Amazon Forecast
Developer Guide

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

The Amazon Forecast API will undergo significant changes during scheduled maintenance occurring from 10 AM on 7/22/19 until 10 AM on 7/23/19. During maintenance, access to the Forecast APIs and console might be interrupted.

After 7/22/19, your Forecast resources (datasets, predictors, and forecasts) will no longer be available. However, you can save your forecasts for future use. We recommend using the CreateForecastExportJob API to save them to your S3 bucket before 7/22/19.

After maintenance concludes, before using the APIs, you must download a new SDK and modify your existing code to reflect the syntax changes. If you use only the console, you won’t need to make any changes.

We will provide new API documentation before scheduled maintenance begins. If you have questions, contact

Exponential Smoothing (ETS) Recipe

Exponential Smoothing (ETS) is a commonly-used local statistical algorithm for time-series forecasting. The Amazon Forecast ETS recipe calls the ets function in the Package 'forecast' of the Comprehensive R Archive Network (CRAN).

How ETS Works

The ETS algorithm is especially useful for datasets with seasonality and other prior assumptions about the data. ETS computes a weighted average over all observations in the input time series dataset as its prediction. The weights are exponentially decreasing over time, rather than the constant weights in simple moving average methods. The weights are dependent on a constant parameter, which is known as the smoothing parameter.

ETS Hyperparameters and Tuning

For information about ETS hyperparameters and tuning, see the ets function documentation in the Package 'forecast' of CRAN.