity, vol. 127, p. 103094, 2023

    2023

  4. [4]

    arXiv preprint arXiv:2509.02379 , year=

    Y . Liet al., “MedDINOv3: How to adapt vision foundation models for medical image segmentation?”arXiv preprint arXiv:2509.02379, 2025

    work page arXiv 2025

  5. [5]

    DINOv3

    O. Siméoniet al., “DINOv3,”arXiv preprint arXiv:2508.10104, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  6. [6]

    AnomalyDINO: Boosting Patch-based Few-Shot Anomaly Detection with DINOv2,

    S. Dammet al., “AnomalyDINO: Boosting Patch-based Few-Shot Anomaly Detection with DINOv2,” inWinter Conference on Applica- tions of Computer Vision (WACV), 2025

    2025

  7. [7]

    Comparative Analysis of ImageNet Pre-Trained Deep Learning Models and DINOv2 in Medical Imaging Classification,

    Y . Huanget al., “Comparative Analysis of ImageNet Pre-Trained Deep Learning Models and DINOv2 in Medical Imaging Classification,” in48th Annual Computers, Software, and Applications Conference (COMPSAC), 2024

    2024

  8. [8]

    Deep Reinforcement Learning for a Self-Driving Vehicle Operating Solely on Visual Information,

    R. Audinyset al., “Deep Reinforcement Learning for a Self-Driving Vehicle Operating Solely on Visual Information,”Electronics, vol. 14, no. 5, p. 825, 2025

    2025

  9. [9]

    TimeGPT-1

    A. Garzaet al., “TimeGPT-1,”arXiv preprint arXiv:2310.03589, 2023

    work page Pith review arXiv 2023

  10. [10]

    Chronos: Learning the Language of Time Series,

    A. F. Ansariet al., “Chronos: Learning the Language of Time Series,” Transactions on Machine Learning Research, 2024

    2024

  11. [11]

    arXiv preprint arXiv:2405.02358 , year =

    J. a. o. Ye, “Empowering Time Series Analysis with Foundation Models: A Comprehensive Survey,”arXiv preprint arXiv:2405.02358, 2024

    work page arXiv 2024

  12. [12]

    Are Time Series Foundation Models Ready to Revolutionize Predictive Building Analytics?

    O. B. Mulayimet al., “Are Time Series Foundation Models Ready to Revolutionize Predictive Building Analytics?” in11th International Conference on Systems for Energy-Efficient Buildings, Cities, and Trans- portation, 2024

    2024

  13. [13]

    AI-enabled Predictive Maintenance of Wind Generators,

    E. Mammadovet al., “AI-enabled Predictive Maintenance of Wind Generators,” inPES Innovative Smart Grid Technologies Europe (ISGT Europe), 2021

    2021

  14. [14]

    Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study,

    M. Canizoet al., “Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study,”Neurocomputing, vol. 363, pp. 246– 260, 2019

    2019

  15. [15]

    Real-time phonocardiogram anomaly detection by adaptive 1D Convolutional Neural Networks,

    S. Kiranyazet al., “Real-time phonocardiogram anomaly detection by adaptive 1D Convolutional Neural Networks,”Neurocomputing, vol. 411, pp. 291–301, 2020

    2020

  16. [16]

    Anomaly Detection in Quasi-Periodic Time Series Based on Automatic Data Segmentation and Attentional LSTM-CNN,

    F. Liuet al., “Anomaly Detection in Quasi-Periodic Time Series Based on Automatic Data Segmentation and Attentional LSTM-CNN,”IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 6, pp. 2626–2640, 2022

    2022

  17. [17]

    Time Series Anomaly Detection for Cyber-physical Systems via Neural System Identification and Bayesian Filtering,

    C. Fenget al., “Time Series Anomaly Detection for Cyber-physical Systems via Neural System Identification and Bayesian Filtering,” in 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD), 2021

    2021

  18. [18]

    Design and Development of RNN Anomaly Detection Model for IoT Networks,

    I. Ullahet al., “Design and Development of RNN Anomaly Detection Model for IoT Networks,”IEEE Access, vol. 10, pp. 62 722–62 750, 2022

    2022

  19. [19]

    LSTM-Markov based efficient anomaly detection algorithm for IoT environment,

    S. V .et al., “LSTM-Markov based efficient anomaly detection algorithm for IoT environment,”Applied Soft Computing, vol. 136, p. 110054, 2023

    2023

  20. [20]

    Anomaly detection in ECG time signals via deep long short-term memory networks,

    S. Chauhanet al., “Anomaly detection in ECG time signals via deep long short-term memory networks,” inInternational Conference on Data Science and Advanced Analytics (DSAA), 2015

    2015

  21. [21]

    CNN and GRU combination scheme for Bearing Anomaly Detection in Rotating Machinery Health Monitoring,

    K. Leeet al., “CNN and GRU combination scheme for Bearing Anomaly Detection in Rotating Machinery Health Monitoring,” in1st Interna- tional Conference on Knowledge Innovation and Invention (ICKII), 2018

    2018

  22. [22]

    Adversarially Learned Anomaly Detection,

    H. Zenatiet al., “Adversarially Learned Anomaly Detection,” inInter- national Conference on Data Mining (ICDM), 2018

    2018

  23. [23]

    f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks,

    T. Schleglet al., “f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks,”Medical Image Analysis, vol. 54, pp. 30–44, 2019

    2019

  24. [24]

    BeatGAN: Anomalous Rhythm Detection using Adver- sarially Generated Time Series,

    B. Zhouet al., “BeatGAN: Anomalous Rhythm Detection using Adver- sarially Generated Time Series,” inInternational Joint Conference on Artificial Intelligence (IJCAI), 2019

    2019

  25. [25]

    USAD: UnSupervised Anomaly Detection on Mul- tivariate Time Series,

    J. Audibertet al., “USAD: UnSupervised Anomaly Detection on Mul- tivariate Time Series,” in26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020

    2020

  26. [26]

    A comprehensive review on GANs for time-series signals,

    D. Zhanget al., “A comprehensive review on GANs for time-series signals,”Neural Computing and Applications, vol. 34, no. 5, pp. 3551– 3571, 2022

    2022

  27. [27]

    Improving Language Understanding by Generative Pre-Training,

    A. Radfordet al., “Improving Language Understanding by Generative Pre-Training,” 2018

    2018

  28. [28]

    MOMENT: A Family of Open Time-series Founda- tion Models,

    M. Goswamiet al., “MOMENT: A Family of Open Time-series Founda- tion Models,” inInternational Conference on Machine Learning, 2024

    2024

  29. [29]

    Lag-Llama: Towards Foundation Models for Time Series Forecasting,

    K. Rasulet al., “Lag-Llama: Towards Foundation Models for Time Series Forecasting,” inR0-FoMo: Robustness of Few-shot and Zero-shot Learning in Large Foundation Models, 2023

    2023

  30. [30]

    KalmanAE: Deep Embedding Optimized Kalman Filter for Time Series Anomaly Detection,

    X. Huanget al., “KalmanAE: Deep Embedding Optimized Kalman Filter for Time Series Anomaly Detection,”IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1–11, 2023

    2023

  31. [31]

    The UCR Time Series Archive,

    H. A. Dauet al., “The UCR Time Series Archive,”IEEE/CAA Journal of Automatica Sinica, vol. 6, no. 6, pp. 1293–1305, 2019

    2019

  32. [32]

    Rolling Element Bearing Fault Diagnosis Using Vibration Signals,

    W. A. Smithet al., “Rolling Element Bearing Fault Diagnosis Using Vibration Signals,” Cleveland, OH, 1997, accessed: 2025-07-25

    1997

  33. [33]

    The impact of the MIT-BIH Arrhythmia Database,

    G. Moodyet al., “The impact of the MIT-BIH Arrhythmia Database,” IEEE Engineering in Medicine and Biology Magazine, vol. 20, no. 3, pp. 45–50, 2001

    2001

  34. [34]

    A Dataset to Support Research in the Design of Secure Water Treatment Systems,

    J. Gohet al., “A Dataset to Support Research in the Design of Secure Water Treatment Systems,” in11th International Conference on Critical Information Infrastructures Security, 2017

    2017

  35. [35]

    Subsampling for Efficient and Effective Unsupervised Outlier Detection Ensembles,

    A. Zimeket al., “Subsampling for Efficient and Effective Unsupervised Outlier Detection Ensembles,” in19th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2013

    2013

  36. [36]

    Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery,

    T. Schleglet al., “Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery,” inInformation Processing in Medical Imaging, 2017

    2017

  37. [37]

    Deep One-Class Classification,

    L. Ruffet al., “Deep One-Class Classification,” in35th International Conference on Machine Learning (ICML), 2018

    2018

  38. [38]

    Classification-Based Anomaly Detection for General Data,

    L. Bergmanet al., “Classification-Based Anomaly Detection for General Data,”arXiv preprint arXiv:2005.02359, 2020

    work page arXiv 2005

  39. [39]

    Incorporating Transformer and LSTM to Kalman Filter with EM algorithm for state estimation,

    Z. Shi, “Incorporating Transformer and LSTM to Kalman Filter with EM algorithm for state estimation,”arXiv preprint arXiv:2105.00250, 2021

    work page arXiv 2021

  40. [40]

    Learning Graph Structures With Transformer for Multivariate Time-Series Anomaly Detection in IoT,

    Z. Chenet al., “Learning Graph Structures With Transformer for Multivariate Time-Series Anomaly Detection in IoT,”IEEE Internet of Things Journal, vol. 9, no. 12, pp. 9179–9189, 2022

    2022

  41. [41]

    ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions,

    Z. Liet al., “ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions,”IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 12, pp. 12 181–12 193, 2023

    2023

  42. [42]

    GAIA: A Comprehensive Pipeline for Enabling Aircraft Digital Twin Creation,

    F. Biondaniet al., “GAIA: A Comprehensive Pipeline for Enabling Aircraft Digital Twin Creation,” inIEEE 34th International Symposium on Industrial Electronics (ISIE), 2025

    2025