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A multi-scale smoothing kernel for measuring time-series similarity

Autor
Troncoso, A.; Arias, M.; Riquelme, J.C
Tipus d'activitat
Article en revista
Revista
Neurocomputing
Data de publicació
2015-11-01
Volum
167
Pàgina inicial
8
Pàgina final
17
DOI
https://doi.org/10.1016/j.neucom.2014.08.099 Obrir en finestra nova
Projecte finançador
MINERIA EN DATOS BIOLOGICOS Y SOCIALES: ALGORITMOS, TEORIA E IMPLEMENTACION
Repositori
http://hdl.handle.net/2117/78645 Obrir en finestra nova
URL
http://www.sciencedirect.com/science/article/pii/S0925231215005585 Obrir en finestra nova
Resum
In this paper a kernel for time-series data is introduced so that it can be used for any data mining task that relies on a similarity or distance metric. The main idea of our kernel is that it should recognize as highly similar time-series that are essentially the same but may be slightly perturbed from each other: for example, if one series is shifted with respect to the other or if it slightly misaligned. Namely, our kernel tries to focus on the shape of the time-series and ignores small pertu...
Citació
Troncoso, A., Arias, M., Riquelme, J.C. A multi-scale smoothing kernel for measuring time-series similarity. "Neurocomputing", 01 Novembre 2015, vol. 167, p. 8-17.
Paraules clau
Distance, Kernel, Similarity, Time-series classification
Grup de recerca
LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge

Participants

  • Troncoso, Alicia  (autor)
  • Arias Vicente, Marta  (autor)
  • Riquelme Santos, José Cristóbal  (autor)

Arxius