AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting
This provides an easy-to-use tool for users needing robust time series forecasting, but it is incremental as it builds on existing AutoGluon and ensemble techniques.
The paper tackles the problem of probabilistic time series forecasting by introducing AutoGluon-TimeSeries, an AutoML library that enables accurate point and quantile forecasts with minimal code, and in evaluation on 29 benchmark datasets, it outperforms a range of existing methods and often improves upon the best prior combinations.
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.