Large Language models for Time Series Analysis: Techniques, Applications, and Challenges | Paper Citation Record

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

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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,

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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,

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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,

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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,

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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

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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,

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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

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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,

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This paper cites Attention is all you need,.

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

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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,

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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

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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

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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

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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

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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,

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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,

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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

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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

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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

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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

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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

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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

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

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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

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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

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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,

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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,

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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

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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?

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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,

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

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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,

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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,

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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

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

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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,

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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,

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

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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

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This paper cites From news to forecast: Integrating event analysis in llm-based time series forecasting with reflection,.

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,

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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

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This paper cites LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters.

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

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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

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Observation 3d6ac34e-a206-4da4-97af-5e77e9e7478f · outbound

This paper cites Using Pre-trained LLMs for Multivariate Time Series Forecasting.

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

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This paper cites Explainable multi-modal time series prediction with llm-in- the-loop,.

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

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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,

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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

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This paper cites Llama-time: Fine-tuning a large language model for advanced time series prediction,.

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

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This paper cites Calf: Aligning llms for time series forecasting via cross-modal fine-tuning,.

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

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

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This paper cites Retrieval Augmented Time Series Forecasting.

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

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This paper cites TimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecasting.

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

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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,

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This paper cites Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation.

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

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This paper cites Hy- bridrag: Integrating knowledge graphs and vector retrieval augmented generation for efficient information extraction,.

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,

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This paper cites StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization.

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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This paper cites In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models.

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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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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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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This paper cites LLM-QAT: Data-Free Quantization Aware Training for Large Language Models.

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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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

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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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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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This paper cites LLM-based Knowledge Pruning for Time Series Data Analytics on Edge-computing Devices.

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

Reference 73

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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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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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This paper cites Position: What can large language models tell us about time series analysis,.

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

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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

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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

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

Reference 79

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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

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

Reference 81

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This paper cites Pre-training enhanced spatial- temporal graph neural network for multivariate time series forecasting,.

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

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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

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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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This paper cites V oice2series: Reprogram- ming acoustic models for time series classification,.

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

Reference 87

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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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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

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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

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

Reference 93

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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

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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

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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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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

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

Reference 98

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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

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