Paper Citation Record · LEDGER

Context information can be more important than reasoning for time series forecasting with a large language model

As of 10 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2502.05699.

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

pith.paper-citation-record.v1
2502.05699 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T18:22:22.897573Z

measured 19 of 19 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 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

19 of 19 outbound references displayed

verified exact0
verified fuzzy11
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 65334702-2155-46ff-ae94-0823e593834a · outbound

This paper cites BERT: Pre- training of Deep Bidirectional Transformers for Language Understanding,.

Context information can be more important than reasoning for time series forecasting with a large language model BERT: Pre- training of Deep Bidirectional Transformers for Language Understanding,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:22:23.284215Z

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 fe29f372-4c99-44e2-8d87-1d11f51eab85 · outbound

This paper cites XLNet: generalized autoregressive pretraining for language understanding,.

Context information can be more important than reasoning for time series forecasting with a large language model XLNet: generalized autoregressive pretraining for language understanding,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:22:23.272599Z

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 a3cc734f-b26c-4ec4-8ae2-1ff32507cd40 · outbound

This paper cites A Survey on Diffusion Models for Time Series and Spatio-Temporal Data,.

Context information can be more important than reasoning for time series forecasting with a large language model A Survey on Diffusion Models for Time Series and Spatio-Temporal Data,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-08T18:22:22.832132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95d17d1e-ada4-42b8-b272-c157b3e52498 · outbound

This paper cites Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm,.

Context information can be more important than reasoning for time series forecasting with a large language model Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:22:23.259656Z

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 d2550ac9-be5c-47e2-aec6-099c34238948 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models,.

Context information can be more important than reasoning for time series forecasting with a large language model Chain-of-thought prompting elicits reasoning in large language models,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:22:23.247044Z

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 7739ec7d-693d-48dc-b547-43eb8a45362b · outbound

This paper cites LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting.

Context information can be more important than reasoning for time series forecasting with a large language model LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-08T18:22:22.844164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d54cf20-2085-4f48-81a3-d63b04f2459a · outbound

This paper cites an unresolved cited work.

Context information can be more important than reasoning for time series forecasting with a large language model Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-08T18:22:23.236476Z

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 2c50fe9b-b69f-4216-9fa6-12c1c3e0b888 · outbound

This paper cites Synthetic prompting: generating chain-of-thought demonstrations for large language models,.

Context information can be more important than reasoning for time series forecasting with a large language model Synthetic prompting: generating chain-of-thought demonstrations for large language models,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:22:23.214746Z

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 97c95969-b436-441c-93b6-a9fa5900c6a7 · outbound

This paper cites Iteratively Prompt Pre-trained Language Models for Chain of Thought,.

Context information can be more important than reasoning for time series forecasting with a large language model Iteratively Prompt Pre-trained Language Models for Chain of Thought,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:22:23.200558Z

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 fe8f7e93-6b18-487b-847b-1f939b099b69 · outbound

This paper cites PACE: Improving Prompt with Actor-Critic Editing for Large Language Model.

Context information can be more important than reasoning for time series forecasting with a large language model PACE: Improving Prompt with Actor-Critic Editing for Large Language Model

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-08T18:22:22.864141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4486d49-5122-4dc8-ba61-d06248bd6e70 · outbound

This paper cites Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting.

Context information can be more important than reasoning for time series forecasting with a large language model Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-08T18:22:22.868872Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e8cb3b3-fe84-48b5-b17a-d9e929f0bea4 · outbound

This paper cites PromptCast: A New Prompt-Based Learning Paradigm for Time Series Forecasting,.

Context information can be more important than reasoning for time series forecasting with a large language model PromptCast: A New Prompt-Based Learning Paradigm for Time Series Forecasting,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:22:23.188071Z

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 760ba6bc-a061-4360-a9b8-1f3c29594361 · outbound

This paper cites Large language models are zero-shot time series forecasters,.

Context information can be more important than reasoning for time series forecasting with a large language model Large language models are zero-shot time series forecasters,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:22:23.174977Z

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 8295d962-8314-4286-b139-8cb64397d527 · outbound

This paper cites Time-LLM: Time Series Forecasting by Reprogramming Large Language Models.

Context information can be more important than reasoning for time series forecasting with a large language model Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-08T18:22:22.881085Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd92e843-f503-420c-9272-e100657fb887 · outbound

This paper cites TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting.

Context information can be more important than reasoning for time series forecasting with a large language model TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-08T18:22:22.885319Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 593b762c-5425-4734-a887-12801d132855 · outbound

This paper cites FinDKG: Dynamic Knowledge Graphs with Large Language Models for Detecting Global Trends in Financial Markets,.

Context information can be more important than reasoning for time series forecasting with a large language model FinDKG: Dynamic Knowledge Graphs with Large Language Models for Detecting Global Trends in Financial Markets,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-08T18:22:22.889679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e63ef069-e837-4b0c-8e32-38e1b8fb090c · outbound

This paper cites Frozen Language Model Helps ECG Zero-Shot Learning,.

Context information can be more important than reasoning for time series forecasting with a large language model Frozen Language Model Helps ECG Zero-Shot Learning,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:22:23.161662Z

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 24ebad0a-2059-4f39-add6-6b5768d51f94 · outbound

This paper cites Summplementary note.

Context information can be more important than reasoning for time series forecasting with a large language model Summplementary note

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:22:23.144905Z

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 5b952e14-30ad-4219-a3a7-8df6140e69b4 · outbound

This paper cites Plan-and-Solve Prompting: Improving Zero-Shot Chain-of- Thought Reasoning by Large Language Models,.

Context information can be more important than reasoning for time series forecasting with a large language model Plan-and-Solve Prompting: Improving Zero-Shot Chain-of- Thought Reasoning by Large Language Models,

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:22:23.226501Z

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.

Pith citing papers

No inbound Pith citation observations are available.