Reference : DSCo-NG: A Practical Language Modeling Approach for Time Series Classification
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/27942
DSCo-NG: A Practical Language Modeling Approach for Time Series Classification
English
Li, Daoyuan mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
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) >]
Oct-2016
The 15th International Symposium on Intelligent Data Analysis
Yes
No
International
The 15th International Symposium on Intelligent Data Analysis
from 13-10-2016 to 15-10-2016
Stockholm
[en] The abundance of time series data in various domains and their high dimensionality characteristic are challenging for harvesting useful information from them. To tackle storage and processing challenges, compression-based techniques have been proposed. Our previous work, Domain Series Corpus (DSCo), compresses time series into symbolic strings and takes advantage of language modeling techniques to extract from the training set knowledge about different classes. However, this approach was flawed in practice due to its excessive memory usage and the need for a priori knowledge about the dataset. In this paper we propose DSCo-NG, which reduces DSCo’s complexity and offers an efficient (linear time complexity and low memory footprint), accurate (performance comparable to approaches working on uncompressed data) and generic (so that it can be applied to various domains) approach for time series classification. Our confidence is backed with extensive experimental evaluation against publicly accessible datasets, which also offers insights on when DSCo-NG can be a better choice than others.
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/10993/27942

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