Today we are launching several new features for DeepAR in Amazon SageMaker. DeepAR is a supervised machine learning algorithm for time series prediction, or forecasting, that uses recurrent neural networks (RNNs) to produce probabilistic forecasts.
Since its launch, the algorithm has been used for a variety of use cases. We are excited to give developers access to new features: support for missing values, user-provided time series features, multiple categorical features, and generalized frequencies.
Forecasting can improve business processes across many industries. This makes forecasting an ideal entry point into the world of automation and optimization using machine learning (ML) and Artificial Intelligence (AI).
Whether you optimize the supply chain through better product demand forecasts, allocate computing resources more effectively by predicting web server traffic, or save lives by staffing hospitals to meet patient needs, there are few domains where forecasting does not quickly return its investment. At Amazon, we use forecasting to drive automated business decision making in a variety of domains.
Some of these include forecasting the product and labor demand in our fulfillment centers or forecasting capacity for AWS services. In this blog post we’ll give you a quick overview of the new features of the DeepAR algorithm that are now available. Read more from aws.amazon.com…
thumbnail courtesy of amazon.com