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Paper Citation Record · LEDGER

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction

As of 14 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2412.19286.

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

pith.paper-citation-record.v1
2412.19286 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T00:48:13.182347Z

measured 28 of 28 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

28 of 28 outbound references displayed

  • verified exact1
  • verified fuzzy3
  • unresolved24
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d28b2026-7332-4007-84d7-d11d7465795b · outbound

This paper cites GPT-4 Technical Report.

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction GPT-4 Technical Report

Reference 1

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-11T00:48:13.052584Z digest=sha256:675a5a4384a4b9b3f55bb1371f15fbc803f613c8ade4b4eb93833b0332b7073f

Observation da9b62c1-c386-4979-93ca-4dc3ae36d0c4 · outbound

This paper cites C.; and Aggarwal, C.

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction C.; and Aggarwal, C

Reference 2

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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.

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Observation f0f88828-363b-4774-9453-a92111c8d259 · outbound

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

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction Chronos: Learning the Language of Time Series

Reference 3

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source=arxiv_source observed=2026-08-11T00:48:13.063383Z digest=sha256:562f648f5e82ecc3d57d511a72e11569fec4e26f772abb6c47522d33cfea11b6

Observation f76f6528-4a0d-4715-bb5d-31d3bd42465f · outbound

This paper cites Invariant Risk Minimization.

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction Invariant Risk Minimization

Reference 4

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source=arxiv_source observed=2026-08-11T00:48:13.068787Z digest=sha256:19281094d51a5f0f067a432a2c9e479d7efcf230b5bcd79db0fa0562f3816fe6

Observation 78640a36-e898-4c1f-8801-3ca7354b16fb · outbound

This paper cites an unresolved cited work.

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction Unresolved cited work

Reference 5

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source=arxiv_source observed=2026-08-11T00:48:13.073961Z digest=sha256:873afdd24ce2d386757b61e113805f1c71a4f073ca2b3f90c44a5dc6b0a81da5

Observation 54aae287-b25d-45dc-a591-5dd81a59492a · outbound

This paper cites S.; Mohapatra, C.; Naidu, S.

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction S.; Mohapatra, C.; Naidu, S

Reference 6

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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.

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Observation 384e4d26-321d-411a-bae7-ed9d10a51772 · outbound

This paper cites TimeGPT-1.

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction TimeGPT-1

Reference 7

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Source-reported events for the cited work

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Observation 35bc8143-36a4-4b9d-8520-24da889af282 · outbound

This paper cites an unresolved cited work.

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction Unresolved cited work

Reference 8

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

source=arxiv_source observed=2026-08-11T00:48:13.088928Z digest=sha256:3f1f980a28c741a77e5068616f4c7dc2aa0cd5d7ac5017474284bc0d59328a0e

Observation e6a3744c-bc54-4219-a51e-cf59de4245ff · outbound

This paper cites Analog and Multi-modal Manufacturing Datasets Acquired on the Future Factories Platform.

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction Analog and Multi-modal Manufacturing Datasets Acquired on the Future Factories Platform

Reference 9

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source=arxiv_source observed=2026-08-11T00:48:13.093212Z digest=sha256:b5f5f3e2a0ebbbe78b9de193abef647cc822f6155e2c1752d0305cab67b92e93

Observation 276df67d-b588-41d5-93a0-594fb174e535 · outbound

This paper cites an unresolved cited work.

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction Unresolved cited work

Reference 10

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source=arxiv_source observed=2026-08-11T00:48:13.097820Z digest=sha256:bfde05773b502d78aaaec05d81531e1ffb9432f06291f90f61d3f8ac6d7a8a4f

Observation 3694ed62-8db7-4c05-9a20-4d036379875b · outbound

This paper cites Time-LLM: Time Series Forecasting by Reprogramming Large Language Models.

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

Reference 11

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Observation d4623174-bc93-4038-9775-80507ece46ea · outbound

This paper cites an unresolved cited work.

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction Unresolved cited work

Reference 12

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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.

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Observation 58a642cd-800f-4ce1-bb76-288f3b125ebf · outbound

This paper cites an unresolved cited work.

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction Unresolved cited work

Reference 13

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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.

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Observation 6e1a511d-ca29-4855-a10f-a8fa0d858613 · outbound

This paper cites an unresolved cited work.

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction Unresolved cited work

Reference 14

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source=arxiv_source observed=2026-08-11T00:48:13.117433Z digest=sha256:ed9dfe59d7bb5219701332a1d9e3a2a343a348aba311003b935d6653eeef9f68

Observation ce31a6c0-0424-4fd7-a0c1-09de508b8684 · outbound

This paper cites Large Language Models are Few-Shot Health Learners.

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction Large Language Models are Few-Shot Health Learners

Reference 15

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source=arxiv_source observed=2026-08-11T00:48:13.121910Z digest=sha256:ea38462b71f99dce2fa90d0d25fd6ad61c3da50bd4cde3ecd09df6f389e314a1

Observation 0a05e938-ea70-4d1f-96f0-b39f1c39957d · outbound

This paper cites Dataset: Rare Event Classification in Multivariate Time Series.

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction Dataset: Rare Event Classification in Multivariate Time Series

Reference 16

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local_arxiv, observed 2026-08-11T00:48:13.394313Z

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.

source=arxiv_source observed=2026-08-11T00:48:13.127238Z digest=sha256:cb269255f2b79a13f67e2038f4be76e53a7bb1260655f12ae253f9e39d9557fb

Observation 54c625b9-8302-4a81-b37f-65a53887d44f · outbound

This paper cites an unresolved cited work.

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction Unresolved cited work

Reference 17

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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.

source=arxiv_source observed=2026-08-11T00:48:13.132830Z digest=sha256:6ca7b4b577ecc67512b83406c13207a3853dc3db9027b78e3bbc72750782015f

Observation a623e0d6-c001-49e4-aa0f-5e8e4a57b0bc · outbound

This paper cites Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts.

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 18

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source=arxiv_source observed=2026-08-11T00:48:13.137812Z digest=sha256:e75d33320948f35561d2b397808216699c60f050b0497c9aa037fcfe54f4721c

Observation 16fa6b95-838f-4d03-b549-08488dc9ae23 · outbound

This paper cites E.; Zi, Y.; Mittal, P.; Narayanan, V.; Harik, R.; and Sheth, A.

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction E.; Zi, Y.; Mittal, P.; Narayanan, V.; Harik, R.; and Sheth, A

Reference 19

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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.

source=arxiv_source observed=2026-08-11T00:48:13.142251Z digest=sha256:dbfba335458c938bc9c7e15ccfe7f6d57f9d623441539a5bcef24a44a6fd640d

Observation 9626f0ea-338f-430d-962e-7db9d4c4dcfc · outbound

This paper cites an unresolved cited work.

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction Unresolved cited work

Reference 20

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source=arxiv_source observed=2026-08-11T00:48:13.146589Z digest=sha256:c72da5aef20e9c9601b95401b198423d2fab8f42db827cb5fc80aeb03d56982a

Observation 1a66df2a-d9cc-4e8f-8241-08d0fb14427b · outbound

This paper cites Unified Training of Universal Time Series Forecasting Transformers.

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction Unified Training of Universal Time Series Forecasting Transformers

Reference 21

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source=arxiv_source observed=2026-08-11T00:48:13.150647Z digest=sha256:3a0a24fa33c1fe41882d4f3e4dd1c96eed9b1a6007db24d50576a2fa194f183a

Observation d2db6246-3c6a-430f-b4a1-bb02536216ff · outbound

This paper cites TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis.

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis

Reference 22

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source=arxiv_source observed=2026-08-11T00:48:13.155335Z digest=sha256:55565d54594b870700fae54a978b27fe78d261bfbf30c4bb7757d551666c9710

Observation eb8ba090-c85d-444d-91c6-b131e6082e13 · outbound

This paper cites an unresolved cited work.

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction Unresolved cited work

Reference 23

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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.

source=arxiv_source observed=2026-08-11T00:48:13.159587Z digest=sha256:8161803044a01e56554edc03b19005aa7bb15eb505859eec0cd41fbbd02e06a9

Observation 4737d6ec-4418-40f8-9125-b6ca54ccfca8 · outbound

This paper cites Large Language Models for Time Series: A Survey.

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction Large Language Models for Time Series: A Survey

Reference 24

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source=arxiv_source observed=2026-08-11T00:48:13.163545Z digest=sha256:2e3d4892593d0beb248d3e9d0703174b3df8d9bf5b451387a9bd610242c512f7

Observation d12777c1-6101-4471-bfef-bae6d52af6fc · outbound

This paper cites Large Language Models for Spatial Trajectory Patterns Mining.

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction Large Language Models for Spatial Trajectory Patterns Mining

Reference 25

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source=arxiv_source observed=2026-08-11T00:48:13.168574Z digest=sha256:180838a87c2de4f581d20992e67fa1abd3e5e528ddb04851c58fb89c06027314

Observation 78ae0ec1-1b75-465c-8dc9-998185f849ff · outbound

This paper cites an unresolved cited work.

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction Unresolved cited work

Reference 26

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Observation 4df3d8b2-11bd-47c8-92c3-b3fae68fae32 · outbound

This paper cites , " * write output.state after.block = add.period write newline.

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction , " * write output.state after.block = add.period write newline

Reference 27

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Observation caa15d71-b0aa-4872-82ce-a3456b6f661b · outbound

This paper cites write newline.

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction write newline

Reference 28

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Pith citing papers

No inbound Pith citation observations are available.