How to cite this WIREs title:

WIREs Data Mining Knowl Discov

WIREs Data Mining Knowl Discov

Impact Factor: 4.476

# Time series motif discovery: dimensions and applications

Focus Article

Published Online: Feb 24 2014

DOI: 10.1002/widm.1119

Can't access this content? Tell your librarian.

Time series motifs are repeated segments in a long time series that, if exist, carry precise information about the underlying source of the time series. Motif discovery in time series data has received significant attention in the data mining community since its inception, principally because, motif discovery is meaningful and more likely to succeed when the data is large. Algorithms for motif discovery generally deal with three aspects the definition of the motifs, domain based preprocessing, and finally, the algorithmic steps. Typical definitions of motifs signify the similarity or the support of the motifs. Domains impose preprocessing requirements to meaningful motif finding such as data alignment, interpolation, and transformation. Motif discovery algorithms vary based on exact or approximate evaluation of the definition. In addition, algorithms require different representations [Symbolic Aggregate approXimation (SAX), DFT etc.] and similarity measures [correlation, dynamic time warping (DTW) distance etc.] for time series segments. In this paper, we discuss these three facets in detail with examples taken from the literature. We briefly describe a set of applications of time series motif in various domains and elaborate on a certain application in entomology to analyze insect behavior. This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Technologies > Structure Discovery and Clustering