Skip to Main content Skip to Navigation
New interface
Conference papers

Adaptive split test for multivariate time series classification trees

Ahlame Douzal-Chouakria 1 Cécile Amblard 1, * 
* Corresponding author
Abstract : This paper proposes an extension of the classification trees to time series input variables. A new split criterion based on time series proximities is introduced. First, it relies on an adaptive (i.e., parametrized) time series metric to cover both behavior and values proximities. The metric's parameters may change from one internal node to another to best bisect the set of time series. Second, it involves the automatic extraction of the most discriminating sub-sequences. The proposed time series classification tree is applied to a wide range of datasets: public and new, real and synthetic, univariate and multivariate data. We show, through the carried out experiments, that the proposed tree outperforms temporal trees using standard time series distances, and leads to good performances compared to other competitive time series classifiers.
Complete list of metadata

Cited literature [10 references]  Display  Hide  Download
Contributor : Ahlame Douzal Connect in order to contact the contributor
Submitted on : Monday, October 15, 2012 - 3:25:37 PM
Last modification on : Wednesday, July 6, 2022 - 4:24:56 AM
Long-term archiving on: : Saturday, December 17, 2016 - 1:03:26 AM


Files produced by the author(s)


  • HAL Id : hal-00741944, version 1


Ahlame Douzal-Chouakria, Cécile Amblard. Adaptive split test for multivariate time series classification trees. CAp 2012 - Conférence Francophone sur l'Apprentissage Automatique, May 2012, Nancy, France. 16p. ⟨hal-00741944⟩



Record views


Files downloads