학술논문
Darts: User-Friendly Modern Machine Learning for Time Series.
Document Type
Article
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
Subject
*ARTIFICIAL neural networks
*TIME series analysis
*MULTIDIMENSIONAL databases
*MACHINE learning
*DEEP learning
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Language
ISSN
1532-4435
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 (Pedregosa et al., 2011). [ABSTRACT FROM AUTHOR]