Learning Multiple Temporal Matching for Time Series Classification

Cedric Frambourg (1) , Ahlame Douzal-Chouakria (1) , Eric Gaussier (1)

1 LIG - Laboratoire d'Informatique de Grenoble

In real applications, time series are generally of complex structure, exhibiting different global behaviors within classes. To discriminate such challenging time series, we propose a multiple temporal matching approach that reveals the commonly shared features within classes, and the most differential ones across classes. For this, we rely on a new framework based on the variance/covariance criterion to strengthen or weaken matched observations according to the induced variability within and between classes. The experiments performed on real and synthetic datasets demonstrate the ability of the multiple temporal matching approach to capture fine-grained distinctions between time series.

Mots clés: Temporal data; load profile; supervised classification; multiple temporal alignement

Domaines: Machine Learning [stat.ML]

Cedric Frambourg, Ahlame Douzal-Chouakria, Eric Gaussier. Learning Multiple Temporal Matching for Time Series Classification. Intelligent Data Analysis, 2013, London, United Kingdom. pp.198-209. ⟨hal-00881159⟩