TsLLM: Augmenting LLMs for General Time Series Understanding and Prediction
Fuente:
arXiv
Saved in:
| Main Authors: | Parker, Felix, Chan, Nimeesha, Zhang, Chi, Ghobadi, Kimia |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Eliciting Chain-of-Thought Reasoning for Time Series Analysis using Reinforcement Learning
by: Parker, Felix, et al.
Published: (2025)
by: Parker, Felix, et al.
Published: (2025)
MedTsLLM: Leveraging LLMs for Multimodal Medical Time Series Analysis
by: Chan, Nimeesha, et al.
Published: (2024)
by: Chan, Nimeesha, et al.
Published: (2024)
Q-Learning with Shift-Aware Upper Confidence Bound in Non-Stationary Reinforcement Learning
by: Bui, Ha Manh, et al.
Published: (2025)
by: Bui, Ha Manh, et al.
Published: (2025)
SciTS: Scientific Time Series Understanding and Generation with LLMs
by: Wu, Wen, et al.
Published: (2025)
by: Wu, Wen, et al.
Published: (2025)
ViTs: Teaching Machines to See Time Series Anomalies Like Human Experts
by: Wang, Zexin, et al.
Published: (2025)
by: Wang, Zexin, et al.
Published: (2025)
Can LLMs Understand Time Series Anomalies?
by: Zhou, Zihao, et al.
Published: (2024)
by: Zhou, Zihao, et al.
Published: (2024)
Joint Score-Threshold Optimization for Interpretable Risk Assessment
by: Ganjkhanloo, Fardin, et al.
Published: (2025)
by: Ganjkhanloo, Fardin, et al.
Published: (2025)
An interpretable data-driven approach to optimizing clinical fall risk assessment
by: Ganjkhanloo, Fardin, et al.
Published: (2025)
by: Ganjkhanloo, Fardin, et al.
Published: (2025)
Data Augmentation in Time Series Forecasting through Inverted Framework
by: Tan, Hongming, et al.
Published: (2025)
by: Tan, Hongming, et al.
Published: (2025)
Rapid Augmentations for Time Series (RATS): A High-Performance Library for Time Series Augmentation
by: Skaf, Wadie, et al.
Published: (2026)
by: Skaf, Wadie, et al.
Published: (2026)
An interpretable data-driven approach to optimizing clinical fall risk assessment
by: Ganjkhanloo, Fardin, et al.
Published: (2026)
by: Ganjkhanloo, Fardin, et al.
Published: (2026)
Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning
by: Bian, Yuxuan, et al.
Published: (2024)
by: Bian, Yuxuan, et al.
Published: (2024)
TimeXL: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop
by: Jiang, Yushan, et al.
Published: (2025)
by: Jiang, Yushan, et al.
Published: (2025)
LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecasting
by: Kowsher, Md, et al.
Published: (2024)
by: Kowsher, Md, et al.
Published: (2024)
Understanding the Role of Textual Prompts in LLM for Time Series Forecasting: an Adapter View
by: Niu, Peisong, et al.
Published: (2023)
by: Niu, Peisong, et al.
Published: (2023)
RTL++: Graph-enhanced LLM for RTL Code Generation
by: Akyash, Mohammad, et al.
Published: (2025)
by: Akyash, Mohammad, et al.
Published: (2025)
Understanding Test-Time Augmentation
by: Kimura, Masanari
Published: (2024)
by: Kimura, Masanari
Published: (2024)
A Meta-Knowledge-Augmented LLM Framework for Hyperparameter Optimization in Time-Series Forecasting
by: Saadallah, Ons, et al.
Published: (2026)
by: Saadallah, Ons, et al.
Published: (2026)
Thoth: Mid-Training Bridges LLMs to Time Series Understanding
by: Lin, Jiafeng, et al.
Published: (2026)
by: Lin, Jiafeng, et al.
Published: (2026)
When LLM Meets Time Series: Can LLMs Perform Multi-Step Time Series Reasoning and Inference
by: Ye, Wen, et al.
Published: (2025)
by: Ye, Wen, et al.
Published: (2025)
From Low Intrinsic Dimensionality to Non-Vacuous Generalization Bounds in Deep Multi-Task Learning
by: Zakerinia, Hossein, et al.
Published: (2025)
by: Zakerinia, Hossein, et al.
Published: (2025)
Conformal Prediction with Time-Series Data via Sequential Conformalized Density Regions
by: Sampson, M., et al.
Published: (2026)
by: Sampson, M., et al.
Published: (2026)
Optimal Transport-based Conformal Prediction
by: Thurin, Gauthier, et al.
Published: (2025)
by: Thurin, Gauthier, et al.
Published: (2025)
DecoRTL: A Run-time Decoding Framework for RTL Code Generation with LLMs
by: Akyash, Mohammad, et al.
Published: (2025)
by: Akyash, Mohammad, et al.
Published: (2025)
TimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecasting
by: Zhang, Huanyu, et al.
Published: (2024)
by: Zhang, Huanyu, et al.
Published: (2024)
CaTs and DAGs: Integrating Directed Acyclic Graphs with Transformers for Causally Constrained Predictions
by: Vowels, Matthew J., et al.
Published: (2024)
by: Vowels, Matthew J., et al.
Published: (2024)
Data Augmentation for Multivariate Time Series Classification: An Experimental Study
by: Ilbert, Romain, et al.
Published: (2024)
by: Ilbert, Romain, et al.
Published: (2024)
TimeOmni-VL: Unified Models for Time Series Understanding and Generation
by: Guan, Tong, et al.
Published: (2026)
by: Guan, Tong, et al.
Published: (2026)
Towards Time Series Reasoning with LLMs
by: Chow, Winnie, et al.
Published: (2024)
by: Chow, Winnie, et al.
Published: (2024)
The Ends Justify the Thoughts: RL-Induced Motivated Reasoning in LLM CoTs
by: Howe, Nikolaus, et al.
Published: (2025)
by: Howe, Nikolaus, et al.
Published: (2025)
LLM-Powered CPI Prediction Inference with Online Text Time Series
by: Fan, Yingying, et al.
Published: (2025)
by: Fan, Yingying, et al.
Published: (2025)
Parametric Augmentation for Time Series Contrastive Learning
by: Zheng, Xu, et al.
Published: (2024)
by: Zheng, Xu, et al.
Published: (2024)
Weakly Augmented Variational Autoencoder in Time Series Anomaly Detection
by: Wu, Zhangkai, et al.
Published: (2024)
by: Wu, Zhangkai, et al.
Published: (2024)
LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters
by: Chang, Ching, et al.
Published: (2023)
by: Chang, Ching, et al.
Published: (2023)
Agentic Retrieval-Augmented Generation for Time Series Analysis
by: Ravuru, Chidaksh, et al.
Published: (2024)
by: Ravuru, Chidaksh, et al.
Published: (2024)
Elastic-dLLM: Position Preserving Context Compression and Augmentation of Diffusion LLMs
by: Wu, Junyi, et al.
Published: (2026)
by: Wu, Junyi, et al.
Published: (2026)
TimesBERT: A BERT-Style Foundation Model for Time Series Understanding
by: Zhang, Haoran, et al.
Published: (2025)
by: Zhang, Haoran, et al.
Published: (2025)
Retrieval Augmented Time Series Forecasting
by: Han, Sungwon, et al.
Published: (2025)
by: Han, Sungwon, et al.
Published: (2025)
AimTS: Augmented Series and Image Contrastive Learning for Time Series Classification
by: Chen, Yuxuan, et al.
Published: (2025)
by: Chen, Yuxuan, et al.
Published: (2025)
Retrieval Augmented Time Series Forecasting
by: Tire, Kutay, et al.
Published: (2024)
by: Tire, Kutay, et al.
Published: (2024)
Similar Items
-
Eliciting Chain-of-Thought Reasoning for Time Series Analysis using Reinforcement Learning
by: Parker, Felix, et al.
Published: (2025) -
MedTsLLM: Leveraging LLMs for Multimodal Medical Time Series Analysis
by: Chan, Nimeesha, et al.
Published: (2024) -
Q-Learning with Shift-Aware Upper Confidence Bound in Non-Stationary Reinforcement Learning
by: Bui, Ha Manh, et al.
Published: (2025) -
SciTS: Scientific Time Series Understanding and Generation with LLMs
by: Wu, Wen, et al.
Published: (2025) -
ViTs: Teaching Machines to See Time Series Anomalies Like Human Experts
by: Wang, Zexin, et al.
Published: (2025)