uni2ts

Source code on GitHub.

README

Unified Training of Universal Time Series Forecasting Transformers

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.

🎉 What's New

* Jun 2024: Released Moirai-1.1-R model weights in small, base, and large.

* May 2024: The Uni2TS paper has been accepted to ICML 2024 as an Oral presentation!

* Mar 2024: Release of Uni2TS library, along with Moirai-1.0-R and LOTSA data!

✅ TODO

• [ ] Improve docstrings and documentation

[//]: # (- [ ] Support more pre-training paradigms)

[//]: # ( - [ ] (Non-)Contrastive learning)

[//]: # ( - [ ] Masked Autoencoder)

[//]: # ( - [ ] Next token prediction)

[//]: # (- [ ] Decoder Transformer)

[//]: # (- [ ] Data augmentations - down sampling, subsampling, aggregation)

⚙️ Installation

• Clone repository:
``shell git clone https://github.com/SalesforceAIResearch/uni2ts.git cd uni2ts `

2) Create virtual environment: `shell virtualenv venv . venv/bin/activate `

3) Build from source: `shell pip install -e '.[notebook]' `

4) Create a .env file: `shell touch .env `

🏃 Getting Started

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.

`python import torch import matplotlib.pyplot as plt import pandas as pd from gluonts.dataset.pandas import PandasDataset from gluonts.dataset.split import split from huggingface_hub import hf_hub_download

from uni2ts.eval_util.plot import plot_single from uni2ts.model.moirai import MoiraiForeca...