Systems and methods for aggregating time-series data streams based on potential state characteristics following aggregation

A method for aggregating time-series data streams based on potential state characteristics following aggregation, the method comprising:

• receiving a first model profile, wherein the first model profile is populated based on a first plurality of time-series data streams, and wherein the first model profile corresponds to a required state characteristic;
• determining a first feature input based on the first model profile;
• receiving a plurality of available time-series data streams;
• training, using unsupervised learning, a first artificial intelligence model to classify labeled feature inputs with known clusters based on similarities between state characteristics in the labeled feature inputs and the known clusters;
• using the first artificial intelligence model to cluster the plurality of available time-series data streams into a first plurality of time-series data stream clusters by aggregating a subset of the plurality of available time-series data streams;
• retrieving the first plurality of time-series data stream clusters, wherein the first plurality of time-series data stream clusters is generated by the first artificial intelligence model;
• determining a second feature input based on the first plurality of time-series data stream clusters;
• determining, based on the first feature input and the second feature input, a first similarity metric between the first plurality of time-series data streams and each of the first plurality of time-series data stream clusters;
• selecting a first time-series data stream cluster from the first plurality of time-series data stream clusters based on the first similarity metric exceeding a first similarity threshold;
• generating a second time-series data stream cluster based on aggregating the first time-series data stream cluster and the first plurality of time-series data streams;
• determining a first state characteristic for the second time-series data stream cluster;
• comparing the first state characteristic to the required state characteristic;
• and
• generating, at a user interface, a first recommendation for the first time-series data stream cluster based on comparing the first state characteristic to the required state characteristic.

One or more non-transitory machine readable media comprising instructions recorded thereon that when executed by one or more processors cause operations comprising:

• receiving a first model profile, wherein the first model profile is populated based on a first plurality of time-series data streams, and wherein the first model profile corresponds to a required state characteristic;
• receiving a plurality of available time-series data streams;
• training, using unsupervised learning, a first artificial intelligence model to classify labeled feature inputs with known clusters based on similarities between state characteristics in the labeled feature inputs and the known clusters;
• using the first artificial intelligence model to cluster the plurality of available time-series data streams into a first plurality of time-series data stream clusters by aggregating a subset of the plurality of available time-series data streams;
• retrieving the first plurality of time-series data stream clusters, wherein the first plurality of time-series data stream clusters is generated by the first artificial intelligence model;
• determining a first similarity metric between the first plurality of time-series data streams and each of the first plurality of time-series data stream clusters;
• selecting a first time-series data stream cluster from the first plurality of time-series data stream clusters based on the first similarity metric exceeding a first similarity threshold;
• generating a second time-series data stream cluster based on aggregating the first time-series data stream cluster and the first plurality of time-series data streams;
• determining a first state characteristic for the second time-series data stream cluster;
• comparing the first state characteristic to the required state characteristic;
• and
• generating, at a user interface, a first recommendation for the first time-series data stream cluster based on comparing the first state characteristic to the required state characteristic.