Amazon SageMaker Canvas supports quicker set up of time-series forecasting models

[Amazon SageMaker Canvas](/sagemaker/canvas/) now supports quicker set up of time-series forecasting models with simplified administration of required permissions. SageMaker Canvas is a visual point-and-click service that enables business analysts to generate accurate machine learning (ML) models for insights and predictions on their own — without requiring any machine learning experience or having to write a single line of code. SageMaker Canvas supports a number of use cases, including time-series forecasting used for inventory management in retail, demand planning in manufacturing, workforce and guest planning in travel and hospitality, revenue prediction in finance, and many other functions where highly-accurate forecasts are important. As an example, time-series forecasting allows retailers to predict future sales demand and plan for inventory levels, logistics, and marketing campaigns. Time-series forecasting models in SageMaker Canvas use advanced technologies such as Amazon Forecast to ensemble model statistical and machine learning algorithms, and deliver highly accurate forecasts. Previously, setting up time-series forecasting models in Canvas required IT administrators to manually configure Identity and Access Management (IAM) policies in the AWS management console and add explicit permissions for time-series forecasting. Starting today, time-series forecasting permissions are enabled by default making it intuitive for IT administrators while setting up a SageMaker domain. This allows Canvas users to quickly set up time-series forecasting models, train these models, and generate predictions to achieve effective business outcomes. This new capability is now available in all AWS regions where SageMaker Canvas is supported. To learn more and get started, see the [documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/canvas-set-up-forecast.html) and the Canvas [product page](/sagemaker/canvas/).