uni2ts
Unified Training of Universal Time Series Forecasting Transformers
Fork A fork of SalesforceAIResearch/uni2ts; the README may describe the upstream project.
Overview
Uni2TS is a PyTorch based library for research and applications related to Time Series Transformers. This library aims to provide a unified solution to large-scale pre-training of Universal Time Series Transformers. Uni2TS also provides tools for fine-tuning, inference, and evaluation for time series forecasting.
Let's see a simple example on how to use Uni2TS to make zero-shot forecasts from a pre-trained model. We first load our data using pandas, in the form of a wide DataFrame. Uni2TS relies on GluonTS for inference as it provides many convenience functions for time series forecasting, such as splitting a dataset into a train/test split and performing rolling evaluations, as demonstrated below.
See the example folder for more examples on common tasks, e.g. visualizing forecasts, predicting from pandas DataFrame, etc.
From the project’s README on GitHub.
At a glance
| Repository | Kentucky-Open-Science/uni2ts |
|---|---|
| Research area | Forks of other projects |
| Primary language | None detected |
| Languages | Jupyter Notebook 71.4%, Python 27.9%, Shell 0.7% |
| License | Apache-2.0 |
| Stars / forks | 0 / 0 |
| Open issues and pull requests | 1 |
| Created | 2024-09-05 |
| Last push | 2026-04-21 |
| Default branch | main |
| Forked from | SalesforceAIResearch/uni2ts — Unified Training of Universal Time Series Forecasting Transformers |
What the README covers
- 🎉 What's New
- ✅ TODO
- ⚙️ Installation
- 🏃 Getting Started
- 📔 Jupyter Notebook Examples
- 💻 Command Line Interface
- Fine-tuning
- Evaluation
- Pre-training
- 👀 Citing Uni2TS
Top contributors
- @gorold (63 commits)
- @chenghaoliu89 (12 commits)
- @liu-jc (10 commits)
- @zqiao11 (1 commit)
Get the code
git clone https://github.com/Kentucky-Open-Science/uni2ts.git