Systems and methods for responding to predicted events in time-series data using synthetic profiles created by artificial intelligence models trained on non-homogonous time-series data

A method for identifying items, the method comprising:

• receiving an entity dataset comprising a plurality of current state characteristics for an entity, wherein the plurality of current state characteristics comprises entity characteristics;
• determining, for the entity using the plurality of current state characteristics as input into a clustering machine learning model, an entity cluster within a plurality of entity clusters, wherein the clustering machine learning model has been trained to classify the entity based on an entity's current state characteristics, into the entity cluster of the plurality of entity clusters, and wherein each entity cluster within the plurality of entity clusters is defined by a corresponding set of current state characteristics;
• identifying a plurality of entities associated with the entity cluster, wherein each entity within the plurality of entities is associated with corresponding one or more items;
• generating, for the entity cluster, an item characteristic dataset based on the corresponding one or more items associated with each entity;
• determining, for the entity cluster using the item characteristic dataset as input into a profiling machine learning model, a plurality of items, wherein the profiling machine learning model has been trained to identify, for a given item characteristic set, a given plurality of items to be linked with a given entity cluster;
• determining, based on the plurality of items, a subset of items for the entity having a predetermined output variable;
• and
• providing to a user an indication of the predetermined output variable and an entity variable corresponding to a current set of items, wherein the predetermined output variable is determined based on item data within the subset of items and the entity variable is determined based on current item data within the current set of items.

One or more non-transitory, computer-readable media, comprising instructions that, when executed by one or more processors, cause operations comprising:

• receiving an entity dataset comprising a plurality of current state characteristics for an entity, wherein the plurality of current state characteristics comprises entity characteristics;
• determining, for the entity using the plurality of current state characteristics as input into a clustering machine learning model, an entity cluster within a plurality of entity clusters, wherein the clustering machine learning model has been trained to classify the entity based on an entity's current state characteristics, into the entity cluster of the plurality of entity clusters;
• identifying a plurality of entities and a corresponding one or more items associated with the entity cluster;
• generating an item characteristic dataset based on the corresponding one or more items associated with each entity;
• determining, for the entity cluster using the item characteristic dataset as input into a profiling machine learning model, a plurality of items, wherein the profiling machine learning model has been trained to identify, for a given item characteristic set, a given plurality of items;
• and
• providing to a user an indication of a predetermined output variable and an entity variable corresponding to a current set of items, wherein the predetermined output variable is determined based on item data within the plurality of items and the entity variable is determined based on current item data within the current set of items.