Clustering time series is still very much an open problem. Though many algorithms, evaluation criteria and similarity measures exist, they often inadequately treat temporal data. This research project focuses on a novel similarity metric for time series data. In state-of-the-art metrics, the time series usually is vectorized, and then a vector-distance is chosen. Quite often, some transformations are done on the data in order to find the best fit for two time series, but ultimately, a lot of temporal information is discarded. We try to generalize the cepstral distance, as explored by R. Martin, K. De Cock and prof. De Moor, to a more general class of time series. this distance measure has a fundamental description in both statistics and information theory, and makes use of the full dynamical and temporal interpretation of the time series, rather than of a vector representation of it.The ultimate goal is to have an insightful way to cluster a broad class of time series, and apply this to real-life problems as smart grid segmentation and customer relation management in banks.