Patent ID: 11868860
Assignee: CITIBANK, N.A.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 14:
15. A non-transitory, computer-readable medium for using cohort-based predictions in clustered time-series data in order to detect rate-of-change events, comprising instructions that, when executed by one or more processors, cause operations comprising:
generating historical time-series training data that indicates historic rates-of-change over given time periods;
receiving a first user profile;
in response to receiving the first user profile, determining a first feature input based on the first user profile;
retrieving a plurality of cohort clusters, wherein the plurality of cohort clusters is generated by a first artificial intelligence model that is trained to cluster a plurality of separate time-series data streams into the plurality of cohort clusters;
inputting the first feature input into a second artificial intelligence model, wherein the second artificial intelligence model is trained to select a subset of the plurality of cohort clusters from the plurality of cohort clusters based on the first feature input, wherein each cohort cluster of the plurality of cohort clusters corresponds to a respective cohort of users having current state characteristics, and wherein training the second artificial intelligence model comprises training a convolutional neural network using unsupervised learning on the historical time-series training data;
receiving an output from the second artificial intelligence model;
selecting, based on the output, a time-series prediction from a plurality of time-series predictions, wherein each of the plurality of time-series predictions comprises a respective predicted event, and wherein each cohort cluster of the subset of the plurality of cohort clusters corresponds to a respective time-series prediction of the plurality of time-series predictions; and
generating, at a user interface, the time-series prediction.