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Paper Citation Record · LEDGER

Expert-Guided Forecast Editing for Time-Series Foundation Models

As of 8 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2607.19659.

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

pith.paper-citation-record.v1
2607.19659 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T12:08:54.192441Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

30 of 30 outbound references displayed

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  • unresolved30
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  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d009f65d-b309-4c4f-bc48-0b051d77ce72 · outbound

This paper cites Proceedings of the 41st International Conference on Machine Learning , pages=.

Expert-Guided Forecast Editing for Time-Series Foundation Models Proceedings of the 41st International Conference on Machine Learning , pages=

Reference 1

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source=arxiv_source observed=2026-08-01T12:08:49.890834Z digest=sha256:11c803eeaba6098cb92b6085c16e44034a7c0fd44935e1540d59f922a7cbb3e2

Observation 1963c9f2-b9cc-4e8f-9f2a-12920a1edfbd · outbound

This paper cites Proceedings of the IEEE , volume=.

Expert-Guided Forecast Editing for Time-Series Foundation Models Proceedings of the IEEE , volume=

Reference 2

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source=arxiv_source observed=2026-08-01T12:08:49.957931Z digest=sha256:3fd4a26590c7ba0e7c08d6cefaf8efd59f7ea67cdf6bb5c282792059ac57adfd

Observation 45a6bd53-bbc4-4b99-acea-1f2a1d4ef2bb · outbound

This paper cites Advances in neural information processing systems , volume=.

Expert-Guided Forecast Editing for Time-Series Foundation Models Advances in neural information processing systems , volume=

Reference 3

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source=arxiv_source observed=2026-08-01T12:08:50.074785Z digest=sha256:9e3504a49de97659ec2bcc9e8a909949f1798e5313f84f38d9a568f7c5dcdc28

Observation 428693c5-929a-419b-a51b-5649b2fa2fb1 · outbound

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

Expert-Guided Forecast Editing for Time-Series Foundation Models Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

Reference 4

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source=arxiv_source observed=2026-08-01T12:08:50.147491Z digest=sha256:6e9554ab9e926dd9c895107fb1550c2eef8e8d4c04e315890e0408ca476ed461

Observation 3e370c1e-8870-4893-9c32-c1d953d7b842 · outbound

This paper cites Advances in neural information processing systems , volume=.

Expert-Guided Forecast Editing for Time-Series Foundation Models Advances in neural information processing systems , volume=

Reference 5

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source=arxiv_source observed=2026-08-01T12:08:50.276361Z digest=sha256:609f10c1a4c22a95d73ee8a8a4fb619be7602e5e01e810ff8b6b21d242173421

Observation 6e0ae222-2559-4641-ada3-ef20475ab5b6 · outbound

This paper cites an unresolved cited work.

Expert-Guided Forecast Editing for Time-Series Foundation Models Unresolved cited work

Reference 6

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Observation 2b36bb34-9776-4a97-ac46-f48b570133c9 · outbound

This paper cites Proceedings of the AAAI conference on artificial intelligence , volume=.

Expert-Guided Forecast Editing for Time-Series Foundation Models Proceedings of the AAAI conference on artificial intelligence , volume=

Reference 7

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Observation 25c29446-bb5c-44b8-8086-024ccc7dd17b · outbound

This paper cites and Bergmeir, Christoph , journal=.

Expert-Guided Forecast Editing for Time-Series Foundation Models and Bergmeir, Christoph , journal=

Reference 8

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source=arxiv_source observed=2026-08-01T12:08:50.812386Z digest=sha256:d00a82f9fbd4c66eec175a18829006b1f40a2448cb2b186c14409cd05f6b0c21

Observation 193134db-efe8-4af4-94da-96ca72fcda04 · outbound

This paper cites Lightweight Online Adaption for Time Series Foundation Model Forecasts.

Expert-Guided Forecast Editing for Time-Series Foundation Models Lightweight Online Adaption for Time Series Foundation Model Forecasts

Reference 9

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source=arxiv_source observed=2026-08-01T12:08:50.998382Z digest=sha256:b6de1409d4bce69f3f4334e2a085d32377c5a26be7f5decb6fb62e8e1a00f77f

Observation ff94ebbb-ecf8-46dc-ac12-4c15f3297f65 · outbound

This paper cites Handbook of Statistics , volume=.

Expert-Guided Forecast Editing for Time-Series Foundation Models Handbook of Statistics , volume=

Reference 10

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source=arxiv_source observed=2026-08-01T12:08:51.151712Z digest=sha256:585535dc7ffe709279b2f6f9428d55a838fadf229ea96ea63a29eca1f86ab30d

Observation 4e175739-4e62-42bb-8007-91c456db75c6 · outbound

This paper cites Swarm and Evolutionary Computation , volume=.

Expert-Guided Forecast Editing for Time-Series Foundation Models Swarm and Evolutionary Computation , volume=

Reference 11

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source=arxiv_source observed=2026-08-01T12:08:51.265940Z digest=sha256:419942479bf347e05538717b39f44e4b1ad80ab97ee86013aa9d0dfd2d03ac47

Observation f2b0d55c-8eb2-4102-a10d-187e23dc2ae9 · outbound

This paper cites 2004 , publisher=.

Expert-Guided Forecast Editing for Time-Series Foundation Models 2004 , publisher=

Reference 13

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Observation cabdfde2-80a8-48d2-a370-06472aadb813 · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Expert-Guided Forecast Editing for Time-Series Foundation Models Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 14

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source=arxiv_source observed=2026-08-01T12:08:51.744671Z digest=sha256:cf5aa9630bd829f4050f70fd994633d013e1294ef781078dfa9762194abcbdf4

Observation f51eab7f-4c5c-42aa-be1c-179d8ccb369b · outbound

This paper cites International Journal of forecasting , volume=.

Expert-Guided Forecast Editing for Time-Series Foundation Models International Journal of forecasting , volume=

Reference 15

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source=arxiv_source observed=2026-08-01T12:08:51.900081Z digest=sha256:0e1145803d9addc83707fc2b6f4acf359a92cbda916afef245d0920016ecb9ba

Observation ee6ccd09-b0b3-4c56-a272-29fc09591e55 · outbound

This paper cites International journal of forecasting , volume=.

Expert-Guided Forecast Editing for Time-Series Foundation Models International journal of forecasting , volume=

Reference 16

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source=arxiv_source observed=2026-08-01T12:08:52.016426Z digest=sha256:a2e41293d21941067143ec08cab382dd672828e6fc8a86dac2e8cfb87035586d

Observation 47b43783-ecee-4437-9841-f5885619effe · outbound

This paper cites IEEE Transactions on Intelligent Transportation Systems , year=.

Expert-Guided Forecast Editing for Time-Series Foundation Models IEEE Transactions on Intelligent Transportation Systems , year=

Reference 17

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source=arxiv_source observed=2026-08-01T12:08:52.167921Z digest=sha256:b3e055ebb52ddea5e8d131c5df600c2af8c2c9e1fd880d96a1fd9cc885ae7ea3

Observation 6d205d91-fc8f-44f1-b57e-44d75b6d9f69 · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

Expert-Guided Forecast Editing for Time-Series Foundation Models Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 18

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source=arxiv_source observed=2026-08-01T12:08:52.359690Z digest=sha256:a1f0d9912395d4c1c744cf9724fa249e49412d6eb467f6e82b8e3178806a2c0c

Observation 9b2b8cd5-8e2b-4b0f-98ad-02c4e08c8d5a · outbound

This paper cites The Fourteenth International Conference on Learning Representations , year=.

Expert-Guided Forecast Editing for Time-Series Foundation Models The Fourteenth International Conference on Learning Representations , year=

Reference 19

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source=arxiv_source observed=2026-08-01T12:08:52.513632Z digest=sha256:2bb9fcc265e65278116e78dfdf2cba16c1afae23b8ea27db3f343a5a8402a7ec

Observation ba530d55-8ff3-4db1-a3d8-a2e26ddfe090 · outbound

This paper cites Cross-Entropy Method Variants for Optimization.

Expert-Guided Forecast Editing for Time-Series Foundation Models Cross-Entropy Method Variants for Optimization

Reference 20

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source=arxiv_source observed=2026-08-01T12:08:52.658893Z digest=sha256:9e1b7f1f67ae3bd9c625084ba31f46f6c4c8d65265c676c256526b1dc1992c93

Observation 14e674eb-644b-4d11-8673-9b01837fb32a · outbound

This paper cites and Carpov, Dmitri and Chapados, Nicolas and Bengio, Yoshua , booktitle=.

Expert-Guided Forecast Editing for Time-Series Foundation Models and Carpov, Dmitri and Chapados, Nicolas and Bengio, Yoshua , booktitle=

Reference 21

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source=arxiv_source observed=2026-08-01T12:08:52.755717Z digest=sha256:3b63e1c230731e99fe65a5f1e787bf00fb8fc252fb4fe639c7cb610dec4e02a4

Observation a91a78d7-76aa-438c-9b92-6cd8cfdd9f6e · outbound

This paper cites Forty-first International Conference on Machine Learning , year=.

Expert-Guided Forecast Editing for Time-Series Foundation Models Forty-first International Conference on Machine Learning , year=

Reference 22

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source=arxiv_source observed=2026-08-01T12:08:52.859055Z digest=sha256:eef17369eeae27f5f33de59f49f97a8861988fde1e0b47e7fc52d99cb661505e

Observation 981378c5-40d0-4ecb-a26a-8267dc7792f1 · outbound

This paper cites Chronos-2: From Univariate to Universal Forecasting.

Expert-Guided Forecast Editing for Time-Series Foundation Models Chronos-2: From Univariate to Universal Forecasting

Reference 23

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source=arxiv_source observed=2026-08-01T12:08:53.025898Z digest=sha256:81b726e1c09f2879a5c78eb03ee9935a2c77147ff36b5accf12050dfffbcce22

Observation 1629393c-4877-4197-a0df-fe9fdc1cc5b2 · outbound

This paper cites Transactions on Machine Learning Research , volume=.

Expert-Guided Forecast Editing for Time-Series Foundation Models Transactions on Machine Learning Research , volume=

Reference 24

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source=arxiv_source observed=2026-08-01T12:08:53.101599Z digest=sha256:f8c90828eac1173351227c4713f0faa98b6e84837186a8ebf5eee481e3a8f27e

Observation 954109f1-ce6c-477c-8f73-0d3a71418a5c · outbound

This paper cites NeurIPS Workshop on Time Series in the Age of Large Models , year=.

Expert-Guided Forecast Editing for Time-Series Foundation Models NeurIPS Workshop on Time Series in the Age of Large Models , year=

Reference 25

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Observation 891f97af-eb6e-4849-b0d7-e5c6ac9f42f3 · outbound

This paper cites Maddix and Syama Rangapuram and David Salinas and Jasper Schulz and Lorenzo Stella and Ali Caner Türkmen and Yuyang Wang , title =.

Expert-Guided Forecast Editing for Time-Series Foundation Models Maddix and Syama Rangapuram and David Salinas and Jasper Schulz and Lorenzo Stella and Ali Caner Türkmen and Yuyang Wang , title =

Reference 26

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source=arxiv_source observed=2026-08-01T12:08:53.392990Z digest=sha256:a38ccf92df2aefa466e28e3efbad914de404ead87f19389e0ac3722d08e38ae0

Observation a905b1db-62f4-4133-a78e-a954c3398cb2 · outbound

This paper cites 2025 IEEE International Conference on Data Mining (ICDM) , pages=.

Expert-Guided Forecast Editing for Time-Series Foundation Models 2025 IEEE International Conference on Data Mining (ICDM) , pages=

Reference 27

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source=arxiv_source observed=2026-08-01T12:08:53.614792Z digest=sha256:9696f54333e633810ca67b4fe50b81fe2a32f8e9e8d43a83c084923bf00cac1c

Observation 870bf5f8-2daf-4297-b035-606958cde052 · outbound

This paper cites International Conference on Machine Learning , year=.

Expert-Guided Forecast Editing for Time-Series Foundation Models International Conference on Machine Learning , year=

Reference 28

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source=arxiv_source observed=2026-08-01T12:08:53.774843Z digest=sha256:510a8fd6cb2108e8c03bcee0ef8e35e80dd5ffcda4a7f3635c2a0c1b5aa1d034

Observation 155d5562-cfbd-4f32-95ad-882963aed605 · outbound

This paper cites arXiv preprint arXiv:2511.11698 , year=.

Expert-Guided Forecast Editing for Time-Series Foundation Models arXiv preprint arXiv:2511.11698 , year=

Reference 29

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Observation 8ef63c67-df9b-4c97-81f2-59efcdb852eb · outbound

This paper cites Sundial: A Family of Highly Capable Time Series Foundation Models.

Expert-Guided Forecast Editing for Time-Series Foundation Models Sundial: A Family of Highly Capable Time Series Foundation Models

Reference 30

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source=arxiv_source observed=2026-08-01T12:08:54.121766Z digest=sha256:6173fd12ec2533aa0431de3849edaa1bb7493fb23dc03e44134c63553d87efbf

Observation ec44f3a8-dcf9-4e4a-9d73-b3e69636c10f · outbound

This paper cites It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks.

Expert-Guided Forecast Editing for Time-Series Foundation Models It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks

Reference 31

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Pith citing papers

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