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A kernel for time series classification: application to atmospheric pollutants

Autor
Arias, M.; Troncoso, A.; Riquelme, J.
Tipus d'activitat
Presentació treball a congrés
Nom de l'edició
7th International Conference on Soft Computing Models in Industrial and Environmental Applications
Any de l'edició
2012
Data de presentació
2012
Llibre d'actes
Soft Computing Models in Industrial and Environmental Applications: 7th International Conference, SOCO’12, Ostrava, Czech Republic, September 5th-7th, 2012
Pàgina inicial
417
Pàgina final
426
DOI
https://doi.org/10.1007/978-3-642-32922-7_43 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/19435 Obrir en finestra nova
Resum
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...
Citació
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.
Paraules clau
Atmospheric pollutants, Computational costs, Data sets, Dynamic time warping, Euclidean distance, Time series classifications, Time-series data
Grup de recerca
LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge

Participants

  • Arias Vicente, Marta  (autor ponent)
  • Troncoso, Alicia  (autor ponent)
  • Riquelme Santos, Jose Cristobal  (autor ponent)

Arxius