LLM4TS

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LLM4TS is a resource collection for applying Large Language Models (LLMs) and Foundation Models (FMs) to time series data. It provides papers and code examples to facilitate research and application in time series analysis.

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API

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Skill level

Technical

About

What is LLM4TS?

LLM4TS is a comprehensive collection of resources specifically curated for the application of Large Language Models (LLMs) and Foundation Models (FMs) in the domain of time series data. This project serves as a valuable hub for researchers and practitioners, offering a compilation of relevant academic papers and practical code examples. Its primary objective is to enhance understanding and utilization of LLMs for various time series analysis tasks, thereby accelerating advancements in this specialized field.

Best used for

Learning and researching the application of Large Language Models and Foundation Models to time series data.

Common actions

Learn LLM for time series

Research time series AI

Find code examples

Understand Foundation Models

Apply AI to time series

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Capabilities

Key features

LLM for time series
Foundation Models resources
Papers collection
Code examples
Time series analysis

Target Audience

ResearchersData ScientistsMachine Learning EngineersAcademics

Integrations

Not yet documented

Pricing & Plans

unknown

Free

FAQs

Is LLM4TS a tool I can use directly for time series analysis, or is it a resource hub?

LLM4TS is primarily a comprehensive resource hub, not a direct analysis tool. It provides curated academic papers and code examples to help researchers and practitioners understand and apply LLMs to time series data, rather than offering a ready-to-use analysis platform.

Does LLM4TS offer guidance on which specific LLMs or FMs are best suited for different time series tasks?

While LLM4TS compiles papers and code examples, it focuses on showcasing the application of various LLMs/FMs to time series. It serves as a starting point for exploration, allowing users to review different approaches and determine suitability for their specific tasks based on the provided resources.

Are the code examples provided in LLM4TS ready-to-run, or do they require significant setup and adaptation?

The code examples in LLM4TS are intended to be practical demonstrations. While they aim to be illustrative, users should expect to adapt them to their specific datasets and environments. The examples serve as a foundation for building and experimenting with LLM-based time series solutions.

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