Paper Citation Record · LEDGER

Are Language Models Actually Useful for Time Series Forecasting?

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2406.16964.

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

pith.paper-citation-record.v1
2406.16964 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:26:14.071928Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T03:12:28.612710Z

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 85461770-b4f7-4a9e-a37b-c0dd68e47705 · inbound

Revisiting Data Analysis with Pre-trained Foundation Models cites this paper.

Revisiting Data Analysis with Pre-trained Foundation Models Are Language Models Actually Useful for Time Series Forecasting?

Reference 139

Resolution: unresolved; no resolver link, observed 2026-08-10T22:26:14.071928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78b34a45-de81-42bc-9a78-e317f10a92ab · inbound

TempoGPT: Enhancing Time Series Reasoning via Quantizing Embedding cites this paper.

TempoGPT: Enhancing Time Series Reasoning via Quantizing Embedding Are Language Models Actually Useful for Time Series Forecasting?

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T20:48:12.836577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da3da4e9-b09b-4e77-8ba2-6b6d2c804f74 · inbound

FoNE: Precise Single-Token Number Embeddings via Fourier Features cites this paper.

FoNE: Precise Single-Token Number Embeddings via Fourier Features Are Language Models Actually Useful for Time Series Forecasting?

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-23T03:12:28.615879Z

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.

Observation 09576d2d-a569-4251-9d0e-188d0b98e870 · inbound

Integrating Traditional Technical Analysis with AI: A Multi-Agent LLM-Based Approach to Stock Market Forecasting cites this paper.

Integrating Traditional Technical Analysis with AI: A Multi-Agent LLM-Based Approach to Stock Market Forecasting Are Language Models Actually Useful for Time Series Forecasting?

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T23:42:00.139285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b3a61c8-838a-401e-80b5-1c06106156da · inbound

ElliottAgents: A Natural Language-Driven Multi-Agent System for Stock Market Analysis and Prediction cites this paper.

ElliottAgents: A Natural Language-Driven Multi-Agent System for Stock Market Analysis and Prediction Are Language Models Actually Useful for Time Series Forecasting?

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T20:15:51.615589Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c88e3080-3476-4eaa-ba19-a1650172815c · inbound

CSI-4CAST: A Hybrid Deep Learning Model for CSI Prediction with Comprehensive Robustness and Generalization Testing cites this paper.

CSI-4CAST: A Hybrid Deep Learning Model for CSI Prediction with Comprehensive Robustness and Generalization Testing Are Language Models Actually Useful for Time Series Forecasting?

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-04T09:52:08.011035Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.