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

Claim 8:
9. A system for generation of an automated forecast for a subject based on one or more input parameters, the subject associated with an end node of a categorical hierarchy, the system comprising at least one processing unit and a data storage, the at least one processing unit in communication with the data storage and configured to execute:
an analysis module to receive historical data associated with the subject, and determine 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; and
a forecasting module to:
obtain historical data associated with at least one ancestor node of the categorical hierarchy that is one or more node levels above the subject and/or historical data associated with similar subjects to the subject;
build the subject machine learning model, comprising a plurality of machine learning models including both a Random Forest model and a Linear Regression model, and train the subject machine learning model using the historical data associated with the subject in combination with the of 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;
build 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;
receive the one or more input parameters;
generate a forecast for the subject using meta-machine learning model provided with the one or more input parameters;
output the forecast.