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 $\mathcal{C}$-means (A-FCM) algorithm. The DCS parametric modeling 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.

本文开发了一种新的时间序列聚类程序，可以实现异方差性，非正态性和模型的非线性。为此，我们采用一种模糊的方法。具体来说，考虑动态条件评分（DCS）模型，我们建议通过基于自相关的模糊算法，根据时间序列的估计条件矩对时间序列进行聚类。$\mathcal{C}$-均值（A-FCM）算法。DCS参数化建模之所以具有吸引力，是因为其通用性和计算可行性。使用模拟数据的实验以及金融时间序列的一些经验应用（假设线性和非线性模型的规格以及关于时间序列密度函数的几种假设），说明了所提出程序的有用性。