Darts：时间序列的User-Friendly现代机器学习
Darts: User-Friendly Modern Machine Learning for Time Series
摘要：
我们介绍了Darts，一个用于时间序列的Python机器学习库，重点是预测。Darts提供了各种模型，从经典的ARIMA到最先进的深度神经网络。该库的重点是提供现代机器学习功能，例如支持多维系列，多系列元学习，大型数据集训练，整合外部数据，集成模型，并为概率预测提供丰富的支持。与此同时，API的设计非常小心，使其用户友好和易于使用。例如，所有模型都可以使用fit（）/predict（）来使用，类似于scikit-learn。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
