US 12,284,071 B2
Techniques for prediction models using time series data
Xiao Huang, Marietta, GA (US); and Yan Wang, Athens, GA (US)
Assigned to EQUIFAX INC., Atlanta, GA (US)
Filed by EQUIFAX INC., Atlanta, GA (US)
Filed on Jan. 17, 2024, as Appl. No. 18/415,258.
Application 18/415,258 is a continuation of application No. 17/218,161, filed on Mar. 31, 2021, granted, now 11,894,971.
Prior Publication US 2024/0195677 A1, Jun. 13, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. H04L 41/0631 (2022.01); H04L 41/16 (2022.01); H04L 43/04 (2022.01); H04L 43/067 (2022.01)
CPC H04L 41/064 (2013.01) [H04L 41/16 (2013.01); H04L 43/04 (2013.01); H04L 43/067 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising one or more processing devices performing operations comprising:
receiving a risk assessment query that identifies a target entity;
providing, to a lagged prediction model, an input predictor record associated with the target entity, the input predictor record comprising a set of time-series attributes associated with the target entity, wherein the lagged prediction model is trained by:
implementing a group feature selection technique configured to select a first time-series attribute as input and to deselect a second time-series attribute, and
determining a number of lagged values included in a first group of lagged values of the first time-series attribute by generating and comparing cross-validated accuracies for subsets of the first group of lagged values;
computing, with the lagged prediction model, an output risk indicator from the input predictor record; and
transmitting the output risk indicator to a remote computing system, wherein the output risk indicator is usable for controlling access by the target entity to one or more interactive computing environments.