Ranking and significance of variable-length similarity-based time series motifs
Review articleOpen access
Joan Serrà - No affiliation found
2016/08/15 Full-length article DOI: 10.1016/j.eswa.2016.02.026
Journal: Expert Systems with Applications
Highlights•Motifs of variable-length are becoming an important asset of current expert systems.•We show that such motifs cannot be compared using length-normalized dissimilarities.•We propose a solution to rank variable-length motifs and measure their significance.•It relies on a compact dissimilarity space model based on the beta distribution.
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