We introduce AutoGluon-TimeSeries - an open-source AutoML library for probabilistic time series forecasting. Focused on ease of use and robustness, AutoGluon-TimeSeries enables users to generate accurate point and quantile forecasts with just 3 lines of Python code. Built on the design philosophy of AutoGluon, AutoGluon-TimeSeries leverages ensembles of diverse forecasting models to deliver high accuracy within a short training time. AutoGluon-TimeSeries combines both conventional statistical models, machine-learning based forecasting approaches, and ensembling techniques. In our evaluation on 29 benchmark datasets, AutoGluon-TimeSeries demonstrates strong empirical performance, outperforming a range of forecasting methods in terms of both point and quantile forecast accuracy, and often even improving upon the best-in-hindsight combination of prior methods.

翻译：我们介绍了AutoGluon-TimeSeries——一个用于概率时间序列预测的开源自动机器学习库。该库注重易用性和鲁棒性，用户仅需3行Python代码即可生成精确的点预测和分位数预测。基于AutoGluon设计理念，AutoGluon-TimeSeries通过集成多种预测模型，在短训练时间内实现高精度。该库融合了传统统计模型、基于机器学习的预测方法以及集成技术。在29个基准数据集上的评估显示，AutoGluon-TimeSeries展现出强大的实证性能，在点预测和分位数预测精度上均优于多种预测方法，甚至常能超越多种先验方法事后最优组合的效果。