Paper Citation Record · LEDGER

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges

As of 17 August 2026, this Paper Citation Record lists 100 of 124 outbound references and 3 inbound Pith citation observations for arXiv:2506.11040.

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

pith.paper-citation-record.v1
2506.11040 v1

Coverage vector

measured 100 of 124 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:26:59.038342Z

measured 103 of 103 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T04:19:17.895567Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

100 of 124 outbound references displayed

verified exact4
verified fuzzy10
unresolved86
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation a571637e-ba7b-445d-bbe6-e6f48d91fffa · outbound

This paper cites an unresolved cited work.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Unresolved cited work

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:49.442145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35784e6f-ea87-45ae-b543-dc816920a1b7 · outbound

This paper cites Time series analysis using autoregressive integrated moving average (arima) models,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Time series analysis using autoregressive integrated moving average (arima) models,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:49.507846Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6aca41ea-90f9-40d9-842f-ac7cf7be75b5 · outbound

This paper cites State space modeling of multiple time series,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges State space modeling of multiple time series,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:49.595949Z

Source-reported events for the cited work

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Observation d6cfd2fe-0d5b-4276-8f33-988d9af5b2d8 · outbound

This paper cites Informer: Beyond efficient transformer for long sequence time-series forecasting,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Informer: Beyond efficient transformer for long sequence time-series forecasting,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:49.680000Z

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Observation e13f5d4f-74e5-40a5-98e7-42fd2edc04d9 · outbound

This paper cites Temporal fusion transformers for interpretable multi-horizon time series forecasting,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Temporal fusion transformers for interpretable multi-horizon time series forecasting,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:49.769737Z

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Observation c7c1ece0-a1b3-4de9-80b4-a283499db13c · outbound

This paper cites A Survey of Large Language Models.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges A Survey of Large Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:49.878210Z

Source-reported events for the cited work

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Observation e7735072-3b2b-47cc-b4b5-ed1a7a85472a · outbound

This paper cites A survey of time series foundation models: Generalizing time series representation with large language model,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges A survey of time series foundation models: Generalizing time series representation with large language model,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:49.968308Z

Source-reported events for the cited work

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Observation 5efd1d8b-e84c-46f7-aa92-16ed4dd551e4 · outbound

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

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Empowering Time Series Analysis with Large Language Models: A Survey

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:50.082414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 745b27a2-75ab-4a7b-a638-8f00aeb482a4 · outbound

This paper cites Deep learning-based time series forecasting,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Deep learning-based time series forecasting,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:50.196626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 960b93ce-579d-44b6-94b5-33a6024c946e · outbound

This paper cites Attention is all you need,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Attention is all you need,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:50.290560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec6291fd-7018-4ea5-9e93-75cbb2af20e3 · outbound

This paper cites Learning graph structures with transformer for multivariate time-series anomaly detection in iot,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Learning graph structures with transformer for multivariate time-series anomaly detection in iot,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:50.403737Z

Source-reported events for the cited work

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Observation b116a262-33fa-4400-984f-b07823cb508b · outbound

This paper cites Language mod- els are few-shot learners,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Language mod- els are few-shot learners,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:50.494180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f50868f-763c-4978-a830-5a7a426b6c5a · outbound

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

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Large language models are zero-shot time series forecasters,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:50.591526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b62fe885-aa51-4ad5-9ebf-03d63b12b42b · outbound

This paper cites TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:50.679920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e8691d9-7e57-4405-9809-8f1365800f66 · outbound

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

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:50.749203Z

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Unavailable: canonical work link unavailable.

Observation f3e850e5-5073-4495-b64c-2b98ec93ac9d · outbound

This paper cites Insight miner: A large-scale multimodal model for insight mining from time series,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Insight miner: A large-scale multimodal model for insight mining from time series,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:50.837318Z

Source-reported events for the cited work

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Observation b877e4b3-fea6-48f8-ab1b-cd8861d8c6d1 · outbound

This paper cites A decoder-only foundation model for time-series forecasting,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges A decoder-only foundation model for time-series forecasting,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:50.910193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2498a529-b1f7-48e8-bb07-1711665837e1 · outbound

This paper cites Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:50.989726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 444d173d-d6e8-4cae-8b09-a5aee1ed2b8b · outbound

This paper cites Units: A unified multi-task time series model,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Units: A unified multi-task time series model,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:51.110453Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97ff7582-4623-4d24-b65b-21d28880694a · outbound

This paper cites Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:51.228206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66fea593-17e1-45d4-8b8c-54fa1d6c971b · outbound

This paper cites Meta- learning for few-shot time series classification,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Meta- learning for few-shot time series classification,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:51.320464Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b50ffeb6-d41f-4106-b8e0-65443eb75732 · outbound

This paper cites Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting

Reference 22

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Observation 17bea100-d49a-4f78-a2d4-fd114ad35f7c · outbound

This paper cites Language models are unsupervised multitask learners,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Language models are unsupervised multitask learners,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:51.566878Z

Source-reported events for the cited work

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Observation 3e5b779e-a007-4acf-a518-a8fde585fbbc · outbound

This paper cites Crosslingual Generalization through Multitask Finetuning.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Crosslingual Generalization through Multitask Finetuning

Reference 24

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Observation d3dd1869-8833-4e00-9be5-8323544c0891 · outbound

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

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges LLaMA: Open and Efficient Foundation Language Models

Reference 25

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Observation 607c7908-360a-4305-8121-097d8723bae1 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 26

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Observation f5117b75-c72b-4eda-bc61-b0ba387abc23 · outbound

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

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Promptcast: A new prompt-based learning paradigm for time series forecasting,

Reference 27

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Observation 09b3b47c-d9cb-4aed-84ea-49be95da28f2 · outbound

This paper cites Exploring large-scale language models to evaluate eeg-based multimodal data for mental health,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Exploring large-scale language models to evaluate eeg-based multimodal data for mental health,

Reference 28

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Observation a4098a98-8eac-4578-962b-fafb7d866844 · outbound

This paper cites AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts

Reference 29

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Observation 6ee5728f-8ba0-4bd5-934c-e5500a84efdf · outbound

This paper cites How can we know what language models know?.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges How can we know what language models know?

Reference 30

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Chain-of-thought prompting elicits reasoning in large language models,

Reference 31

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Observation 3662b8f8-acfc-4c21-810b-20756e58c665 · outbound

This paper cites Multimodal Chain-of-Thought Reasoning in Language Models.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Multimodal Chain-of-Thought Reasoning in Language Models

Reference 32

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Tree of thoughts: Deliberate problem solving with large language models,

Reference 33

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Graph of thoughts: Solving elaborate problems with large language models,

Reference 34

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This paper cites EEG-GPT: Exploring Capabilities of Large Language Models for EEG Classification and Interpretation.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges EEG-GPT: Exploring Capabilities of Large Language Models for EEG Classification and Interpretation

Reference 35

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Large Language Models Are Reasoning Teachers

Reference 36

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This paper cites Ticktack: Long span temporal alignment of large language models leveraging sexagenary cycle time expression,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Ticktack: Long span temporal alignment of large language models leveraging sexagenary cycle time expression,

Reference 37

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Observation 73cc6bef-43e0-4e2f-85e8-bf837a69d95e · outbound

This paper cites Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Open vocabulary electroencephalography-to-text decoding and zero-shot sentiment classification,

Reference 39

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges MedTsLLM: Leveraging LLMs for Multimodal Medical Time Series Analysis

Reference 40

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Observation 60ce54a7-d675-4b5f-ae57-021dfe903e64 · outbound

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

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting

Reference 41

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges From news to forecast: Integrating event analysis in llm-based time series forecasting with reflection,

Reference 42

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Observation ea766d36-5920-4d33-9dea-280614c09bb2 · outbound

This paper cites Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Data.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Data

Reference 43

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 44

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges TPP-LLM: Modeling Temporal Point Processes by Efficiently Fine-Tuning Large Language Models

Reference 45

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Using Pre-trained LLMs for Multivariate Time Series Forecasting

Reference 46

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Explainable multi-modal time series prediction with llm-in- the-loop,

Reference 47

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This paper cites Chatts: Aligning time series with llms via synthetic data for enhanced understanding and reasoning,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Chatts: Aligning time series with llms via synthetic data for enhanced understanding and reasoning,

Reference 48

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

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 49

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Llama-time: Fine-tuning a large language model for advanced time series prediction,

Reference 50

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Calf: Aligning llms for time series forecasting via cross-modal fine-tuning,

Reference 51

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Retrieval- augmented generation for knowledge-intensive nlp tasks,

Reference 52

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Retrieval Augmented Time Series Forecasting

Reference 53

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges TimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecasting

Reference 54

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Observation f93d3feb-f5c4-4328-94d2-dc886831085c · outbound

This paper cites Timerag: Boosting llm time series forecasting via retrieval-augmented generation,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Timerag: Boosting llm time series forecasting via retrieval-augmented generation,

Reference 55

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation

Reference 56

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Hy- bridrag: Integrating knowledge graphs and vector retrieval augmented generation for efficient information extraction,

Reference 57

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization

Reference 58

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges A survey on model com- pression for large language models,

Reference 59

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models

Reference 60

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Less is more: Task-aware layer-wise distillation for language model compres- sion,

Reference 61

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Observation d5c48d92-cec7-4cb4-9fa4-4294bd5fee41 · outbound

This paper cites Contrastive adversarial knowledge distillation for deep model compression in time- series regression tasks,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Contrastive adversarial knowledge distillation for deep model compression in time- series regression tasks,

Reference 62

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Observation fc5e8bec-cbd1-480e-8ebd-80dd6ca7255d · outbound

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Lightts: Lightweight time series classification with adaptive ensemble distillation,

Reference 63

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Timedistill: Efficient long-term time series forecasting with mlp via cross-architecture dis- tillation,

Reference 64

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Energy-Efficient Transformer Inference: Optimization Strategies for Time Series Classification

Reference 65

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Observation 0ba1c425-02c2-4533-b9fb-70d751a16708 · outbound

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges LLM-QAT: Data-Free Quantization Aware Training for Large Language Models

Reference 66

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Observation 836be77c-8eed-4fd1-b02b-07f10283aa86 · outbound

This paper cites Sparsegpt: Massive language models can be accurately pruned in one-shot,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Sparsegpt: Massive language models can be accurately pruned in one-shot,

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:56.254388Z

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Importance estimation for neural network pruning,

Reference 68

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Observation 3f592d0c-0c6b-4f7f-b382-8e4346239b9e · outbound

This paper cites Llm-pruner: On the structural pruning of large language models,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Llm-pruner: On the structural pruning of large language models,

Reference 69

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges LLM-based Knowledge Pruning for Time Series Data Analytics on Edge-computing Devices

Reference 70

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Observation edd8c5e8-73c5-4d9b-8332-396c3e0ad17d · outbound

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Parameter-efficient and student-friendly knowledge distillation,

Reference 71

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Knowledge Distillation vs. Pretraining from Scratch under a Fixed (Computation) Budget

Reference 72

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Observation c855eece-645c-4f70-a7d0-6c2f6beaa35a · outbound

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges The knowledge pyramid: the dikw hierarchy,

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:56.815916Z

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This paper cites Geng: An llm-based generic time series data generation approach for edge intelligence via cross-domain collaboration,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Geng: An llm-based generic time series data generation approach for edge intelligence via cross-domain collaboration,

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:56.894040Z

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Observation e299df04-d83b-4c58-90d7-55ef0938ebad · outbound

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement

Reference 75

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Position: What can large language models tell us about time series analysis,

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:57.127535Z

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Observation f1cae9de-45d5-46fe-b860-fe677d35c9e2 · outbound

This paper cites Hierarchical multimodal llms with semantic space alignment for enhanced time series classification,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Hierarchical multimodal llms with semantic space alignment for enhanced time series classification,

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:57.204108Z

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges $\textbf{S}^2$IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:57.340697Z

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Observation 224805da-4518-44bd-af56-c93f3d130c17 · outbound

This paper cites Worldgpt: Empowering llm as multimodal world model,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Worldgpt: Empowering llm as multimodal world model,

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:57.413103Z

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Integrating large language model, eeg, and eye-tracking for word-level neural state classification in reading comprehension,

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:57.507383Z

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Ts2vec: Towards universal representation of time series,

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:57.603758Z

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Pre-training enhanced spatial- temporal graph neural network for multivariate time series forecasting,

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:57.709132Z

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Towards Time Series Reasoning with LLMs

Reference 83

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges A survey of multimodel large language models,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:03.440975Z

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Model reprogramming: Resource-efficient cross-domain machine learning,

Reference 85

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges V oice2series: Reprogram- ming acoustic models for time series classification,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:03.421222Z

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Observation fc9b2184-e77d-4f56-bd37-c200caa97162 · outbound

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Enhance time series modeling by integrating llm,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:03.409233Z

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Observation a2dfa6de-e43b-42a4-8246-6650ab05c8b8 · outbound

This paper cites Large Language Models for Time Series: A Survey.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Large Language Models for Time Series: A Survey

Reference 88

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This paper cites A dual-stream cross agformer-gpt network for traffic flow prediction based on large-scale road sensor data,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges A dual-stream cross agformer-gpt network for traffic flow prediction based on large-scale road sensor data,

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:03.398162Z

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Observation cda864ff-f55a-40e0-bf28-4c91512de575 · outbound

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Speech emotion recognition using dual-stream representation and cross-attention fusion,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:03.385271Z

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Observation b890566b-2553-4a54-8bfd-ee3a8b6d4e55 · outbound

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Long short-term memory,

Reference 91

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Unresolved cited work

Reference 92

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Observation e5c8bd97-9264-4b8e-987b-2a98dc52932f · outbound

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Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:58.542732Z

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This paper cites A novel hybrid model for stock price forecasting: Combining arima, random forests, and gradient boosting techniques,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges A novel hybrid model for stock price forecasting: Combining arima, random forests, and gradient boosting techniques,

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:03.354046Z

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Observation 6bde37ee-b9a1-4f08-b350-cd0f20cb46f3 · outbound

This paper cites A survey of ensemble learning: Concepts, algorithms, applications, and prospects,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges A survey of ensemble learning: Concepts, algorithms, applications, and prospects,

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:03.342372Z

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Observation e6742996-5fe6-4bec-8339-1ce97c92bb9b · outbound

This paper cites Stacking Your Transformers: A Closer Look at Model Growth for Efficient LLM Pre-Training.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Stacking Your Transformers: A Closer Look at Model Growth for Efficient LLM Pre-Training

Reference 96

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This paper cites Llm-enhanced multi-teacher knowledge distillation for modality-incomplete emotion recognition in daily healthcare,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Llm-enhanced multi-teacher knowledge distillation for modality-incomplete emotion recognition in daily healthcare,

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:03.329408Z

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Observation e566ec5c-cad4-4bc3-acd8-59256cc19beb · outbound

This paper cites Vilbert: Pretraining task- agnostic visiolinguistic representations for vision-and-language tasks,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Vilbert: Pretraining task- agnostic visiolinguistic representations for vision-and-language tasks,

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:03.317940Z

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Observation 37ef0fc4-daa1-4be1-ad63-29fdc68b8ab2 · outbound

This paper cites Text2TimeSeries: Enhancing Financial Forecasting through Time Series Prediction Updates with Event-Driven Insights from Large Language Models.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Text2TimeSeries: Enhancing Financial Forecasting through Time Series Prediction Updates with Event-Driven Insights from Large Language Models

Reference 99

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Observation 7c996ae0-6361-4a10-a5ef-1bad41d57e28 · outbound

This paper cites Spatial–temporal transformer networks for traffic flow forecasting using a pre-trained language model,.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Spatial–temporal transformer networks for traffic flow forecasting using a pre-trained language model,

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:03.306045Z

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Pith citing papers

Observation b695dd5b-5db8-4a23-be5d-2256367f9d82 · inbound

MemCast: Memory-Driven Time Series Forecasting with Experience-Conditioned Reasoning cites this paper.

MemCast: Memory-Driven Time Series Forecasting with Experience-Conditioned Reasoning Large Language models for Time Series Analysis: Techniques, Applications, and Challenges

Reference 29

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Heterogeneous Scientific Foundation Model Collaboration Large Language models for Time Series Analysis: Techniques, Applications, and Challenges

Reference 122

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Observation bb7b7f8c-e55f-438f-a11c-44f31f823aee · inbound

CastFSR: A Fast--Slow--Reflect Agentic Reasoning Framework for Context-Aware Time Series Forecasting cites this paper.

CastFSR: A Fast--Slow--Reflect Agentic Reasoning Framework for Context-Aware Time Series Forecasting Large Language models for Time Series Analysis: Techniques, Applications, and Challenges

Reference 86

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