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Paper Citation Record · LEDGER

Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2503.11411.

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

pith.paper-citation-record.v1
2503.11411 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 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 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T15:43:12.734156Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-08T04:14:29.630786Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 9774502a-3637-4ca4-ad0f-a513070da8e5 · inbound

Generative Models for Synthetic Data: Transforming Data Mining in the GenAI Era cites this paper.

Generative Models for Synthetic Data: Transforming Data Mining in the GenAI Era Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-05T15:43:12.734156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:43:12.734156Z digest=sha256:ecf079dca6607d2da2e79a7a7028b5e3dc3c9d68633a049843b452bf651d1345

Observation 0a915f45-b059-4a82-9fd1-2350b4142c9c · inbound

A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models cites this paper.

A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models

Reference 76

Resolution
unresolved
no resolver link, observed 2026-08-04T16:49:31.716235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T16:49:31.716235Z digest=sha256:a70e362ceb45a753554a89928c72eb46a756839165b284e1e7f4230a7f0415fc

Observation 5abba74d-020c-4568-b104-17afdcf7b5b6 · inbound

Parallel Complex Diffusion for Scalable Time Series Generation cites this paper.

Parallel Complex Diffusion for Scalable Time Series Generation Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-03T02:51:55.787719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T02:51:55.787719Z digest=sha256:daf0d0e17ef122424b5d32e73b75ce84679180cdd7b0b5b6b763c16830da831a

Observation 7498473b-064a-47d6-88d1-13671099149b · inbound

TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning cites this paper.

TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models

Reference 40

Resolution
verified exact
arxiv_id, observed 2026-05-10T07:16:54.862970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-10T07:15:28.648869Z digest=sha256:ebe21c12178301179b54a244c76e57dd7c755094da24e2599ef906e2b4930baf

Observation 809dd6fd-dfc8-412f-981d-d70c48173544 · inbound

TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning cites this paper.

TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-03T02:26:14.140867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T02:26:14.140867Z digest=sha256:a54430427b2ea8e811b963fd225e9eece5b298e2258364d24f7f43190875f44c

Observation 8ee8e519-8f4a-4352-97a3-5dd62aa68dfa · inbound

TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models cites this paper.

TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-07-02T12:46:56.977379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-28T01:50:24.426751Z digest=sha256:477c0cafff5f86fe55a81fef0c954b2fe0089060b8a79e1592d218142a444f6b

Observation bb060eba-de58-4c0a-b771-f9a6540ad6eb · inbound

RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models cites this paper.

RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models

Reference 20

Resolution
verified exact
local_arxiv, observed 2026-07-08T04:14:29.632114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-07-08T04:07:59.537908Z digest=sha256:78f6f9bfe4c2ff44361a6f47966142844e87c8157d6904d697fa90159023f5bf

Observation 442c5cb9-0282-4654-add1-65f063243f8b · inbound

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data cites this paper.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-02T09:50:22.243832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T09:50:22.243832Z digest=sha256:2707874658a946f9d22f0e253bfc248dd990364e8b19bb181cbf3e9d7675e00e