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Paper Citation Record · LEDGER

Expert-Guided Forecast Editing for Time-Series Foundation Models

As of 21 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 2026-08-21T06:32:19.484+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
  • metadata mismatch0

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=sha256:9384c5a1cd7afb297dcebfb870f4b00324b688444c965ac1a41b801e850d3b03

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=sha256:03eb85cc561b5f5324be9d4850867693f4bcd174b6f353cde0af2b72bc892e11

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=sha256:530da5f2418261361058006a40cc65a7b5621f4d4c12f4bbf06aef3b5ee2b592

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=sha256:219e026c93c132cc5f1740657d143c9cf36b2a54105c859a95ee0a047ad6759f

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=sha256:72c59bf5148219d2966fc18b9b666a42d16353b6f6e287632856ad9f9c57c09d

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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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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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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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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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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source=arxiv_source observed=2026-08-01T12:08:51.523142Z digest=sha256:3057f4b94af7e526a16890c1a592a0a9d1d10080afc3a2afae9077f0622e2595

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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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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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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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=sha256:2400cdf25b3df6edbaf508e3de4c4afc1f03caf9303ff9ffabb62b0358f60461

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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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=sha256:469456f56b9dca63b34d4c85a00d7d288729cfa7bbb2af4ba25b0d2b199c4d88

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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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=sha256:83437e614af58443c1cf5898b907130e35f751bbddff85262fe18dc3e84ad8e9

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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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:b973658fe1853288d9f02188679a02b5cde0d213a6b46a5ae322554f33c95a92

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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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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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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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=sha256:2f07c3b240ce22988f2b243be10d5efaee84e5076104170a151283ae1316aa01

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=sha256:60d9d72c711ea206da890e9f6cf39bbb6d6e13fdd78f878c1ac14640b0c4b6ce

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

No inbound Pith citation observations are available.