Choose this use-case if you wish to develop a model that analyzes historical demand data for a product or service to predict future demand patterns. Demand forecasting may be useful in inventory management, supply chain efficiency, budget planning, or strategic decision-making. Given a dataset of historical demand data for your good or service and an optional secondary dataset of all relevant item attributes such as product information, promotional history, or other external factors, you can generate a model that generates an accurate and comprehensive demand forecast.
Dataset and Feature Group RequirementsThis section specifies the Datasets / Feature Groups requirements to successfully train a Demand Forecasting model. Feature requirements include recommendations on additional datasets that might enhance model performance.
Training Models - Training Options and MetricsThis section describes all the available model training options that can be used to create a Demand Forecasting model. You can utilize the metric explanations to better understand how they measure the performance of the model you trained.
Evaluating PredictionsThis section contains a quick model evaluation guide that helps you understand how well your model is performing.
Prediction APIThis section discusses the prediction API method so that you could properly generate predictions from the model you deployed.