Improving Forecast Accuracy with Machine Learning

The Improving Forecast Accuracy with Machine Learning solution generates, tests, compares, and iterates on Amazon Forecast forecasts. The 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. The solution outputs probabilistic predictions at three default quantiles to address sensitivity to over and under-provisioning (shown in the sample forecasting graph to the right). You can also customize the forecasting predictions to meet your needs. 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 to over and under-provisioning, with the 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.