Reference : Extracting Statistical Graph Features for Accurate and Efficient Time Series Classifi...

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/35125

Title :	Extracting Statistical Graph Features for Accurate and Efficient Time Series Classification
Language :	English
Author, co-author :	Li, Daoyuan [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Lin, Jessica []
	Bissyande, Tegawendé François D Assise [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Klein, Jacques [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Computer Science and Communications Research Unit (CSC) >]
	Le Traon, Yves [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	Mar-2018
Main document title :	21st International Conference on Extending Database Technology
Peer reviewed :	Yes
Audience :	International
Event name :	21st International Conference on Extending Database Technology
Event date :	from 26-03-2018 to 29-03-2018
Event place (city) :	Vienna
Event country :	Austria
Keywords :	[en] Time series classification ; multiscale visibility graph ; time series mining
Abstract :	[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.
Target :	Researchers ; Professionals ; Students ; General public ; Others
Permalink :	http://hdl.handle.net/10993/35125

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