AutoGluon-TimeSeries: Every Time Series Forecasting Model In One Library | by Nikos Kafritsas | Jan, 2024

The open-source panorama for time sequence is heating up.

This comprises worthwhile libraries paying homage to Darts, GluonTS, and Nixtla.

Ultimate 12 months, Amazon constructed an extension of its AutoGluon library, which focuses on time-series — usually referred to as AutoGluon-Timeseries(AG-TS)[1].

AG-TS leverages the expertise of various libraries:

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

And the easiest half: AG-TS has a user-friendly API— we’ll get predictions with just some strains of code!

This textual content explores AG-TS and descriptions its capabilities. We are going to even assemble a simple mission, utilizing the extensively recognized Tourism dataset[2].

Let’s dive in

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

AG–TS helps:

• Open-Provide: The code is on the market on GitHub — the library is part of Amazon’s Autogluon suite.
• Client-Nice API: Load your data, and title the match() and predict() methods.
• Model choice: Entry to SOTA forecasting fashions all through quite a few lessons, along with statistical fashions, tree-based approaches, and deep-learning fashions.
• Extremely efficient Ensembles: AG–TS incorporates automated configurations for optimum ensembling, a key approach in forecasting.
• Probabilistic output: The purchasers can generate stage forecasts, with optionally obtainable prediction intervals.
• Superior Effectivity: AG–TS excels in an evaluation of 29 benchmark datasets, outperforming many forecasting methods.

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