AWS Forecast and AWS SageMaker with DeepAR

AWS Forecast offers companies a machine learning-based demand forecasting solution that can significantly improve forecast accuracy over traditional methods. Through the use of various algorithms, including statistical algorithms and advanced deep learning models, AWS Forecast can adapt to a wide range of forecasting scenarios. The service automates much of the machine learning process, reducing the need for technical expertise, and enables the creation of customized forecasts, taking into account multiple variables such as historical data, external factors (e.g., weather conditions or economic events), and seasonality.

One of the most innovative features of AWS Forecast is the ability to perform “what-if” analysis. This feature allows companies to simulate different hypothetical scenarios and assess their impact on future forecasts. For example, it is possible to test how changes in pricing, marketing strategies or product offerings might affect future demand. “What-if” analysis thus becomes a valuable tool for strategic planning, enabling organizations to make more informed and responsive decisions with respect to changing market conditions.

Isolation with the DeepAR algorithm in SageMaker

AWS SageMaker offers more flexibility and control than AWS Forecast, making it an ideal choice for companies with more complex and specific needs. While AWS Forecast is a fully managed service that automates much of the predictive model building process, SageMaker allows developers and data scientists to build, train and deploy custom machine learning models from scratch. This approach allows for greater model customization, including the choice of specific algorithms, advanced hyperparametric optimization, and, if desired, the integration of proprietary models.

One of the most relevant algorithms, and already included in SageMaker builtins, for demand forecasting is DeepAR, a model based on recurrent neural networks. This algorithm allows accurate forecasting over multiple time series, exploiting the ability of RNNs to capture long-term sequential dependencies, it is particularly effective in scenarios where time series are characterized by strong seasonality or complex trends.

DeepAR is a powerful option for those who wish to use a deep learning model within AWS SageMaker to further optimize their forecasts.

With SageMaker, one can choose exactly what data and techniques to use, without being limited to a predefined range of algorithms. It also allows you to integrate open-source algorithms or create solutions that leverage deep learning frameworks such as TensorFlow or PyTorch, increasing your ability to develop highly sophisticated models such as convolutional neural networks (CNNs) or transformers.

For all those who require advanced predictive performance and want to experiment with custom algorithms, SageMaker represents a much more powerful solution than Forecast’s managed service, which is primarily optimized for quick and easy use but less customizable.

Article written by Walter Dal Mut.

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