Multivariate time-series clustering for vehicle usage states identification

This thesis aims to automatically identify vehicle usage states by analyzing the data collected through remotely controlled sensors. Since the considered time series are unlabeled, unsupervised clustering techniques have been exploited. The proposed solution is a two-step algorithm that merges the precision of the univariate methods to the general information that the multivariate algorithms can provide.