Predictive, machine-learning, time-series computer models suitable for sparse training sets

Provided is a process including: obtaining, for a plurality of entities, entity logs, wherein: the entity logs comprise events involving the entities, a first subset of the events are actions by the entities, at least some of the actions by the entities are targeted actions, and the events are label...

Full description

Saved in:
Bibliographic Details
Main Authors Lakshmipathy, Sathish Kumar, Engeling, Michael Henry, Briancon, Alain Charles, Amini, Sara, Zion, Eyal Ben, Boldyrev, Dmitrii Aleksandrovich, Curry, David Alexander
Format Patent
LanguageEnglish
Published 25.04.2023
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
Online AccessGet full text

Cover

More Information
Summary:Provided is a process including: obtaining, for a plurality of entities, entity logs, wherein: the entity logs comprise events involving the entities, a first subset of the events are actions by the entities, at least some of the actions by the entities are targeted actions, and the events are labeled according to an ontology of events having a plurality of event types; training, with one or more processors, based on the entity logs, a predictive machine learning model to predict whether an entity characterized by a set of inputs to the model will engage in a targeted action in a given duration of time in the future; and storing the trained predictive machine learning model in memory.
Bibliography:Application Number: US202016868385