  This paper develops a new time series clustering procedure allowing for
heteroskedasticity, non-normality and model's non-linearity. At this aim, we
follow a fuzzy approach. Specifically, considering a Dynamic Conditional Score
(DCS) model, we propose to cluster time series according to their estimated
conditional moments via the Autocorrelation-based fuzzy C-means (A-FCM)
algorithm. The DCS parametric modelling is appealing because of its generality
and computational feasibility. The usefulness of the proposed procedure is
illustrated using an experiment with simulated data and several empirical
applications with financial time series assuming both linear and nonlinear
models' specification and under several assumptions about time series density
function.
