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Scalable KInetoscopic Matrix Profile (SKIMP) is a family of algorithms which compute the Pan Matrix Profile (PMP), a new data structure which contains the nearest neighbor information for all subsequences of all lengths. This data structure allows the first truly parameter-free motif discovery algorithm in the literature.
In exploratory data mining, we may have no idea as to the subsequence lengths in which patterns are conserved in a dataset necessitating the need for variable-length motif discovery.
This very basic problem is ubiquitous in nearly all domains as the user's choice limits what regularities can be found in the dataset.
Figure: A toy data set of a random walk with two embedded subsequences (red and green).
In many cases, the suggested subsequence length for motif discovery is not readily apparent. This problem is exacerbated if a time series has multiple motifs of different lengths.
Figure: A frame-by-frame animation of the calculation of a pan matrix profile. Each frame corresponds to the completion of another matrix profile.
In this work we solve the motif-length sensitivity problem by introducing the Pan Matrix Profile, a data structure that contains all Matrix Profile information of a time series, and SKIMP, a family of parameter-less, anytime algorithms used to quickly approximate SKIMP.
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