Learning recurrent behaviors from heterogeneous multivariate time-series
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. 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. 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. The results are very promising. They also highlight the difficulty of tuning the parameters of the method.
heterogeneous multivariate time-series time-series mining temporal pattern unsupervised learning activity monitoring