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A kernel for time series classification: application to atmospheric pollutants

Author
Arias, M.; Troncoso, A.; Riquelme, J.
Type of activity
Presentation of work at congresses
Name of edition
7th International Conference on Soft Computing Models in Industrial and Environmental Applications
Date of publication
2012
Presentation's date
2012
Book of congress proceedings
Soft Computing Models in Industrial and Environmental Applications: 7th International Conference, SOCO’12, Ostrava, Czech Republic, September 5th-7th, 2012
First page
417
Last page
426
DOI
https://doi.org/10.1007/978-3-642-32922-7_43 Open in new window
Repository
http://hdl.handle.net/2117/19435 Open in new window
Abstract
In this paper a kernel for time-series data is presented. The main idea of the kernel is that it is designed to recognize as similar time series that may be slightly shifted with one another. Namely, it tries to focus on the shape of the time-series and ignores the fact that the series may not be perfectly aligned. The proposed kernel has been validated on several datasets based on the UCR time-series repository [1]. A comparison with the well-known Dynamic Time Warping (DTW) distance and Euclid...
Citation
Arias, M.; Troncoso, A.; Riquelme, J. A kernel for time series classification: application to atmospheric pollutants. A: International Conference on Soft Computing Models in Industrial and Environmental Applications. "Advances in Intelligent Systems and Computing". 2012, p. 417-426.
Keywords
Atmospheric pollutants, Computational costs, Data sets, Dynamic time warping, Euclidean distance, Time series classifications, Time-series data
Group of research
LARCA - Laboratory of Relational Algorithmics, Complexity and Learnability

Participants

  • Arias Vicente, Marta  (author and speaker )
  • Troncoso, Alicia  (author and speaker )
  • Riquelme Santos, Jose Cristobal  (author and speaker )

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