Paper Citation Record · LEDGER

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection

As of 14 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2606.01300.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2606.01300 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-28T17:15:38.257093Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

42 of 42 outbound references displayed

verified exact2
verified fuzzy0
unresolved36
parse uncertain0
malformed identifier0
metadata mismatch4

External citation measurements

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Outbound references

Observation f2297d68-66e7-4530-bb4b-1e0decec8e79 · outbound

This paper cites Anomaly Dtection: A Survey,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection Anomaly Dtection: A Survey,

Reference 1

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Observation db81a94d-3fb2-40d6-8ea0-d59d8e4b2f28 · outbound

This paper cites A Comprehensive Survey on Deep Learning-based Predictive Maintenance,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection A Comprehensive Survey on Deep Learning-based Predictive Maintenance,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

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Observation 35d923bf-0548-43d3-8428-c42ad3ddd8ce · outbound

This paper cites GRU-Based Interpretable Multivariate Time Series Anomaly Detection in Industrial Control System,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection GRU-Based Interpretable Multivariate Time Series Anomaly Detection in Industrial Control System,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

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Observation 6365b600-bb94-48b5-9d35-92ed9ffa145b · outbound

This paper cites & Yang, X.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection & Yang, X

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T21:16:14.095029Z

Source-reported events for the cited work

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Observation 2dd8a29b-49d1-4d04-958d-c577c1df40c0 · outbound

This paper cites DINOv3.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection DINOv3

Reference 5

Resolution: metadata mismatch; local_arxiv, observed 2026-07-01T21:16:14.101956Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1b85f5b3-f773-478a-8bc5-c79ad811358e · outbound

This paper cites AnomalyDINO: Boosting Patch-based Few-Shot Anomaly Detection with DINOv2,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection AnomalyDINO: Boosting Patch-based Few-Shot Anomaly Detection with DINOv2,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

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Observation 98caacf7-f460-4593-991a-3c5ad31579c7 · outbound

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

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection Comparative Analysis of ImageNet Pre-Trained Deep Learning Models and DINOv2 in Medical Imaging Classification,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d614e76-7f64-45af-979b-4c4fc9816d6e · outbound

This paper cites Deep Reinforcement Learning for a Self-Driving Vehicle Operating Solely on Visual Information,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection Deep Reinforcement Learning for a Self-Driving Vehicle Operating Solely on Visual Information,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

Source-reported events for the cited work

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Observation 9a55c10e-d5bf-4b3f-a65a-ed10436a9212 · outbound

This paper cites TimeGPT-1.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection TimeGPT-1

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-01T21:16:14.104647Z

Source-reported events for the cited work

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Observation 0da4ef15-00a3-4e0e-85aa-1928d21d0841 · outbound

This paper cites Chronos: Learning the Language of Time Series,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection Chronos: Learning the Language of Time Series,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

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Observation 98f12460-db5a-4c00-a476-30345629a915 · outbound

This paper cites arXiv preprint arXiv:2405.02358 , year =.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection arXiv preprint arXiv:2405.02358 , year =

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T21:16:14.092449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f4dbd300-4a60-447b-affe-ae1ee1146e45 · outbound

This paper cites Are Time Series Foundation Models Ready to Revolutionize Predictive Building Analytics?.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection Are Time Series Foundation Models Ready to Revolutionize Predictive Building Analytics?

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

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Unavailable: canonical work link unavailable.

Observation 56f67a54-3086-4602-9dec-bd840c5332a5 · outbound

This paper cites AI-enabled Predictive Maintenance of Wind Generators,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection AI-enabled Predictive Maintenance of Wind Generators,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

Source-reported events for the cited work

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Observation dbdf24c0-48b0-4f6b-abad-779e29810c82 · outbound

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

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

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Unavailable: canonical work link unavailable.

Observation 58960304-cb77-44b7-9823-b248408f3f51 · outbound

This paper cites Real-time phonocardiogram anomaly detection by adaptive 1D Convolutional Neural Networks,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection Real-time phonocardiogram anomaly detection by adaptive 1D Convolutional Neural Networks,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ec40d1f-203c-4576-8a5a-3c424d258e75 · outbound

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

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection Anomaly Detection in Quasi-Periodic Time Series Based on Automatic Data Segmentation and Attentional LSTM-CNN,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c654e60-c6d2-4d4b-a8a6-ffc0f26877df · outbound

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

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection Time Series Anomaly Detection for Cyber-physical Systems via Neural System Identification and Bayesian Filtering,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2647199-4a0b-48c2-871b-aa8fcf00fe91 · outbound

This paper cites Design and Development of RNN Anomaly Detection Model for IoT Networks,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection Design and Development of RNN Anomaly Detection Model for IoT Networks,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9df210c-dcdb-4815-8ce3-e4894d65debd · outbound

This paper cites LSTM-Markov based efficient anomaly detection algorithm for IoT environment,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection LSTM-Markov based efficient anomaly detection algorithm for IoT environment,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17e08550-07da-4c8a-9f2a-f2c160b549e3 · outbound

This paper cites Anomaly detection in ECG time signals via deep long short-term memory networks,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection Anomaly detection in ECG time signals via deep long short-term memory networks,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

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Observation 54c82539-2d11-4f46-8587-d62b3de4ff9f · outbound

This paper cites CNN and GRU combination scheme for Bearing Anomaly Detection in Rotating Machinery Health Monitoring,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection CNN and GRU combination scheme for Bearing Anomaly Detection in Rotating Machinery Health Monitoring,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

Source-reported events for the cited work

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Observation 4803a1de-06e9-4a53-b898-32c001e3e2e9 · outbound

This paper cites Adversarially Learned Anomaly Detection,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection Adversarially Learned Anomaly Detection,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

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Observation 58acb049-ffa7-4b7e-87db-67e0067c2603 · outbound

This paper cites f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

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Unavailable: canonical work link unavailable.

Observation 10732658-d706-42ca-9b40-a3cf77a94173 · outbound

This paper cites BeatGAN: Anomalous Rhythm Detection using Adver- sarially Generated Time Series,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection BeatGAN: Anomalous Rhythm Detection using Adver- sarially Generated Time Series,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

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Observation 6e9f1ca4-8fb1-45d7-bb97-5bd2cacb1596 · outbound

This paper cites USAD: UnSupervised Anomaly Detection on Mul- tivariate Time Series,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection USAD: UnSupervised Anomaly Detection on Mul- tivariate Time Series,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 804c726e-c659-43e8-b625-8752c5c67c48 · outbound

This paper cites A comprehensive review on GANs for time-series signals,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection A comprehensive review on GANs for time-series signals,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

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Unavailable: canonical work link unavailable.

Observation 3093c5ca-3145-48cb-b766-9dfc3f2ccf77 · outbound

This paper cites Improving Language Understanding by Generative Pre-Training,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection Improving Language Understanding by Generative Pre-Training,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

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Unavailable: canonical work link unavailable.

Observation 894800a6-477b-481c-b77d-118910690b8e · outbound

This paper cites MOMENT: A Family of Open Time-series Founda- tion Models,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection MOMENT: A Family of Open Time-series Founda- tion Models,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

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Unavailable: canonical work link unavailable.

Observation 3075ef3f-bef4-4051-9251-352d6466f477 · outbound

This paper cites Lag-Llama: Towards Foundation Models for Time Series Forecasting,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection Lag-Llama: Towards Foundation Models for Time Series Forecasting,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

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Unavailable: canonical work link unavailable.

Observation 63cbd556-d1f4-4eea-a74c-6d7479804e17 · outbound

This paper cites KalmanAE: Deep Embedding Optimized Kalman Filter for Time Series Anomaly Detection,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection KalmanAE: Deep Embedding Optimized Kalman Filter for Time Series Anomaly Detection,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

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Observation 9c314482-027f-4866-a8e5-4298027f65d3 · outbound

This paper cites The UCR Time Series Archive,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection The UCR Time Series Archive,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f761802-686f-42c0-8ddd-c3d846ac08e1 · outbound

This paper cites Rolling Element Bearing Fault Diagnosis Using Vibration Signals,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection Rolling Element Bearing Fault Diagnosis Using Vibration Signals,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0553e5b7-d8c3-4aeb-aff2-16ba504fc7f2 · outbound

This paper cites The impact of the MIT-BIH Arrhythmia Database,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection The impact of the MIT-BIH Arrhythmia Database,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

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Unavailable: canonical work link unavailable.

Observation b309640d-0871-4b79-bef2-d7691f95f929 · outbound

This paper cites A Dataset to Support Research in the Design of Secure Water Treatment Systems,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection A Dataset to Support Research in the Design of Secure Water Treatment Systems,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

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Unavailable: canonical work link unavailable.

Observation 61dbc1ae-aa0c-4be2-9d63-545e98f5dfd3 · outbound

This paper cites Subsampling for Efficient and Effective Unsupervised Outlier Detection Ensembles,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection Subsampling for Efficient and Effective Unsupervised Outlier Detection Ensembles,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

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Unavailable: canonical work link unavailable.

Observation 81f54848-424e-4514-8066-ebb1887462fa · outbound

This paper cites Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

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Observation 5806aaaa-1167-4f34-9e3f-bb0b48c77dc2 · outbound

This paper cites Deep One-Class Classification,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection Deep One-Class Classification,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

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Observation 1152245e-b588-4b5b-8a39-a92c435e7b5f · outbound

This paper cites Classification-Based Anomaly Detection for General Data.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection Classification-Based Anomaly Detection for General Data

Reference 38

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T21:16:14.104923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1997be2b-59b4-4a96-ac3e-030d955ca20b · outbound

This paper cites Incorporating Transformer and LSTM to Kalman Filter with EM algorithm for state estimation.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection Incorporating Transformer and LSTM to Kalman Filter with EM algorithm for state estimation

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-07-01T21:16:14.099384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 163940d7-01bb-48d2-856e-9226caf6db66 · outbound

This paper cites Learning Graph Structures With Transformer for Multivariate Time-Series Anomaly Detection in IoT,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection Learning Graph Structures With Transformer for Multivariate Time-Series Anomaly Detection in IoT,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

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Observation d5a06361-7d29-4070-8cea-1f3408f03eda · outbound

This paper cites ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

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Observation d4781b2a-d38e-4033-b77d-17a096a084c7 · outbound

This paper cites GAIA: A Comprehensive Pipeline for Enabling Aircraft Digital Twin Creation,.

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection GAIA: A Comprehensive Pipeline for Enabling Aircraft Digital Twin Creation,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-06-28T17:15:38.257093Z

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