US 12,002,063 B2
Method and system for generating ensemble demand forecasts
Adam Morgan, Minneapolis, MN (US); Anand Das, Bangalore (IN); Samir H. Shah, San Jose, CA (US); and Phillip Yelland, Minneapolis, MN (US)
Assigned to Target Brands, Inc., Minneapolis, MN (US)
Filed by Target Brands, Inc., Minneapolis, MN (US)
Filed on Jun. 27, 2022, as Appl. No. 17/850,640.
Application 17/850,640 is a continuation of application No. 16/172,575, filed on Oct. 26, 2018, granted, now 11,373,199.
Prior Publication US 2023/0069403 A1, Mar. 2, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 30/0202 (2023.01); G06N 3/044 (2023.01); G06Q 10/087 (2023.01)
CPC G06Q 30/0202 (2013.01) [G06N 3/044 (2023.01); G06Q 10/087 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system for forecasting demand for a plurality of items, the system comprising:
a common data preparation computing subsystem including a standard data store, processor, and memory communicatively coupled to the processor, the memory storing instructions executable by the processor to:
receive sales data having different formats from a plurality of different sources;
incrementally reformat the sales data as it is received to common format data to enable use of the sales data by each component model of a plurality of component models; and
store the common format data in the standard data store;
an enterprise forecast computing subsystem including a processor, and a memory communicatively coupled to the processor, the memory storing instructions executable by the processor to:
build an ensemble forecasting model using at least two component models of the plurality of component models, wherein building the ensemble forecasting model comprises a selecting of the at least two or more component based on past performance data and a weighting of the at least two component models relative to each other based on past forecasting performance data;
access the common format data;
generate an aggregate demand forecast using the ensemble forecasting model and the common format data;
communicate the aggregate demand forecast to a forecast data store for storage;
access the standard data store to receive real-time updates to the common format data; and
in response to receiving the real-time updates to the common format data, update the ensemble forecasting model by modifying the weighting, relative to each other, of the at least two component models of the plurality of component models used to build the ensemble forecasting model; and
a server comprising an API accessible by one or more client applications, the API configured to:
receive requests for demand forecasts,
communicate demand forecast requests to the forecasts data store, and
receive demand forecasts from the forecasts data store.