Improving Forecast Accuracy with Machine Learning

Generate, test, compare, and iterate with Amazon Forecast

Improving Forecast Accuracy with Machine Learning will be retired on December 31, 2023. After that time, all existing deployments will continue to work and customers will retain full control of their environments and data, however, the solution will no longer be supported or maintained.

If you are considering a new deployment, please refer to the Automate the deployment of an Amazon Forecast time-series forecasting model blog post.


The Improving Forecast Accuracy with Machine Learning solution automatically produces forecasts and generates visualization dashboards for Amazon QuickSight or Amazon SageMaker Jupyter Notebooks—providing a quick, easy, drag-and-drop interface that displays time series input and forecasted output. Forecasting can be applied to predict retail inventory demand, supply-chain planning, workforce status, web traffic forecasting, and more.

Forecasts can be compared across dimensions (for example, retail store location) or item-level metadata (for example, product brand, size, and color). You can use this data for the following:

  • Optimize existing forecasts - Save time and retain compatibility with your legacy tools, or gain insight into over- and under-provisioning, with the median (P50) forecast.
  • Meet variable customer demand - Provide high levels of customer satisfaction with the P90 forecast, where the true value is expected to be lower than the predicted value 90% of the time.
  • Avoid over-provisioning - Save on costs and avoid over-provisioning with the P10 forecast, where the true future demand value is expected to be lower than the predicted value only 10% of the time.



Automated processes

Streamline the process of ingesting, modeling, and forecasting multiple experiments through the automation of Amazon Forecast.

Secure deployment

Provide a secure one-click deployment using an AWS CloudFormation template developed with the AWS Well-Architected Framework methodologies.

Proactive monitoring

Easily monitor forecasts by emailing users when successes and failures occur. 

Automated visualization

Facilitate collaboration and experimentation by combining your input data and forecast output in an Amazon QuickSight Analysis or Jupyter Notebook.

Technical details

The AWS CloudFormation template deploys the resources required to automate your Amazon Forecast usage and deployments. Based on the capabilities of the solution, the architecture is divided into three parts: data preparation, forecasting, and data visualization. The template includes the following components:

About this deployment
Est. deployment time
5 mins
Estimated cost
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