title: Learning recurrent behaviors from heterogeneous multivariate time-series.
abstract: OBJECTIVE
  For the last years, time-series mining has become a challenging issue for researchers. An important application lies in most monitoring purposes, which require analyzing large sets of time-series for learning usual patterns. Any deviation from this learned profile is then considered as an unexpected situation. Moreover, complex applications may involve the temporal study of several heterogeneous parameters. In that paper, we propose a method for mining heterogeneous multivariate time-series for learning meaningful patterns.
  
  
  METHODS
  The proposed approach allows for mixed time-series - containing both pattern and non-pattern data - such as for imprecise matches, outliers, stretching and global translating of patterns instances in time.
  
  
  RESULTS
  We present the results of our approach on synthetic data generated in the context of monitoring a person at home, as well as early results on few real sequences. The purpose is to build a behavioral profile of a person in their daily activities by analyzing the time variations of several quantitative or qualitative parameters recorded through a provision of sensors.
  
  
  CONCLUSIONS
  The results are very promising. They also highlight the difficulty of tuning the parameters of the method.
authors.affiliation: Laboratory TIMC-IMAG, Institut d'Ingénierie de l'Information de Santé, Faculté de médecine de Grenoble, 38706 La Tronche Cedex, France. floduchene@yahoo.fr
authors.forename: Florence
authors.initials: F
authors.lastname: Duchêne
authors.forename: Catherine
authors.initials: C
authors.lastname: Garbay
authors.forename: Vincent
authors.initials: V
authors.lastname: Rialle
mesh_terms: D001519:Behavior; D006801:Humans; D007858:Learning; D015999:Multivariate Analysis