Systems and methods for cohort-based predictions in clustered time-series data in order to detect significant rate-of-change events
Systems and methods may use one or more artificial intelligence models that predict an effect of a predicted event on a current state of the system. The systems and methods may use one or more artificial intelligence models that predict an effect and/or occurrence of a predicted event based on the current state of the system. In order to generate responses that are both timely and pertinent (e.g., in a dynamic fashion), the system must determine, both quickly (i.e., in real-time or near real-time) and accurately, the predicted event.
1. A system for using cohort-based predictions in clustered time-series data in order to detect rate-of-change events, the system comprising:
one or more processors; and
a non-transitory, computer-readable medium comprising instructions recorded thereon that when executed by the one or more processors cause operations comprising:
generating historical time-series training data that indicates historic rates-of-change over given time periods;
receiving, at a user interface of a client portal, a request for a time-series prediction;
receiving a first user profile, wherein the user profile comprises a current state characteristic;
determining a subset of state characteristics for generating a first feature input based on the current state characteristic;
populating the first user profile with the subset of state characteristics;
in response to receiving the first user profile, determining a first feature input based on the subset of state characteristics;
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 through unsupervised hierarchical clustering;
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, and 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 the user interface of the client portal, the time-series prediction comprising a rate-of-change predicted event.
2. A method for using cohort-based predictions in clustered time-series data in order to detect rate-of-change events, the method comprising:
generating historical time-series training data that indicates historic rates-of-change over given time periods;
receiving a first user profile, wherein the user profile comprises a current state characteristic;
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.
3. The method of claim 2 , wherein determining the first feature input based on the first user profile comprises:
determining a subset of current state characteristics for generating a first feature input based on the current state characteristic;
populating the first user profile with the subset of state characteristics; and
in response to receiving the first user profile, determining a first feature input based on the subset of state characteristics.
4. The method of claim 3 , wherein populating the first user profile with the subset of state characteristics further comprises:
crawling the Internet for a remote server comprising the subset of state characteristics; and
retrieving the subset of state characteristics from the remote server.
5. The method of claim 2 , wherein determining the first feature input based on the first user profile further comprises:
receiving, at the user interface, a user selection of the current state characteristic; and
determining to use the current state characteristic for the first feature input based on the first feature input.
6. The method of claim 2 , further comprising:
receiving a second user profile;
in response to receiving the second user profile, determining a second feature input for the second artificial intelligence model based on the second user profile;
inputting the second feature input into the second artificial intelligence model;
receiving a different output from the second artificial intelligence model; and
selecting, based on the different output, a different time-series prediction from the plurality of time-series predictions that corresponds to a different subset of the plurality of cohort clusters.
7. The method of claim 2 , wherein generating the time-series prediction further comprises:
determining a gradient for the time-series prediction;
determining a magnitude of the gradient; and
identifying a predicted event for the time-series prediction based on the magnitude.
8. The method of claim 7 , further comprising:
determining an effect of the predicted event based on the magnitude; and
generating a user recommendation based on the effect.
9. The method of claim 2 , wherein generating the time-series prediction further comprises:
determining a scale factor for the time-series prediction; and
performing, based on the scale factor, a linear transformation to the time-series prediction to identify a predicted event for the time-series prediction.
10. The method of claim 2 , wherein the first artificial intelligence model is trained to cluster the plurality of separate time-series data streams into the plurality of cohort clusters through unsupervised hierarchical clustering into hierarchies of correlation-distances between separate time-series data streams.
11. The method of claim 2 , wherein training the first artificial intelligence model comprises:
generating a matrix of pairwise correlations corresponding to the plurality of separate time-series data streams; and
clustering the plurality of separate time-series data streams based on pairwise distances.
12. The method of claim 2 , further comprising:
receiving a first labeled feature input, wherein the first labeled feature input is labeled with a known cohort cluster for the first labeled feature input; and
training the second artificial intelligence model to classify the first labeled feature input with the known cohort cluster.
13. The method of claim 2 , wherein generating the time-series prediction further comprises:
generating an isobaric representation of the time-series prediction; and
identifying a predicted event for the time-series prediction in the isobaric representation.
14. The method of claim 2 , wherein generating the time-series prediction further comprises:
identifying a predicted event for the time-series prediction; and
generating an option to modify the current state characteristic based on the predicted event.
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.
16. The non-transitory, computer-readable medium of claim 15 , wherein determining the first feature input based on the first user profile comprises:
determining a subset of current state characteristics for generating a first feature input based on the current state characteristic;
populating the first user profile with the subset of state characteristics; and
in response to receiving the first user profile, determining a first feature input based on the subset of state characteristics.
17. The non-transitory, computer-readable medium of claim 16 , wherein populating the first user profile with the subset of state characteristics further comprises:
crawling the Internet for a remote server comprising the subset of state characteristics; and
retrieving the subset of state characteristics from the remote server.
18. The non-transitory, computer-readable medium of claim 15 , wherein determining the first feature input based on the first user profile further comprises:
receiving, at the user interface, a user selection of the current state characteristic; and
determining to use the current state characteristic for the first feature input based on the first feature input.
19. The non-transitory, computer-readable medium of claim 15 , wherein generating the time-series prediction further comprises:
determining a gradient for the time-series prediction;
determining a magnitude of the gradient; and
identifying a predicted event for the time-series prediction based on the magnitude.
20. The non-transitory, computer-readable medium of claim 19 , wherein the instructions further cause operations comprising:
determining an effect of the predicted event based on the magnitude; and
generating a user recommendation based on the effect.