In a way similar to the string-to-string correction problem we address time series similarity in the light of a time-series-to-time-series-correction problem for which the similarity between two time series is measured as the minimum cost sequence of "edit operations" needed to transform one time series into another. To define the "edit operations" we use the paradigm of a graphical editing process and end up with a dynamic programming recursive algorithm we call Time Warp Edit Distance (TWED). TWED is slightly different in form from Dynamic Time Warping or Edit Distance with Real Penalty algorithms. In particular it highlights a parameter that drives a kind of stiffness of the elastic measure. We show that the similarity provided by TWED is a metric potentially useful in time series retrieval applications since they could benefit from the triangular inequality property to speed up the retrieval process while tuning the stiffness of the elastic measure. Empiric quality of the TWED distance is evaluated on a simple classification task. Compared to Edit Distance, Dynamic Time Warping and Edit Distance with Real Penalty, TWED exhibits promising behaviors.