학술논문
Darts: User-Friendly Modern Machine Learning for Time Series
Document Type
Working Paper
Author
Herzen, Julien; Lässig, Francesco; Piazzetta, Samuele Giuliano; Neuer, Thomas; Tafti, Léo; Raille, Guillaume; Van Pottelbergh, Tomas; Pasieka, Marek; Skrodzki, Andrzej; Huguenin, Nicolas; Dumonal, Maxime; Kościsz, Jan; Bader, Dennis; Gusset, Frédérick; Benheddi, Mounir; Williamson, Camila; Kosinski, Michal; Petrik, Matej; Grosch, Gaël
Source
Journal of Machine Learning Research 23 (2022) 1-6
Subject
Language
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.
Comment: Darts Github repository: https://github.com/unit8co/darts
Comment: Darts Github repository: https://github.com/unit8co/darts