Reference : Extracting Statistical Graph Features for Accurate and Efficient Time Series Classifi...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/35125
Extracting Statistical Graph Features for Accurate and Efficient Time Series Classification
English
Li, Daoyuan mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Lin, Jessica []
Bissyande, Tegawendé François D Assise mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Klein, Jacques mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Computer Science and Communications Research Unit (CSC) >]
Le Traon, Yves mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Mar-2018
21st International Conference on Extending Database Technology
Yes
International
21st International Conference on Extending Database Technology
from 26-03-2018 to 29-03-2018
Vienna
Austria
[en] Time series classification ; multiscale visibility graph ; time series mining
[en] This paper presents a multiscale visibility graph representation for time series as well as feature extraction methods for time series classification (TSC). Unlike traditional TSC approaches that seek to find global similarities in time series databases (eg., Nearest Neighbor with Dynamic Time Warping distance) or methods specializing in locating local patterns/subsequences (eg., shapelets), we extract solely statistical features from graphs that are generated from time series. Specifically, we augment time series by means of their multiscale approximations, which are further transformed into a set of visibility graphs. After extracting probability distributions of small motifs, density, assortativity, etc., these features are used for building highly accurate classification models using generic classifiers (eg., Support Vector Machine and eXtreme Gradient Boosting). Thanks to the way how we transform time series into graphs and extract features from them, we are able to capture both global and local features from time series. Based on extensive experiments on a large number of open datasets and comparison with five state-of-the-art TSC algorithms, our approach is shown to be both accurate and efficient: it is more accurate than Learning Shapelets and at the same time faster than Fast Shapelets.
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/10993/35125

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