Darts: User-Friendly Modern Machine Learning for Time Series
This library addresses the need for accessible and comprehensive time series analysis tools for data scientists and researchers, though it is incremental as it builds on existing methods.
The authors introduced Darts, a Python library for time series forecasting that integrates classical and deep learning models, emphasizing user-friendly design and modern functionalities like multidimensional series support and probabilistic forecasting.
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.