Method and system for generating ensemble demand forecasts

Patent metadata

Field	Value
Publication number	US-2023069403-A1
Application number	US-202217850640-A
Country	US
Kind code	A1
Filing date	Jun 27, 2022
Priority date	Oct 26, 2018
Publication date	Mar 2, 2023
Grant date	—

Abstract

Methods and systems for forecasting demand for a plurality of items are provided. In particular, the demand forecasting system and methods described herein are useful for predicting demand of products in a retail context. Forecast models are built and used to score incoming sales data to predict future demand for items. Forecast models are validated by evaluating actual demand against predicted demand and using that information to inform how future ensemble forecast will be generated. Forecasts may be broken down into smaller components to satisfy a variety of requests for data from client applications.

First claim

1 . A system for forecasting demand for a plurality of items, the system comprising:
a common data preparation engine comprising:
a computing system including a data store, processor, and memory communicatively coupled to the processor, the memory storing instructions executable by the processor to:
receive sales data,
convert sales data to common format data, and
store common format data in a standard data store;
an enterprise forecast engine comprising:
a computing system including a processor, and a memory communicatively coupled to the processor, the memory storing instructions executable by the processor to:
build a forecasting model;
access the common format data;
generate an aggregate demand forecast using the forecasting model and the common format data; and
communicate demand forecast to a forecast data store for storage;
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.

2 . The system of claim 1 , wherein the forecasting model is an ensemble forecast model built by selecting two or more component models used to forecast demand for items and weighting the two or more component models based on past forecasting performance.

3 . The system of claim 2 , wherein the ensemble forecast model is calculated using an affine function.

4 . The system of claim 2 , wherein the component models include one or more of an ARIMA model or a LOESS model.

5 . The system of claim 1 , wherein the forecasting model is a single model based on a recurring neural network (RNN).

6 . The system of claim 1 , wherein the API is further configured to generate an administrator user interface for visualizing demand forecast data.

7 . The system of claim 1 , further comprising a cloud platform with at least one load balancer to direct requests to available servers for processing.

8 . The system of claim 2 , wherein the past forecasting performance is evaluated by a forecast validation engine.

9 . A method of forecasting demand for a plurality of items within a retail enterprise, the method comprising:
receiving sales data at a common data preparation engine; formatting the sales data into a common format; building, with an enterprise forecast engine, an ensemble forecast model from two or more component models; analyzing, with the ensemble forecast model, the common format data to generate an aggregate demand forecast; and storing the aggregate demand forecast in a forecasts data store.

10 . The method of claim 9 , further comprising:
receiving, at an API, a request from a client application for a demand forecast; querying the forecasts data store for the appropriate aggregate demand forecast; and responding to the client application with the requested demand forecast.

11 . The method of claim 9 , wherein building further comprises weighting the two or more component models based on past performance of the component models in predicting item demand.

12 . The method of claim 9 , wherein the demand forecast is generated for an individual item, over a 1 week time period, for all sales of the retailer.

13 . The method of claim 10 , wherein the request comprises a time period, a location, and one or more items.

14 . The method of claim 10 , wherein the request is communicated to the data store by a real-time query service.

15 . The method of claim 10 , wherein the demand forecast is visualized on a user interface for viewing and analysis by an administrator user.

16 . The method of claim 10 , further comprising validating the forecast model by:
receiving a validation and selection of configuration options at a command line tool; sending a submission packet to a validation server; querying a demand forecast data store; storing a validation set in the data repository; calibrate the model with historical training data; testing the model by calculating prediction for each set of forecast coordinates; saving the predicted values; calculating forecast validation results and store in data repository; and display visualization of forecast performance on validation user interface.

17 . A non-transitory computer-readable storage medium comprising computer-executable instruction which, when executed by a computing system, cause the computing system to perform a method of generating a demand forecast for one or more items, the method comprising:
receiving store sales data and web sales data at a common data preparation engine;
formatting the store sales data and web sales data into a common format;
building, with an enterprise forecast engine, an ensemble forecast model from two or more component models;
analyzing, with the ensemble forecast model, the common format data to generate a demand forecast;
storing the aggregate demand forecast in a forecasts data store;
receiving, at an API, a request from an administrator computing device for a demand forecast;
communicating the request to the forecasts data store;
communicating the demand forecast to a user interface on the administrator computing device; and
visualizing the demand forecast on a display of the administrator computing device.

18 . The non-transitory computer-readable storage medium of claim 17 , wherein building the ensemble forecast model comprises:
receiving parameters for a demand forecast; selecting component models based on past performance; weighting the component models based on past performance; and calculating the ensemble forecast using affine function.

Assignees

• Target Brands Inc

Inventors

• Morgan Adam
• Das Anand
• Shah Samir H
• Yelland Phillip

Classifications

• G06N3/09

  Supervised learning · CPC title

• G06N3/0442

  characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title

• G06Q10/087 Primary

  Inventory or stock management, e.g. order filling, procurement or balancing against orders · CPC title

• G06Q30/0202 Primary

  Market predictions or forecasting for commercial activities · CPC title

• G06N3/08

  Learning methods · CPC title