Darts: User-Friendly Modern Machine Learning for Time Series
Abstract
We present Darts, a Python machine learning library for time series, with a focus on forecasting. Darts offers a variety of models, from classics such as ARIMA to state-of-the-art deep neural networks. The emphasis of the library is on offering modern machine learning functionalities, such as supporting multidimensional series, fitting models on multiple series, training on large datasets, incorporating external data, ensembling models, and providing a rich support for probabilistic forecasting. At the same time, great care goes into the API design to make it user-friendly and easy to use. For instance, all models can be used using fit()/predict(), similar to scikit-learn.
Cite
Text
Herzen et al. "Darts: User-Friendly Modern Machine Learning for Time Series." Machine Learning Open Source Software, 2022.Markdown
[Herzen et al. "Darts: User-Friendly Modern Machine Learning for Time Series." Machine Learning Open Source Software, 2022.](https://mlanthology.org/mloss/2022/herzen2022jmlr-darts/)BibTeX
@article{herzen2022jmlr-darts,
title = {{Darts: User-Friendly Modern Machine Learning for Time Series}},
author = {Herzen, Julien and Lässig, Francesco and Piazzetta, Samuele Giuliano and Neuer, Thomas and Tafti, Léo and Raille, Guillaume and Van Pottelbergh, Tomas and Pasieka, Marek and Skrodzki, Andrzej and Huguenin, Nicolas and Dumonal, Maxime and Kościsz, Jan and Bader, Dennis and Gusset, Frédérick and Benheddi, Mounir and Williamson, Camila and Kosinski, Michal and Petrik, Matej and Grosch, Gaël},
journal = {Machine Learning Open Source Software},
year = {2022},
pages = {1-6},
volume = {23},
url = {https://mlanthology.org/mloss/2022/herzen2022jmlr-darts/}
}