Hierarchical Bayesian Method

Hierarchical Bayesian method is useful for retailers wanting to optimize regular price of items. This is a Bayesian-based approach that uses regular price as one of the features to generate forecast. Typically, the data requirement for this method is more than one year of sales and prices.

The stages for Hierarchical Bayesian method:

1. Data Preparation defines the levels at which data is aggregated on merchandise, location/price zone, customer segment, and time dimensions and the duration of the data used for parameter estimation.

2. Preprocessing filters the historical data and makes the first determination of item eligibility.

3. Training enables the user to select the configurations and features that can be used for model training. Note that some features are mandatory.

4. Forecast Generation generates the ML models that can be later used by applications consuming demand forecasts.