The detection of very similar patterns in a time series, commonly called
motifs, has received continuous and increasing attention from diverse
scientific communities. In particular, recent approaches for discovering
similar motifs of different lengths have been proposed. In this work, we show
that such variable-length similarity-based motifs cannot be directly compared,
and hence ranked, by their normalized dissimilarities. Specifically, we find
that length-normalized motif dissimilarities still have intrinsic dependencies
on the motif length, and that lowest dissimilarities are particularly affected
by this dependency. Moreover, we find that such dependencies are generally
non-linear and change with the considered data set and dissimilarity measure.
Based on these findings, we propose a solution to rank those motifs and measure
their significance. This solution relies on a compact but accurate model of the
dissimilarity space, using a beta distribution with three parameters that
depend on the motif length in a non-linear way. We believe the incomparability
of variable-length dissimilarities could go beyond the field of time series,
and that similar modeling strategies as the one used here could be of help in a
more broad context.Comment: 20 pages, 10 figure
