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.
翻译:我们介绍Darts,这是Python机器学习图书馆,是一个时间序列,重点是预测。Darts提供了各种模型,从ARIMA等经典到最先进的深层神经网络。图书馆的重点是提供现代机器学习功能,如支持多层面系列、多系列元学习、大型数据集培训、纳入外部数据、组合模型和为概率预测提供大量支持。与此同时,API的设计非常谨慎,以方便用户和方便使用。例如,所有模型都可以使用与Scikit-learn相似的相近()/预科(predict ) 。