Readings in computer vision: issues, problems, principles, and paradigms
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
Sustainable operation and management of data center chillers using temporal data mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Time Series Epenthesis: Clustering Time Series Streams Requires Ignoring Some Data
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
InfraWatch: data management of large systems for monitoring infrastructural performance
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
ACM Computing Surveys (CSUR)
MDL-Based analysis of time series at multiple time-scales
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
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More and more, physical systems are being fitted with various kinds of sensors in order to monitor their behavior, health or intensity of use. The large quantities of time series data collected from these complex systems often exhibit two important characteristics: the data is a combination of various superimposed effects operating at different time scales, and each effect shows a fair degree of repetition. Each of these effects can be described by a small collection of motifs: recurring temporal patterns in the data. We propose a method to discover characteristic and potentially overlapping motifs at multiple time scales, taking into account systemic deformations and temporal warping. Our method is based on a combination of scale-space theory and the Minimum Description Length principle. We show its effectiveness on two time series datasets from real world applications.