{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/darts-user-friendly-modern-machine-learning","title":"Darts: User-Friendly Modern Machine Learning for Time Series","arxiv_id":"2110.03224","date":"2021-10-07","proceeding":null,"authors":["Julien Herzen","Francesco Lässig","Samuele Giuliano Piazzetta","Thomas Neuer","Léo Tafti","Guillaume Raille","Tomas Van Pottelbergh","Marek Pasieka","Andrzej Skrodzki","Nicolas Huguenin","Maxime Dumonal","Jan Kościsz","Dennis Bader","Frédérick Gusset","Mounir Benheddi","Camila Williamson","Michal Kosinski","Matej Petrik","Gaël Grosch"],"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, meta-learning 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.","url_abs":"https://arxiv.org/abs/2110.03224v3","url_pdf":"https://arxiv.org/pdf/2110.03224v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"darts-user-friendly-modern-machine-learning","repo_url":"https://github.com/unit8co/darts","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"meta-learning","task_name":"Meta-Learning"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[{"method_slug":"darts","method_name":"DARTS"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2110.03224","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}