US 2026/0228764 A1 · HIERARCHICAL DATA FORECASTING USING PROCESSOR-BASED BAYESIAN MODELS AND DEEP LEARNING

Abstract

Techniques are provided for hierarchical data forecasting using processor-based Bayesian models and deep learning. One method comprises obtaining data characterizing a performance of hierarchical items, wherein the hierarchical items comprise one or more item families in an item family hierarchical level and multiple items in an item hierarchical level; applying at least portions of the obtained data to a processor-based Bayesian-based model to obtain output data characterizing an item family forecast for at least one item family and a first item-level forecast for one or more of the items; applying at least portions of the obtained output data to a processor-based deep learning model to obtain data characterizing a second item-level forecast for one or more of the items; and initiating one or more processing steps based at least in part on the at least one second item-level forecast.

Claims20

Dependent claims

A method, comprising:

accessing at least one data structure comprising data characterizing a performance of a plurality of hierarchical items, wherein the plurality of hierarchical items comprises one or more item families in an item family hierarchical level and at least a plurality of items in an item hierarchical level;
applying at least portions of the data structure to at least one processor-based Bayesian-based model to obtain data characterizing an item family forecast for at least one of the item families and at least one first item-level forecast for one or more of the plurality of items;
applying at least portions of the item family forecast to at least one processor-based deep learning model to obtain data characterizing at least one second item-level forecast for one or more of the plurality of items;
and
initiating one or more processing steps based at least in part on the at least one second item-level forecast;
wherein the method is performed by at least one processing device comprising a processor coupled to a memory.

3 more elements

An apparatus comprising:

at least one processing device comprising a processor coupled to a memory;
the at least one processing device being configured to implement the following steps:
accessing at least one data structure comprising data characterizing a performance of a plurality of hierarchical items, wherein the plurality of hierarchical items comprises one or more item families in an item family hierarchical level and at least a plurality of items in an item hierarchical level;
applying at least portions of the data structure to at least one processor-based Bayesian-based model to obtain data characterizing an item family forecast for at least one of the item families and at least one first item-level forecast for one or more of the plurality of items;
applying at least portions of the item family forecast to at least one processor-based deep learning model to obtain data characterizing at least one second item-level forecast for one or more of the plurality of items;
and
initiating one or more processing steps based at least in part on the at least one second item-level forecast.

4 more elements

A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device to perform the following steps:

accessing at least one data structure comprising data characterizing a performance of a plurality of hierarchical items, wherein the plurality of hierarchical items comprises one or more item families in an item family hierarchical level and at least a plurality of items in an item hierarchical level;
applying at least portions of the data structure to at least one processor-based Bayesian-based model to obtain data characterizing an item family forecast for at least one of the item families and at least one first item-level forecast for one or more of the plurality of items;
applying at least portions of the item family forecast to at least one processor-based deep learning model to obtain data characterizing at least one second item-level forecast for one or more of the plurality of items;
and
initiating one or more processing steps based at least in part on the at least one second item-level forecast.

2 more elements

Dependencies are inferred from the claim text.

Hierarchical data forecasting using processor-based bayesian models and deep learning

Life of this patent

Hierarchical data forecasting using processor-based bayesian models and deep learning

Life of this patent

Abstract

Claims20