Time-LLM offers a framework for repurposing Large Language Models (LLMs) for time series forecasting tasks. It targets researchers and practitioners seeking to leverage LLM capabilities for predictive analysis by treating time series data as a form of language. The primary benefit is enabling off-the-shelf LLMs to perform time series forecasting without altering their core architecture.

How It Works

Time-LLM reprograms LLMs by converting time series data into text-based representations that LLMs can process. It augments this with declarative prompts, incorporating domain knowledge and task instructions to guide the LLM's reasoning process. This approach allows existing LLMs to be adapted for forecasting tasks, treating them as a novel "language task."

Quick Start & Requirements

• Install dependencies via pip install -r requirements.txt.
• Requires Python 3.11, PyTorch 2.2.2, and specific versions of libraries like transformers (4.31.0) and deepspeed (0.14.0).
• Datasets need to be downloaded from a provided Google Drive link and placed in the ./dataset directory.
• Example execution scripts are available in the ./scripts folder (e.g., ./scripts/TimeLLM_ETTh1.sh).
• Official paper, YouTube talks, and Medium blog are linked for further details.

Highlighted Details

• Official implementation for the ICLR 2024 paper "Time-LLM: Time Series Forecasting by Reprogramming Large Language Models."
• Supports general time series forecasting by keeping LLM backbones intact.
• Defaults to Llama-7B and is compatible with GPT-2 and BERT by adjusting model and dimension parameters.
• Adopted by XiMou Optimization Technology Co., Ltd. for solar, wind, and weather forecasting.

Maintenance & Community

• Project has been included in NeuralForecast.
• Open to contributions and suggestions.
• Links to multiple Chinese interpretation articles are provided.

Licensing & Compatibility

• The repository does not explicitly state a license.
• Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The project relies on specific versions of dependencies, which may require careful environment management. The absence of an explicit license raises questions about commercial use and redistribution.