Prognostics acceleration for machine learning anomaly detection

A non-transitory computer-readable medium that includes stored thereon computer-executable instructions that when executed by at least a processor of a computer cause the computer to:

• separate time series signals into a plurality of alternative configurations of clusters based on correlations between the time series signals, wherein the alternative configurations of clusters differ by amount of individual clusters that the time series signals are separated into;
• train machine learning models for the individual clusters in the alternative configurations of clusters;
• determine whether one or more of the alternative configurations of clusters is viable for use in a production environment based on whether the trained machine learning models for the individual clusters in the one or more of the alternative configurations of clusters satisfy an accuracy threshold and a completion time threshold;
• select one configuration from the alternative configurations of clusters that were determined to be viable configurations;
• and
• deploy production machine learning models into the production environment to detect anomalies in the time series signals based on the selected configuration.

A computing system, comprising:

• at least one processor;
• at least one memory connected to the at least one processor;
• a non-transitory computer readable medium including instructions stored thereon that when executed by at least the processor cause the computing system to:
• separate time series signals into a plurality of alternative configurations of clusters based on correlations between the time series signals, wherein the alternative configurations of clusters differ by amount of individual clusters that the time series signals are separated into;
• train machine learning models for the individual clusters in the alternative configurations of clusters;
• determine whether one or more of the alternative configurations of clusters is viable for use in a target environment based on whether the trained machine learning models for the individual clusters in the one or more of the alternative configurations of clusters satisfy an accuracy threshold and a completion time threshold;
• select one configuration from the alternative configurations of clusters that were determined to be viable configurations;
• and
• deploy production machine learning models into the production environment to detect anomalies in the time series signals based on the selected configuration.