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Paper Citation Record · LEDGER

Using LLMs for Explainable, Data-Driven Insight Generation from Time Series

As of 8 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2607.18271.

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

pith.paper-citation-record.v1
2607.18271 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

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

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

47 of 47 outbound references displayed

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Outbound references

Observation da965b34-c1ee-4384-809b-18ca0a98852e · outbound

This paper cites an unresolved cited work.

Using LLMs for Explainable, Data-Driven Insight Generation from Time Series Unresolved cited work

Reference 1

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Observation 4f0deb66-7306-481e-a424-9417f11c0fef · outbound

This paper cites Deductive Closure Training of Language Models for Coherence, Accuracy, and Updatability.

Using LLMs for Explainable, Data-Driven Insight Generation from Time Series Deductive Closure Training of Language Models for Coherence, Accuracy, and Updatability

Reference 2

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This paper cites MAIRA-2: Grounded Radiology Report Generation.

Using LLMs for Explainable, Data-Driven Insight Generation from Time Series MAIRA-2: Grounded Radiology Report Generation

Reference 3

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Reference 4

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Using LLMs for Explainable, Data-Driven Insight Generation from Time Series Unresolved cited work

Reference 5

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Using LLMs for Explainable, Data-Driven Insight Generation from Time Series Unresolved cited work

Reference 6

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Using LLMs for Explainable, Data-Driven Insight Generation from Time Series Unresolved cited work

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Using LLMs for Explainable, Data-Driven Insight Generation from Time Series Unresolved cited work

Reference 9

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Using LLMs for Explainable, Data-Driven Insight Generation from Time Series Unresolved cited work

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This paper cites A Survey on In-context Learning.

Using LLMs for Explainable, Data-Driven Insight Generation from Time Series A Survey on In-context Learning

Reference 12

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This paper cites The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics.

Using LLMs for Explainable, Data-Driven Insight Generation from Time Series The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics

Reference 14

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Using LLMs for Explainable, Data-Driven Insight Generation from Time Series A Survey on LLM-as-a-Judge

Reference 15

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Using LLMs for Explainable, Data-Driven Insight Generation from Time Series 2018.Forecasting: principles and practice

Reference 19

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Using LLMs for Explainable, Data-Driven Insight Generation from Time Series SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning

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Using LLMs for Explainable, Data-Driven Insight Generation from Time Series UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

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Reference 36

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Using LLMs for Explainable, Data-Driven Insight Generation from Time Series Cloze procedure

Reference 37

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Using LLMs for Explainable, Data-Driven Insight Generation from Time Series 2022.Unlocking the Power of Sentence Embeddings with all-MiniLM- L6-v2

Reference 38

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Using LLMs for Explainable, Data-Driven Insight Generation from Time Series Persuasion with Large Language Models: A Survey of Empirical Evidence, Study Methodologies, and Ethical Implications

Reference 39

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Using LLMs for Explainable, Data-Driven Insight Generation from Time Series Unresolved cited work

Reference 40

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Using LLMs for Explainable, Data-Driven Insight Generation from Time Series Unresolved cited work

Reference 41

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

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

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This paper cites NeuralProphet: Explainable Forecasting at Scale.

Using LLMs for Explainable, Data-Driven Insight Generation from Time Series NeuralProphet: Explainable Forecasting at Scale

Reference 44

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This paper cites an unresolved cited work.

Using LLMs for Explainable, Data-Driven Insight Generation from Time Series Unresolved cited work

Reference 1990

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This paper cites InProceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP).

Using LLMs for Explainable, Data-Driven Insight Generation from Time Series InProceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Reference 2019

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This paper cites Contrastive Chain-of-Thought Prompting.

Using LLMs for Explainable, Data-Driven Insight Generation from Time Series Contrastive Chain-of-Thought Prompting

Reference 2023

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This paper cites Language Models Still Struggle to Zero-shot Reason about Time Series.

Using LLMs for Explainable, Data-Driven Insight Generation from Time Series Language Models Still Struggle to Zero-shot Reason about Time Series

Reference 2024

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