公开(公告)号：US20210117995A1

公开(公告)日：2021-04-22

申请号：US16810465

申请日：2020-03-05

Applicant: SAP SE

Inventor： Pankti Jayesh Kansara ,  James Rapp ,  John Seeburger ,  Sangeetha Krishnamoorthy ,  Mario Ponce Midence

IPC: G06Q30/02 ,  G06F17/18 ,  G06Q30/06

Abstract: The present disclosure involves systems, software, and computer implemented methods for proactively predicting demand based on sparse transaction data. One example method includes receiving a request to predict transaction quantities for a plurality of transaction entities for a future time period. Historical transaction data for the transaction entities is identified for a plurality of categories of transacted items. The plurality of categories are organized using a hierarchy of levels. Multiple levels of the hierarchy are iterated over starting at a lowest level. For each current level in the iteration, features to include in a quantity forecasting model for the current level are identified. The quantity forecasting model is trained using the identified features.
Predicted transaction dates are predicted for the current level by a transaction date prediction model. The quantity forecasting model is used to generate predicted quantity information for the current level for the predicted transaction dates.

公开(公告)号：US11875368B2

公开(公告)日：2024-01-16

申请号：US16810465

申请日：2020-03-05

Applicant: SAP SE

Inventor： Pankti Jayesh Kansara ,  James Rapp ,  John Seeburger ,  Sangeetha Krishnamoorthy ,  Mario Ponce Midence

IPC: G06Q30/0202 ,  G06Q30/0601 ,  G06N20/00 ,  G06Q10/047 ,  G06Q10/1093 ,  G06N7/01

CPC classification number: G06Q30/0202 ,  G06N7/01 ,  G06N20/00 ,  G06Q10/047 ,  G06Q10/1093 ,  G06Q30/0605

Abstract: The present disclosure involves systems, software, and computer implemented methods for proactively predicting demand based on sparse transaction data. One example method includes receiving a request to predict transaction quantities for a plurality of transaction entities for a future time period. Historical transaction data for the transaction entities is identified for a plurality of categories of transacted items. The plurality of categories are organized using a hierarchy of levels. Multiple levels of the hierarchy are iterated over starting at a lowest level. For each current level in the iteration, features to include in a quantity forecasting model for the current level are identified. The quantity forecasting model is trained using the identified features.
Predicted transaction dates are predicted for the current level by a transaction date prediction model. The quantity forecasting model is used to generate predicted quantity information for the current level for the predicted transaction dates.