Patent ID: 11928616
Assignee: KINAXIS INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A computer-implemented method for generation of an automated forecast for a subject based on one or more input parameters, the method executed on at least one processing unit, the subject associated with an end node of a categorical hierarchy, the method comprising:
receiving historical data associated with the subject;
determining that the historical data associated with the subject is insufficient based on a feasibility of building a subject machine learning model to generate a forecast with a predetermined level of accuracy using the historical data associated with the subject;
obtaining historical data associated with at least one ancestor node of the hierarchy that is one or more node levels above the subject and/or historical data associated with similar subjects to the subject;
building the subject machine learning model, comprising a plurality of machine learning models including both a Random Forest model and a Linear Regression model, and training the subject machine learning model using the historical data associated with the subject in combination with the historical data associated with the at least one ancestor node of the categorical hierarchy and/or the similar subjects, the at least one ancestor node selected by successively determining the sufficiency of the historical data until there is sufficient historical data or a root node is reached, and the similar subjects manually selected by a user or automatically selected based on a metric of similarity comprising at least one of hierarchy, history of relatedness, and other characteristics of similarity;
building a meta-machine learning model to generate the forecast by combining predictions from each of the plurality of machine learning models by stacking the plurality of machine learning models, the meta-machine learning model trained using the predictions from each of the machine learning models as features to the meta-machine learning model;
receiving the one or more input parameters;
generating a forecast for the subject using the meta-machine learning model provided with the one or more input parameters;
outputting the forecast.