This is a supporting page of the paper

Ranking and Significance of Variable-length Similarity-based Time Series Motifs

by Joan Serrą, Isabel Serra, Įlvaro Corral, and Josep Lluis Arcos

Full reference

J. Serrą, I. Serra, Į. Corral, and J.L. Arcos. "Ranking and significance of variable-length similarity-based time series motifs". Submitted.


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.

Code and Results

Our Matlab source codes can be found here. The full results, including intermediate results, can be downloaded here.


If using original code or results from here, please cite the above reference. We do not provide any support or assistance for the supplied code nor we offer any other compilation/variant of it. In addition, we assume no responsibility regarding this web page or the use of the contents linked in it.

This page was last updated by
Joan Serrą, Dept. of Learning Systems, IIIA-CSIC
on March 2015.