Informatics and Applications
2016, Volume 10, Issue 4, pp 121-131
FEATURE-BASED TIME-SERIES CLASSIFICATION
- M. E. Karasikov
- V. V. Strijov
Abstract
The paper is devoted to the multiclass time series classification problem. The feature-based approach that uses meaningful and concise representations for feature space construction is applied. A time series is considered as a sequence of segments approximated by parametric models, and their parameters are used as time series features.
This feature construction method inherits from the approximation model such unique properties as shift invariance.
The authors propose an approach to solve the time series classification problem using distributions of parameters of the approximation model. The proposed approach is applied to the human activity classification problem. The computational experiments on real data demonstrate superiority of the proposed algorithm over baseline solutions.
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[+] About this article
Title
FEATURE-BASED TIME-SERIES CLASSIFICATION
Journal
Informatics and Applications
2016, Volume 10, Issue 4, pp 121-131
Cover Date
2016-12-30
DOI
10.14357/19922264160413
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
time series; multiclass classification; time series segmentation; hyperparameters of approximation model; autoregressive model; discrete Fourier transform
Authors
M. E. Karasikov  ,  and V.V. Strijov 
Author Affiliations
Moscow Institute of Physics and Technology, 9 Institutskiy Per., Dolgoprudny, Moscow Region 141700, Russian Federation
Skolkovo Institute of Science and Technology, Skolkovo Innovation Center, Building 3, Moscow 143016, Russian Federation
A. A. Dorodnicyn Computing Center, Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
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