Awesome-TimeSeries-SpatioTemporal-LM-LLM and LLM4TS

Maintenance 0/25
Adoption 10/25
Maturity 8/25
Community 18/25
Maintenance 0/25
Adoption 10/25
Maturity 8/25
Community 16/25
Stars: 1,203
Forks: 90
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Stars: 560
Forks: 47
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No License Stale 6m No Package No Dependents

About Awesome-TimeSeries-SpatioTemporal-LM-LLM

qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM

A professional list on Large (Language) Models and Foundation Models (LLM, LM, FM) for Time Series, Spatiotemporal, and Event Data.

This list compiles research on using Large Language Models (LLMs) and Foundation Models for analyzing various types of temporal data, including time series, spatio-temporal, and event data. It provides a curated collection of papers, code, and datasets, acting as a resource for staying updated on advanced techniques. Researchers and data scientists working with complex real-world temporal datasets will find this useful for their modeling and prediction tasks.

time-series-forecasting spatio-temporal-analysis event-prediction predictive-modeling financial-modeling

About LLM4TS

liaoyuhua/LLM4TS

Large Language & Foundation Models for Time Series.

This project helps data scientists and machine learning engineers stay updated on the latest research in applying large language models (LLMs) and other foundation models to time series data. It curates a collection of academic papers and code implementations, allowing practitioners to explore state-of-the-art techniques for tasks like time series forecasting. The input is academic research, and the output is a deeper understanding and access to resources for building advanced time series models.

time-series-forecasting AI-research predictive-analytics machine-learning-engineering data-science

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