Uni2ts

Abstract

1. Introduction

Uni2TS is a PyTorch based library for research and applications related to Time Series Forecasting. It provides a unified framework for large-scale pre-training, fine-tuning, inference, and evaluation of Universal Time Series Transformers.

Related reading: Moirai Paper, Moirai Salesforce Blog, Moirai-MoE Paper, Moirai-MoE Salesforce Blog, Moirai-MoE AI Horizon Forecast Blog, Moirai-MoE Jiqizhixin Blog.

2. Objective

Unified Training of Universal Time Series Forecasting Transformers

This project demonstrates how Jupyter Notebook, 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 with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/SalesforceAIResearch/uni2ts.git
cd uni2ts

1. Clone repository:

git clone https://github.com/SalesforceAIResearch/uni2ts.git
cd uni2ts

virtualenv venv
. venv/bin/activate

pip install -e '.[notebook]'

touch .env

Full 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.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by SalesforceAIResearch 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.