Abstract
Time LLM is an open-source Data Science project. [ICLR 2024] Official implementation of " 🦙 Time-LLM: Time Series Forecasting by Reprogramming Large Language Models". Time-LLM is a reprogramming framework to repurpose LLMs for general time series forecasting with the backbone language models kept intact. Notably, we show that time series analysis (e.g., forecasting) can be cast as yet another "language task" that can be effectively tackled by an off-the-shelf LLM. It is built using Python, Deep Learning, Machine Learning. The complete source code is publicly available on GitHub under the Apache License 2.0, making it a useful reference for students building a Data Science mini project or final-year project.
1. Introduction
Time-LLM is a reprogramming framework to repurpose LLMs for general time series forecasting with the backbone language models kept intact. Notably, we show that time series analysis (e.g., forecasting) can be cast as yet another "language task" that can be effectively tackled by an off-the-shelf LLM.
(ICLR'24) Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
News (Oct. 2025): Time-LLM has been cited 1,000 times in the past two years! We are deeply grateful to the community for the incredible support along the journey.
2. Objective
[ICLR 2024] Official implementation of " 🦙 Time-LLM: Time Series Forecasting by Reprogramming Large Language Models"
This project demonstrates how Python, Deep Learning, Machine Learning can be applied to a real-world Data Science problem.
4. Technology Stack
5. System Requirements
General requirements for this technology stack — check the README for exact versions.
- Python 3.8 or later
- pip / virtualenv for dependencies
- VS Code, PyCharm or Jupyter Notebook
- Git (to clone the repository)
6. Installation & Setup
git clone https://github.com/KimMeen/Time-LLM.git
cd Time-LLMFull setup instructions are in the project README.
7. Future Enhancements
Suggested extensions you can add to make this your own project.
- Turn the analysis into an interactive dashboard
- Automate data refresh with a scheduled job
- Add a predictive model on top of the analysis
8. Viva / Review Questions
Common questions examiners ask for projects in this domain.
- What is the source of the dataset and how was missing data handled?
- Which exploratory analysis steps revealed the most useful insight?
- Why were these particular charts chosen to present the data?
- Which statistical or ML technique supports the conclusions?
- How could the analysis be automated or refreshed with new data?
9. Source Code & License
This project is developed by KimMeen and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.
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