# Multiresolution Motif
Discovery in Time Series

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
series.

Figure 1:
Example of a motif with 3 repetitions (instances) in the context of EEG
data from [1].

We introduce MrMotif, a
scalable algorithm to discover motifs in time series at several
resolutions.

MrMotif is:
- Fast
- executes in linear time, by
using one single sequential disk scan
over
the database and constant
access time structures;
- Space-efficient
- it uses a small (and
adjustable) amount of
memory; use a well studied space saving algorithm [3];
- Intuitive -
outputs the most frequent
patterns of the database in an intuitive way;
- Robust to noise -
it maintains the quality
of the retrieved patterns in noisy data;
- Easy to use - a small number of parameters
need be configured;
- Straightforward - algorithm is simple to
understand and implement;
- Reproducible
- we provide the source code and datasets.

MrMotif is based on the state of the art iSAX [2] time series
representation. For an explanation of time series similarity,
repetition counting and iSAX check this page.

## References

[1] 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.

[2] 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.

[3] 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.

[4] 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.