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Paper Citation Record · LEDGER

TS-RAG: Retrieval Augmented Generation for Time Series Forecasting

As of 8 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2608.06223.

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

pith.paper-citation-record.v1
2608.06223 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:12:14.332889Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

36 of 36 outbound references displayed

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  • verified fuzzy2
  • unresolved26
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 686b3f1c-1477-4803-a6aa-51314ceb237e · outbound

This paper cites an unresolved cited work.

TS-RAG: Retrieval Augmented Generation for Time Series Forecasting Unresolved cited work

Reference 1

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Observation aa40afdd-7a17-4027-83dc-eb49ea98830b · outbound

This paper cites An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.

TS-RAG: Retrieval Augmented Generation for Time Series Forecasting An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 2

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Observation 11043343-c673-473b-ae61-3393126aced2 · outbound

This paper cites Conditional Time Series Forecasting with Convolutional Neural Networks.

TS-RAG: Retrieval Augmented Generation for Time Series Forecasting Conditional Time Series Forecasting with Convolutional Neural Networks

Reference 3

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Observation 629ced24-49a6-47a9-b185-c1c5f016f4b0 · outbound

This paper cites Optimal global second-order regularity and improved integrability for parabolic equations with variable growth.

TS-RAG: Retrieval Augmented Generation for Time Series Forecasting Optimal global second-order regularity and improved integrability for parabolic equations with variable growth

Reference 4

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Observation 6d16e8bd-511b-43b7-88ac-6b762a92b107 · outbound

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Reference 5

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Observation ae25c383-33a0-44e2-96a1-77ea4545f48b · outbound

This paper cites Correcting discount-factor mismatch in on-policy policy gradient methods.

TS-RAG: Retrieval Augmented Generation for Time Series Forecasting Correcting discount-factor mismatch in on-policy policy gradient methods

Reference 6

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Observation 9faf6b84-01fd-474c-99a1-5831dc0eae38 · outbound

This paper cites Connected Hamel bases in Hilbert spaces.

TS-RAG: Retrieval Augmented Generation for Time Series Forecasting Connected Hamel bases in Hilbert spaces

Reference 7

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Observation fdd65482-c5a6-423f-85f0-3cc0c5bf1f89 · outbound

This paper cites Quenched CLT for ancestral lineages of logistic branching random walks.

TS-RAG: Retrieval Augmented Generation for Time Series Forecasting Quenched CLT for ancestral lineages of logistic branching random walks

Reference 8

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 99e7cc9f-cbaa-4d82-87d1-2a236d48b847 · outbound

This paper cites Clifford algebra Cl(0,6) approach to beyond the standard model and naturalness problems.

TS-RAG: Retrieval Augmented Generation for Time Series Forecasting Clifford algebra Cl(0,6) approach to beyond the standard model and naturalness problems

Reference 9

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Observation 79e044a8-cc3c-48ef-9d38-f54098c17d89 · outbound

This paper cites Representations Matter: Embedding Modes of Large Language Models using Dynamic Mode Decomposition.

TS-RAG: Retrieval Augmented Generation for Time Series Forecasting Representations Matter: Embedding Modes of Large Language Models using Dynamic Mode Decomposition

Reference 10

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Observation b915b90f-73a8-4eab-99e0-20797f3496b4 · outbound

This paper cites A new hydrogen-filled Cherenkov detector for Kaon tagging at the NA62 experiment at CERN.

TS-RAG: Retrieval Augmented Generation for Time Series Forecasting A new hydrogen-filled Cherenkov detector for Kaon tagging at the NA62 experiment at CERN

Reference 11

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Observation ad9d0f95-3eda-488f-97e6-7bf751a838e7 · outbound

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Reference 12

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Observation 9412522b-56bd-4a1c-a13b-1f830e5614da · outbound

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Reference 13

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Observation 1f70470c-eb2c-40a2-bdc9-f29f3ee157ed · outbound

This paper cites Superconductivity with high upper critical field in Ta-Hf Alloys.

TS-RAG: Retrieval Augmented Generation for Time Series Forecasting Superconductivity with high upper critical field in Ta-Hf Alloys

Reference 14

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Reference 15

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Observation cfd04dcb-e4dd-4e98-ab1c-5e45a226b5a9 · outbound

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Reference 16

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Reference 17

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Reference 18

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Observation c29037e0-e685-4736-8740-92b39ed5ad9b · outbound

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Reference 19

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Observation 8dd7830c-9938-4701-8987-d7d2d7833f0c · outbound

This paper cites iTransformer: Inverted Transformers Are Effective for Time Series Forecasting.

TS-RAG: Retrieval Augmented Generation for Time Series Forecasting iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 20

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Observation f8c5b05c-5afa-441a-9f1b-385cc763db48 · outbound

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TS-RAG: Retrieval Augmented Generation for Time Series Forecasting Nader, X

Reference 21

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Observation 13c0379e-11af-4737-87ae-36fa670991f3 · outbound

This paper cites A Time Series is Worth 64 Words: Long-term Forecasting with Transformers.

TS-RAG: Retrieval Augmented Generation for Time Series Forecasting A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Reference 22

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Observation c1f22816-27de-4a46-90e1-fa811a465ba3 · outbound

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Reference 23

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Reference 24

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Observation 75279b1d-55b0-4758-9f2a-9dceab3c8c20 · outbound

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TS-RAG: Retrieval Augmented Generation for Time Series Forecasting Optimal compilation of parametrised quantum circuits

Reference 25

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Observation 484ec2f2-e38b-4460-a671-aea46f35805c · outbound

This paper cites TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables.

TS-RAG: Retrieval Augmented Generation for Time Series Forecasting TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables

Reference 26

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This paper cites Exploring the hardness of the ionising radiation with the infrared softness diagram. I. Similar effective temperature scales for starbursts and (ultra)luminous infrared galaxies.

TS-RAG: Retrieval Augmented Generation for Time Series Forecasting Exploring the hardness of the ionising radiation with the infrared softness diagram. I. Similar effective temperature scales for starbursts and (ultra)luminous infrared galaxies

Reference 27

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Observation 75e0bd39-b3a1-4a8c-94ee-c3ce0b033ff2 · outbound

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Reference 28

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Reference 29

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Observation 69710be9-2237-4868-82be-76ecf84bc548 · outbound

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Reference 30

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Reference 31

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Reference 32

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Reference 33

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This paper cites InProceedings of the International Conference on Machine Learning (ICML).

TS-RAG: Retrieval Augmented Generation for Time Series Forecasting InProceedings of the International Conference on Machine Learning (ICML)

Reference 2020

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This paper cites GPT-4 Technical Report.

TS-RAG: Retrieval Augmented Generation for Time Series Forecasting GPT-4 Technical Report

Reference 2023

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Source-reported events for the cited work

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This paper cites Retrieval Augmented Time Series Forecasting.

TS-RAG: Retrieval Augmented Generation for Time Series Forecasting Retrieval Augmented Time Series Forecasting

Reference 2024

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Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.