PERUSE: An Unsupervised Algorithm for Finding Recurrig Patterns in Time Series
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Probabilistic discovery of time series motifs
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
A generic motif discovery algorithm for sequential data
Bioinformatics
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
Discovering original motifs with different lengths from time series
Knowledge-Based Systems
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Robust Singular Spectrum Transform
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Identifying hierarchical structure in sequences: a linear-time algorithm
Journal of Artificial Intelligence Research
Improving activity discovery with automatic neighborhood estimation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Approximate variable-length time series motif discovery using grammar inference
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Discovering patterns in real-valued time series
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
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Most available motif discovery algorithms in real-valued time series find approximately recurring patterns of a known length without any prior information about their locations or shapes. In this paper, a new motif discovery algorithm is proposed that has the advantage of requiring no upper limit on the motif length. The proposed algorithm can discover multiple motifs of multiple lengths at once, and can achieve a better accuracy-speed balance compared with a recently proposed motif discovery algorithm. We then briefly report two successful applications of the proposed algorithm to gesture discovery and robot motion pattern discovery.