This work focuses on grouping timed sequences, i.e., sequen\-ces that are irregular, with non-identical temporal differences between two observations, and multivariate, where observations are detailed along multiple dimensions.
A similarity between timed sequences, accounting for these particularities, is  essential to achieve these groupings. Several alignment algorithms exist for this purpose, such as the classical Dynamic Time Warping (DTW) distance and its robust to outlier extension, Drop-DTW. 
Recently, Drop-DTW has been successfully extended to temporal sequences and applied to the grouping of care pathways. Our contribution extends Drop-DTW to multivariate sequences, with the goal of matching observations on some subset of the dimensions. 
One drawback of such a similarity measure lies in the number of parameters. Considering the downstream task, namely grouping sequences using clustering, we propose an approach to tune the parameters by optimizing the classical silhouette score with Bayesian optimization. Our tests on real datasets show the effectiveness of using this extension of Drop-DTW  to cluster multivariate timed sequences.

Keywords : Dynamic Time Warping,  Bayesian optimization and Multivariate irregular timed sequences.