Cover Image

Position: What Can Large Language Models Tell Us about Time Series Analysis

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Jin, Ming, Zhang, Yifan, Chen, Wei, Zhang, Kexin, Liang, Yuxuan, Yang, Bin, Wang, Jindong, Pan, Shirui, Wen, Qingsong
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects
by: Zhang, Kexin, et al.
Published: (2023)

Foundation Models for Time Series Analysis: A Tutorial and Survey
by: Liang, Yuxuan, et al.
Published: (2024)

Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective
by: Hu, Jiaxi, et al.
Published: (2024)

Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
by: Jin, Ming, et al.
Published: (2023)

National Reference Measurement: What Can It Tell Us about Staffing?
by: Murfin, Marjorie E.
Published: (1983)

From Entanglement to Alignment: Representation Space Decomposition for Unsupervised Time Series Domain Adaptation
by: Cai, Rongyao, et al.
Published: (2025)

ShapeX: Shapelet-Driven Post Hoc Explanations for Time Series Classification Models
by: Huang, Bosong, et al.
Published: (2025)

A Survey on Diffusion Models for Time Series and Spatio-Temporal Data
by: Yang, Yiyuan, et al.
Published: (2024)

What Can "Marriage Announcements" Tell Us? A Content Analysis of News Articles on Library-Press Collaboration
by: Zhang, Mei, et al.
Published: (2021)

What Radio Waves Tell Us about Sleep
by: He, Hao, et al.
Published: (2024)

What Citations Tell Us about Canadian Research.
by: Garfield, Eugene
Published: (1993)

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting
by: Zhong, Siru, et al.
Published: (2025)

Breaking the Regional Barrier: Inductive Semantic Topology Learning for Worldwide Air Quality Forecasting
by: Cui, Zhiqing, et al.
Published: (2026)

What Students' E-Mails Tell Us about Their Needs
by: Al-Jarf, Reima
Published: (2009)

What Research Tells Us about the Importance of School Libraries.
by: Lance, Keith Curry
Published: (2002)

TimeOmni-1: Incentivizing Complex Reasoning with Time Series in Large Language Models
by: Guan, Tong, et al.
Published: (2025)

What Can String Probability Tell Us About Grammaticality?
by: Hu, Jennifer, et al.
Published: (2025)

Continuous Evolution Pool: Taming Recurring Concept Drift in Online Time Series Forecasting
by: Zhan, Tianxiang, et al.
Published: (2025)

What Research Tells Us about Children's Use of Information Media.
by: Fasick, Adele M.
Published: (1992)

Sonar-TS: Search-Then-Verify Natural Language Querying for Time Series Databases
by: Tan, Zhao, et al.
Published: (2026)

What Artificial Neural Networks Can Tell Us About Human Language Acquisition
by: Warstadt, Alex, et al.
Published: (2022)

Parallel Complex Diffusion for Scalable Time Series Generation
by: Cai, Rongyao, et al.
Published: (2026)

Towards Neural Scaling Laws for Time Series Foundation Models
by: Yao, Qingren, et al.
Published: (2024)

Toward Physics-guided Time Series Embedding
by: Hu, Jiaxi, et al.
Published: (2024)

Can Competition Enhance the Proficiency of Agents Powered by Large Language Models in the Realm of News-driven Time Series Forecasting?
by: Zhang, Yuxuan, et al.
Published: (2025)

What Doctoral Student Motivation Tells Us about the Future of LIS Education
by: Hands, Africa S.
Published: (2018)

What Does Softmax Probability Tell Us about Classifiers Ranking Across Diverse Test Conditions?
by: Tu, Weijie, et al.
Published: (2024)

Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey
by: Liang, Yuxuan, et al.
Published: (2025)

LeapTS: Rethinking Time Series Forecasting as Adaptive Multi-Horizon Scheduling
by: Pan, Sheng, et al.
Published: (2026)

What Children Tell Us about Recreational Reading through Summer Library Programs.
by: Fuhry, Mary Lou
Published: (1992)

Time Series Analysis for Education: Methods, Applications, and Future Directions
by: Mao, Shengzhong, et al.
Published: (2024)

Am I on the Right Track? What Can Predicted Query Performance Tell Us about the Search Behaviour of Agentic RAG
by: Tian, Fangzheng, et al.
Published: (2025)

What Can Student-AI Dialogues Tell Us About Students' Self-Regulated Learning? An exploratory framework
by: Zhang, Long, et al.
Published: (2025)

Lepton Magnetic Moments: What They Tell Us
by: Jegerlehner, Fred
Published: (2026)

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
by: Jin, Ming, et al.
Published: (2023)

What Can DKIST/DL-NIRSP Tell Us About Quiet-Sun Magnetism?
by: Liu, Jiayi, et al.
Published: (2024)

Undergraduate Use of Government Information: What Citation Studies Tell Us about Instruction Strategies
by: Brunvand, Amy, et al.
Published: (2008)

Reaching Maximum Occupancy: What the Numbers Tell Us about Space and Ways to Improve Services
by: Torres, Ana, et al.
Published: (2019)

Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecasting
by: Jin, Ming, et al.
Published: (2023)

Time-SSM: Simplifying and Unifying State Space Models for Time Series Forecasting
by: Hu, Jiaxi, et al.
Published: (2024)