I will present in this talk a modification of the dynamic time warping distance which is, unlike the original quantity, differentiable in all of its inputs. As a result, that alternative distance can be used naturally as a learning loss to learn with datasets of time series, to produce means, clusters or structured prediction where the goal is to forecast entire time series.