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

Autoregressive Integrated Moving Average (ARIMA) Recipe

Autoregressive Integrated Moving Average (ARIMA) is a commonly-used local statistical algorithm for time-series forecasting. ARIMA captures standard temporal structures (patterned organizations of time) in the input dataset. The Amazon Forecast ARIMA recipe calls the Arima function in the Package 'forecast' of the Comprehensive R Archive Network (CRAN).

How ARIMA Works

The ARIMA algorithm is especially useful for datasets that can be mapped to stationary time series. The statistical properties of stationary time series, such as autocorrelations, are independent of time. Datasets with stationary time series usually contain a combination of signal and noise. The signal may exhibit a pattern of sinusoidal oscillation or have a seasonal component. ARIMA acts like a filter to separate the signal from the noise, and then extrapolates the signal in the future to make predictions.

ARIMA Hyperparameters and Tuning

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