公开(公告)号：US20190156357A1

公开(公告)日：2019-05-23

申请号：US16169923

申请日：2018-10-24

Applicant: Staples, Inc.

Inventor： Evangelos Palinginis ,  Nitin Verma ,  Michael Bhaskaran ,  Jian Jiao

IPC: G06Q30/02 ,  G06F17/18 ,  G06K9/62

Abstract: In an example embodiment, systems and methods are described for demand prediction and profitability modeling based on heterogeneous data and blended clustering models. Data for a plurality of items is received and differentiated into a first set of good items and a second set of bad items. Good items and bad items may be indicated by a threshold for a prediction accuracy metric, such as weighted Mean Average Percentage Error (MAPE). A first model for predicted demand levels of the good items is generated that excludes cross-cluster effects with the bad items. A second model of the bad items is generated that includes a residual correction and cross-cluster effects with the good items. A predicted demand of a particular item is generated based on a cluster-level regression model and at least one of the first model and the second model.