Paper Citation Record · LEDGER

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting

As of 14 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2507.17016.

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

pith.paper-citation-record.v1
2507.17016 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:04:12.216956Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

41 of 41 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fbc8f043-123a-457f-9477-45ebaf952bd7 · outbound

This paper cites Time-series large language models: A systematic review of state-of-the-art,.

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Time-series large language models: A systematic review of state-of-the-art,

Reference 1

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Observation c2b165bd-5304-4c4f-882c-49a803f98db4 · outbound

This paper cites How can time series analysis benefit from multiple modalities? a survey and outlook,.

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting How can time series analysis benefit from multiple modalities? a survey and outlook,

Reference 2

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This paper cites A Comprehensive Survey of Deep Learning for Time Series Forecasting: Architectural Diversity and Open Challenges.

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting A Comprehensive Survey of Deep Learning for Time Series Forecasting: Architectural Diversity and Open Challenges

Reference 3

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Observation ab2c8024-bb8b-4a22-b6ec-757623cab1c7 · outbound

This paper cites Towards Time Series Reasoning with LLMs.

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Towards Time Series Reasoning with LLMs

Reference 4

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This paper cites Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series.

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series

Reference 5

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Observation ac06cd2d-a698-4895-a64d-4ffd4cd2aec1 · outbound

This paper cites Empowering Time Series Analysis with Large Language Models: A Survey.

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Empowering Time Series Analysis with Large Language Models: A Survey

Reference 6

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Observation bb2d50f3-caca-4c45-a934-165763dccf54 · outbound

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

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T15:04:11.130646Z

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Observation 1c1804c7-6a27-4281-b914-3a2ca9111ab9 · outbound

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

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 8

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This paper cites Promptcast: A new prompt-based learning paradigm for time series forecasting,.

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Promptcast: A new prompt-based learning paradigm for time series forecasting,

Reference 9

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Observation 9b6bc11c-8624-4973-a505-883a9cce5d7e · outbound

This paper cites Chronos: Learning the Language of Time Series.

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Chronos: Learning the Language of Time Series

Reference 10

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Source-reported events for the cited work

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Observation 9eef9b6c-687c-4806-a24e-24cf4e8a7a6e · outbound

This paper cites Large Language Models Are Zero-Shot Time Series Forecasters.

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Large Language Models Are Zero-Shot Time Series Forecasters

Reference 11

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Observation 88044821-112e-4b94-9632-e10e8bcfbd3d · outbound

This paper cites Gpt4mts: prompt-based large language model for multimodal time-series forecasting,.

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Gpt4mts: prompt-based large language model for multimodal time-series forecasting,

Reference 12

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Observation bc541acf-d961-480c-b278-6ec9e219dd14 · outbound

This paper cites Unitime: A language-empowered unified model for cross-domain time series forecasting,.

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Unitime: A language-empowered unified model for cross-domain time series forecasting,

Reference 13

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Observation c111966a-479e-4659-be50-be9e97912c90 · outbound

This paper cites S2ip-llm: semantic space informed prompt learning with llm for time series forecasting,.

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting S2ip-llm: semantic space informed prompt learning with llm for time series forecasting,

Reference 14

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Observation 07dccdb8-51d7-4f4a-9f85-2ce7741f0c79 · outbound

This paper cites Language models can improve event prediction by few-shot abductive reasoning,.

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Language models can improve event prediction by few-shot abductive reasoning,

Reference 15

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Observation c6f8e752-9573-4f07-a8f9-b7a11554db65 · outbound

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

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting

Reference 16

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Observation 51721008-f35f-4128-8532-f1e922942c0b · outbound

This paper cites Can chatgpt forecast stock price movements? return predictability and large language models,.

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Can chatgpt forecast stock price movements? return predictability and large language models,

Reference 17

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Observation e81503f4-cff5-4792-8d2c-360c4bd92ebf · outbound

This paper cites Large Language Models are Few-Shot Health Learners.

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Large Language Models are Few-Shot Health Learners

Reference 18

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Observation 52d08652-f003-44e2-a383-c1163a7c2396 · outbound

This paper cites Leveraging language foun- dation models for human mobility forecasting,.

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Leveraging language foun- dation models for human mobility forecasting,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:04:15.711349Z

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Observation c1f292d4-56bf-43a0-8603-c936931710a4 · outbound

This paper cites Where Would I Go Next? Large Language Models as Human Mobility Predictors.

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Where Would I Go Next? Large Language Models as Human Mobility Predictors

Reference 20

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Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Spatial- temporal large language model for traffic prediction,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:04:15.511948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 35a6f8f7-2b10-4915-b4ff-9e08747d52d7 · outbound

This paper cites A tutorial on fuzzy time series forecasting models: recent advances and challenges,.

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting A tutorial on fuzzy time series forecasting models: recent advances and challenges,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:04:15.346960Z

Source-reported events for the cited work

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Observation f1018448-a892-4dd6-8eea-11d63d075982 · outbound

This paper cites Detecting and quantifying causal associations in large nonlinear time series datasets,.

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Detecting and quantifying causal associations in large nonlinear time series datasets,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:04:15.151261Z

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Observation e92311f8-1643-45e9-8749-756fef46c8d8 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding,.

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Bert: Pre-training of deep bidirectional transformers for language understanding,

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Observation 77ac798f-aa2a-40ab-b68c-dee07b51dbb7 · outbound

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Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Language models are unsupervised multitask learners,

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Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 26

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Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Attention is all you need,

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Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Language models are few-shot learners,

Reference 28

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Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Palm: Scaling language modeling with pathways,

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Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Training Compute-Optimal Large Language Models

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Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting GPT-4 Technical Report

Reference 31

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Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting LaMDA: Language Models for Dialog Applications

Reference 32

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Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Llama: Open and efficient foundation language models,

Reference 33

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Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 34

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Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Forecasting enrollments with fuzzy time series—part i,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:04:14.347787Z

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This paper cites Forecasting enrollments with fuzzy time series—part ii,.

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Forecasting enrollments with fuzzy time series—part ii,

Reference 36

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Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Forecasting enrollments based on fuzzy time series,

Reference 37

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Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Fuzzy sets,

Reference 38

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Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting PYFTS/pyFTS: Stable version 1.7,

Reference 39

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This paper cites Available: https://www.science.org/doi/10.1126/sciadv.

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Available: https://www.science.org/doi/10.1126/sciadv

Reference 2019

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This paper cites LLaMA: Open and Efficient Foundation Language Models.

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting LLaMA: Open and Efficient Foundation Language Models

Reference 2023

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Pith citing papers

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