Unsupervised segmentation of a univariate time series dataset using motifs and shapelets

Abstract

Systems and methods are provided for receiving a time series dataset from a monitored processor and group the dataset into a plurality of clusters. Using an unsupervised machine learning model, the system may combine a subset of the plurality of clusters by data signature similarities to form a plurality of motifs and combine the plurality of motifs into one or more shapelets. In some examples, the system may train a supervised machine learning model using the plurality of motifs and the one or more shapelets as input to the supervised machine learning model. The system can perform various actions in response to labelling the time series dataset, including predicting a second time series dataset, determining that a monitored processor corresponds with an overutilization at a particular time, or suggesting a reduction of additional utilization of the monitored processor.