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Paper Citation Record · LEDGER

Understanding the Role of Textual Prompts in LLM for Time Series Forecasting: an Adapter View

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2311.14782.

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

pith.paper-citation-record.v1
2311.14782 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T12:15:26.142027Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-24T04:28:53.428142Z

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 3d2a9e1f-1804-4826-9aa9-94f76c86e754 · inbound

Universal Time-Series Representation Learning: A Survey cites this paper.

Universal Time-Series Representation Learning: A Survey Understanding the Role of Textual Prompts in LLM for Time Series Forecasting: an Adapter View

Reference 245

Resolution
verified exact
arxiv_id, observed 2026-05-24T04:28:53.431207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-24T04:26:45.527625Z digest=sha256:11038446811194baafb8ebe4c880422a141d4197fe69e5ae06281591f9712fa2

Observation 589a7b6a-7a3c-4a8f-a752-b99da2767a8a · inbound

From Time Series Analysis to Question Answering: A Survey in the LLM Era cites this paper.

From Time Series Analysis to Question Answering: A Survey in the LLM Era Understanding the Role of Textual Prompts in LLM for Time Series Forecasting: an Adapter View

Reference 78

Resolution
verified exact
arxiv_id, observed 2026-05-19T09:32:15.795790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-19T09:31:55.829045Z digest=sha256:4655464264c026fa10885f773e4b6ae61825dd484cb041a7133a0a3a8112df9b

Observation 3a60a3d0-c1ba-4443-8977-0b790d0f4cd2 · inbound

SSDA: Bridging Spectral and Structural Gaps via Dual Adaptation for Vision-Based Time Series Forecasting cites this paper.

SSDA: Bridging Spectral and Structural Gaps via Dual Adaptation for Vision-Based Time Series Forecasting Understanding the Role of Textual Prompts in LLM for Time Series Forecasting: an Adapter View

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-05-14T21:43:00.838651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-14T21:39:37.875167Z digest=sha256:23985681f6c9032638890b131979fbe1ceda3b439894b9841411905dd7c7565c

Observation e819a7c8-618b-4523-bd29-c94a6d32ff2c · inbound

Using LLMs for Explainable, Data-Driven Insight Generation from Time Series cites this paper.

Using LLMs for Explainable, Data-Driven Insight Generation from Time Series Understanding the Role of Textual Prompts in LLM for Time Series Forecasting: an Adapter View

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-02T12:15:26.142027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T12:15:26.142027Z digest=sha256:39e163973981a78e8d5c1b828174b42e17c88654015ecfb74e9f3d0cf1da7e3d