AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting

TL;DR

AutoGluon-TimeSeries is an open-source AutoML library that simplifies probabilistic time series forecasting, achieving high accuracy with minimal user effort by leveraging diverse models and ensembling techniques.

Contribution

It introduces a user-friendly AutoML framework for probabilistic time series forecasting that combines statistical, machine learning, and ensembling methods for improved accuracy.

Findings

Outperforms existing forecasting methods on benchmark datasets.

Achieves high accuracy with just 3 lines of Python code.

Demonstrates robustness and efficiency in diverse scenarios.

Abstract

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.