Forecasting platforms & libraries · Amazon Web Services (open source)

AutoGluon

Open-source AutoML library from AWS that automates model selection and ensembling, including a time-series forecasting module.

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AutoGluon is an open-source AutoML library covering tabular, text, image and multimodal tasks, with a dedicated AutoGluon-TimeSeries module for forecasting. Rather than requiring the user to pick a model family, it trains and ensembles a pool of statistical, gradient-boosting and deep-learning forecasters (including its own pretrained Chronos foundation model) and automatically selects or blends the best performers on held-out data. The trade-off for that automation is less manual control than a library like Darts or sktime, but a much lower barrier to a strong baseline: a few lines of code can produce a tuned ensemble forecast without model-selection expertise. AWS has pointed users away from the now-deprecated Amazon Forecast service toward AutoGluon-TimeSeries (often paired with SageMaker) as its recommended forecasting path.

At a glance

Vendor Amazon Web Services (open source)
Pricing model Open source + paid options
Free tier Yes
Deployment Cloud, Self-hosted
Open source Yes (Apache-2.0)
Best for Teams that want a strong automated forecasting baseline without manually selecting or tuning models.

Pricing

Free and open source; compute costs apply if run on cloud infrastructure such as SageMaker.

Pricing has not been verified yet — see the vendor's site.

Features

  • Automated model selection and ensembling for forecasting
  • Chronos pretrained foundation model included
  • Statistical, gradient-boosting and deep-learning forecasters in one pool
  • Minimal-code API for a tuned baseline forecast
  • Covariate and static-feature support
  • Also covers tabular, text, image and multimodal AutoML
  • Integrates with Amazon SageMaker for managed training

Integrations

Profile last reviewed September 21, 2026

Alternatives

GluonTS Open-source Python toolkit from AWS for building and evaluating probabilistic deep-learning forecasting models. Nixtla Open-source statistical/ML forecasting libraries plus TimeGPT, a hosted foundation model for time series. PyTorch Forecasting Open-source deep-learning forecasting library built on PyTorch and PyTorch Lightning for panel/multi-series data.

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Terms to know

Time series forecasting Predicting future values of a metric using patterns found in its own historical sequence over time. Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Predictive analytics Analytics that uses historical data and statistical models to estimate what is likely to happen next. AutoML (automated machine learning) Tooling that automates parts of building a machine learning model, such as feature selection, algorithm choice and hyperparameter tuning. Large language model (LLM) A neural network trained on vast amounts of text to predict and generate language, the technology behind most modern AI assistants.

Related guides

How to choose a forecasting platform or library Pick a time-series forecasting library by how many series you're forecasting and how much modeling control your team wants.