A tree-construction search approach for multivariate time series motifs discovery

This paper examines an unsupervised search method to discover motifs from multivariate time series data. Our method first scans the entire series to construct a list of candidate motifs in linear time, the list is then used to populate a sparse self-similarity matrix for further processing to genera...

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Bibliographic Details
Published in:Pattern recognition letters 2010-07, Vol.31 (9), p.869-875
Main Authors: Wang, L., Chng, E.S., Li, H.
Format: Article
Language:English
Subjects:
Applied sciences
Exact sciences and technology
Information retrieval
Information theory
Information, signal and communications theory
Motif discovery
Multidimensional sequences
Music
Pattern recognition
Signal and communications theory
Signal processing
Signal representation. Spectral analysis
Signal, noise
Telecommunications and information theory
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Summary:This paper examines an unsupervised search method to discover motifs from multivariate time series data. Our method first scans the entire series to construct a list of candidate motifs in linear time, the list is then used to populate a sparse self-similarity matrix for further processing to generate the final selections. The proposed algorithm is efficient in both running time and memory storage. To demonstrate its effectiveness, we applied it to search for repeating segments in both music and sensory data sets. The experimental results showed that the proposed method can efficiently detect repeating segments as compared to well-known methods such as self-similarity matrix search and symbolic aggregation approximation approaches.
ISSN:0167-8655
1872-7344
DOI:10.1016/j.patrec.2010.01.005