Amazon Forecast
Developer Guide

This is prerelease documentation for a service in preview release. It is subject to change.

The Amazon Forecast API underwent significant changes during the scheduled maintenance that occurred from 7/22/19 through 7/23/19. As part of maintenance, all Forecast resources (datasets, predictors, and forecasts) were deleted.

To use the new APIs, you must install new JSON service files by following Steps 3 through 6 in Set Up the AWS CLI and modify your existing code to reflect the syntax changes. We have revised our GitHub notebooks to help you understand how to interact with the new APIs. If you use only the console, no changes are necessary.

For a table describing the API changes, see API Changes in Amazon Forecast.

If you have questions, contact .

How Amazon Forecast Works

When creating forecasting projects in Amazon Forecast, you work with the following resources:

  • Datasets and Dataset Groups – You create an Amazon Forecast dataset and import your training data. You create a dataset group and add related datasets. Amazon Forecast algorithms use your datasets to train custom forecasting models, called predictors.

  • Predictors – Predictors are custom models trained on your data. You can train a predictor by choosing a prebuilt recipe (which provides an algorithm and the data featurization associated with the algorithm) or, by choosing the AutoML option, ask Amazon Forecast to pick the best algorithm for you.

  • Forecasts – You can generate forecasts for your time-series data and export the forecasts to your own Amazon Simple Storage Service (Amazon S3) bucket.