Forecasting extrapolates the values of a time series into the future, and is crucial to optimize core operations for many businesses and organizations. Building machine learning (ML)-based forecasting applications presents a challenge though, due to non-stationary data and large numbers of time series. As there is no single dominating approach to forecasting, forecasting systems have to support a wide variety of approaches, ranging from deep learning-based methods to classical methods built on probabilistic modeling.
We revisit our earlier work on a monolithic platform for forecasting from VLDB 2017, and describe how we evolved it into a modern forecasting stack consisting of several layers that support a wide range of forecasting needs and automate common tasks like model selection. This stack leverages our open source forecasting libraries GluonTS and AutoGluon-TimeSeries, the scalable ML platform SageMaker, and forms the basis of the no-code forecasting solutions (SageMaker Canvas and Amazon Forecast), available in the Amazon Web Services cloud. We give insights into the predictive performance of our stack and discuss learnings from using it to provision resources for the cloud database services DynamoDB, Redshift and Athena.
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