Dynamic feature selection for model generation

A non-transitory computer-readable medium having instructions stored thereon that, when executed by a-one or more processors, cause the processors to generate a model of demand of a product that comprises an optimized feature set, the generating comprising:

• receiving sales history for the product;
• receiving a set of relevant features for the product and designating a subset of the relevant features as mandatory features;
• from the sales history, forming a training dataset and a validation dataset;
• randomly selecting, from the set of relevant features, one or more optional features to create a set of optional features and including the set of optional features with the mandatory features to create a first feature test set;
• training an algorithm using the training dataset and the first feature test set to generate a trained algorithm;
• calculating an early stopping metric using the trained algorithm and the validation dataset;
• repeating the randomly selecting to create a revised feature test set, the revised feature test set comprising the mandatory feature set and a different set of optional features than the first feature test set;
• repeating the training and calculating using the revised feature test set instead of the first feature test set;
• and
• repeating the repeating the randomly selecting and the repeating the training and calculating until the early stopping metric is below a predefined threshold;
• wherein when the early stopping metric is below the predefined threshold, the revised feature test set is the optimized feature set.

A retail sales forecasting system that forecasts demand for a product using an optimized feature set, the system comprising:

• one or morea processors coupled to a storage device that implements a demand forecasting module comprising:
• receiving sales history for the product;
• receiving a set of relevant features for the product and designating a subset of the relevant features as mandatory features;
• from the sales history, forming a training dataset and a validation dataset;
• randomly selecting, from the set of relevant features, one or more optional features to create a set of optional features and including the set of selected optional features with the mandatory features to create a first feature test set;
• training an algorithm using the training dataset and the first feature test set to generate a trained algorithm;
• calculating an early stopping metric using the trained algorithm and the validation dataset;
• repeating the randomly selecting to create a revised feature test set, the revised feature test set comprising the mandatory feature set and a different set of optional features than the first feature test set;
• repeating the training and calculating using the revised feature test set instead of the first feature test set;
• and
• repeating the repeating the randomly selecting and the repeating the training and calculating until the early stopping metric is below a predefined threshold;
• wherein when the early stopping metric is below the predefined threshold, the revised feature test set is the optimized feature set.