  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.
