Proceedings of the 2008 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Toward unsupervised activity discovery using multi-dimensional motif detection in time series
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A review on time series data mining
Engineering Applications of Artificial Intelligence
A disk-aware algorithm for time series motif discovery
Data Mining and Knowledge Discovery
Unsupervised discovery of motifs under amplitude scaling and shifting in time series databases
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
CPMD: a matlab toolbox for change point and constrained motif discovery
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
G-SteX: greedy stem extension for free-length constrained motif discovery
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
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Discovering recurring patterns in time series data is a fundamental problem for temporal data mining. This paper addresses the problem of locating subdimensional motifs in real-valued, multivariate time series, which requires the simultaneous discovery of sets of recurring patterns along with the corresponding relevant dimensions. While many approaches to motif discovery have been developed, most are restricted to categorical data, univariate time series, or multivariate data in which the temporal patterns span all of the dimensions. In this paper, we present an expected linear-time algorithm that addresses a generalization of multivariate pattern discovery in which each motif may span only a subset of the dimensions. To validate our algorithm, we discuss its theoretical properties and empirically evaluate it using several data sets including synthetic data and motion capture data collected by an on-body inertial sensor.