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Paper Citation Record · LEDGER

T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1811.08295.

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

pith.paper-citation-record.v1
1811.08295 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T05:26:03.646783Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-18T14:46:29.325174Z

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 8a58a823-918d-4ae5-9c43-4251c1b8f1ab · inbound

CTBench: Cryptocurrency Time Series Generation Benchmark cites this paper.

CTBench: Cryptocurrency Time Series Generation Benchmark T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-06T05:26:03.646783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T05:26:03.646783Z digest=sha256:3ed8a801751ac82a1a5d6bb4b2aa48e516eefa80bb9f090a1bb186c3afe0566e

Observation b03c0cd6-9a62-49cc-989d-b1db55767258 · inbound

Causal Time Series Generation via Diffusion Models cites this paper.

Causal Time Series Generation via Diffusion Models T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling

Reference 31

Resolution
verified exact
local_arxiv, observed 2026-05-18T14:46:29.327090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-18T14:45:29.515515Z digest=sha256:4d7bcb7ccd42836048c1c3a9b51543e3f0ed43134ad469cf3a223e640d4d01a9

Observation 244e53dd-2008-481f-bb5c-850aea3b6e17 · inbound

Diff-MN: Diffusion Parameterized MoE-NCDE for Continuous Time Series Generation with Irregular Observations cites this paper.

Diff-MN: Diffusion Parameterized MoE-NCDE for Continuous Time Series Generation with Irregular Observations T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-03T09:34:54.286149Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:34:54.286149Z digest=sha256:46b1c0abad60be28239c72af30f3cc924136a553bf4a545162e16395d0367eb1