Patent ID: 11922440
Assignee: ORACLE INTERNATIONAL CORPORATION
Field: IT methods for management (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A method of forecasting demand of an item, the method comprising:
receiving historical sales data for the item for a plurality of past time periods, the historical sales data comprising a plurality of features of the item that impact the demand of the item, the plurality of features comprising at least two or more different types comprised from at least two of price, seasonality, brand, promotions, size or color;
defining a plurality of different feature sets, each feature set comprised of one or more features of the plurality of features from the historical sales data;
using the plurality of different feature sets as inputs to one or more different algorithms to generate a plurality of different machine learning models, each of the different machine learning models corresponding to each of the different feature sets, each of the plurality of machine learning models generated and configured based on the types of features from each of the different feature sets, and each different feature set having a different number of features of the plurality of features and/or different types of features of the plurality of features;
wherein the different machine learning models each have a same algorithm, each same algorithm having a different number of features corresponding to one of the different feature sets, or the different machine learning models each have the same algorithm, each same algorithm having a different number of nodes corresponding to one of the different feature sets, or the different machine learning models each have a different algorithm, each different algorithm corresponding to one of the different feature sets, the different algorithms comprising at least two of linear regression, Support Vector Machine, or Artificial Neural Networks;
after generating each of the different machine learning models, training each of the different machine learning models with a same training set to generate a plurality of trained models, the training set generated from the historical sales data and comprising a plurality of values, each of the values corresponding to one of the types of features;
using each of the trained models, generating a plurality of past demand forecasts for each of some or all of the past time periods and generating a plurality of future demand forecasts for each of future time periods;
determining a root-mean-square error (RMSE) for each of the past demand forecasts;
based on the RMSE, determining a weight for each of the trained models and normalizing each weight; and
generating a final demand forecast for the item for each of the future time periods by combining a weighted value for each trained model.