This repository is a curated list of research papers, code, and datasets focusing on Large Language Models (LLMs) and Foundation Models (FMs) applied to time series, spatiotemporal, and event data. It serves as a comprehensive resource for researchers and practitioners in AI for Time Series (AI4TS) and related fields, aiming to systematically summarize recent advancements.

How It Works

The list categorizes research by data type (time series, spatiotemporal, event) and application domain (general, transportation, finance, healthcare, weather, video). It highlights papers that adapt LLMs for forecasting, representation learning, and analysis, as well as FMs specifically designed for temporal and spatiotemporal tasks, often leveraging Transformer architectures and self-supervised pre-training.

Quick Start & Requirements

This repository is a curated list of resources, not a runnable software package. It provides links to papers and official code repositories for specific models. Requirements vary per linked project.

Highlighted Details

• Comprehensive coverage of LLMs and FMs for time series, spatiotemporal, and event data.
• Includes links to papers, official code, and relevant datasets across various domains.
• Features a survey paper on "Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook."
• Organizes resources by application areas like finance, healthcare, and weather forecasting.

Maintenance & Community

The repository is actively maintained, with an invitation for community contributions via issues or pull requests for missed resources or errors. It cites a survey paper and provides a citation format for the repository itself.

Licensing & Compatibility

The repository itself does not have a specific license as it is a collection of links. Individual linked papers and code repositories will have their own licenses, which may vary.

Limitations & Caveats

This is a reference list and does not provide a unified framework or codebase. Users must consult individual linked projects for specific model implementations, requirements, and usage instructions.