The open-source landscape for time series is heating up.

This includes successful libraries such as Darts, GluonTS, and Nixtla.

Last year, Amazon built an extension of its AutoGluon library, which focuses on time-series — known as AutoGluon-Timeseries(AG-TS)[1].

AG-TS leverages the expertise of other libraries:

• From Amazon itself (AutoGluon and GluonTS).
• From Nixtla (StatsForecast and MLForecast).

And the best part: AG-TS has a user-friendly API— we can get predictions with only a few lines of code!

This article explores AG-TS and outlines its capabilities. We will also construct a simple project, utilizing the widely known Tourism dataset[2].

Let’s dive in

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What is AutoGluon-Timeseries

AutoGluon–TimeSeries is an AutoML time-series framework, focusing on probabilistic forecasting and leveraging ensembling.

AG–TS supports:

• Open-Source: The code is available on GitHub — the library is part of Amazon’s Autogluon suite.
• User-Friendly API: Load your data, and call the fit() and predict() methods.
• Model variety: Access to SOTA forecasting models across various categories, including statistical models, tree-based approaches, and deep-learning models.
• Powerful Ensembles: AG–TS incorporates automatic configurations for optimal ensembling, a key strategy in forecasting.
• Probabilistic output: The users can generate point forecasts, with optional prediction intervals.
• Superior Performance: AG–TS excels in an evaluation of 29 benchmark datasets, outperforming many forecasting methods.

Models of AG–TS