Supporting web page of our paper:
Nuno Castro and Paulo Azevedo, Multiresolution Motif Discovery in Time Series,
in Proceedings of the SIAM International Conference on Data Mining (SDM 2010), Columbus, Ohio, USA. SIAM, 2010, pp. 665-676. [pdf] [slides]
[Free Java source code] [DBLP] [Scholar] [BibTeX]
[iMotifs - a MrMotif GUI] [You may also be interested in finding Statistically Significant Motifs.]
A time series motif is a frequent pattern in time series
data, i.e. a repetition of a
particular subsection of the
Figure 1: Example of a motif with 3 repetitions (instances) in the context of EEG data from .We introduce MrMotif, a scalable algorithm to discover motifs in time series at several resolutions.
 Yankov, D., Keogh, E., Medina, J., Chiu, B., Zordan, V.,
Detecting Motifs Under Uniform Scaling,
in Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2007), pp. 844-853.
 Shieh, J. and Keogh, E., iSAX: indexing and mining terabyte
sized time series,
in Proceedings of the 14th ACM SIGKDD international Conference on Knowledge Discovery and Data Mining (2008), pp. 623-631.
 Metwally, A., Agrawal, D., and Abbadi, A., Efficient
Computation of Frequent and Top-k Elements in Data Streams,
in Proceedings of the 10th International Conference on Database Theory (2005), pp. 398-412.
 Mueen, A., Keogh, E., Zhu, Q., Cash, S., and West-over, B.,
Exact Discovery of Time Series Motifs,
in Proceedings of SIAM International Conference on Data Mining (2009), pp. 473-484.