In this article we address the problem of separation of shape and time components in time series. In statistics, this problem relates to the separation of amplitude and phase variations. The concept of shape that we tackle is termed temporally neutral to consider that it may possibly exist outside of any temporal specification, as it is the case for a geometric form. We propose to exploit and adapt a probabilistic temporal alignment algorithm, initially designed to estimate the centroid of a set of time series, to build some heuristic elements of solution to this separation problem. We show on some controlled synthetic data that this algorithm meets empirically the initial objectives. We finally evaluate it on real data, in the context of some online handwritten signature authentication tasks. On the evaluated benchmarks, our approach is positioned slightly above the current state of the art showing the applicative benefit of this separation problem.