Frequency-domain signal clustering

One or more non-transitory computer-readable media that includes stored thereon computer-executable instructions that when executed by at least a processor of a computer system cause the computer system to:

• access a collection of time series signals that are undifferentiated with respect to clusters;
• determine similarity in a frequency domain among the time series signals;
• chooses a cluster of similar time series signals from the time series signals based on the similarity in the frequency domain;
• and
• route the cluster to a destination that is discrete from destinations for other clusters detected in the time series signals.

A computer system, comprising:

• at least one processor;
• at least one memory connected to the at least one processor;
• one or more non-transitory computer-readable media including instructions stored thereon that when executed by at least the processor cause the computing system to:
• access a training range of time series signals that include unidentified clusters;
• determine measures of correlation among the time series signals based on analysis in the frequency domain;
• transfer time series signals into a cluster based on the measures of correlation;
• train a machine learning model to detect anomalies based on the training range of the cluster;
• and
• in response to detecting an anomaly in a surveillance range of the cluster, transmit an electronic alert that the anomaly has occurred in the cluster.