In this work, a new approach to cluster large sets of time series is presented. The proposed methodology takes into account the dependency among the time series to obtain a fuzzy partition of the set of observations. A two-step procedure to accomplish this is presented. First, the cophenetic distances, based on a time series linear cross-dependency measure, are obtained. Second, these distances are used as an input of a non-Euclidean fuzzy relational clustering algorithm. As a result, we obtain a robust fuzzy procedure capable of detecting groups of time series with different types of cross-dependency. We illustrate the usefulness of the stated methodology through some Monte Carlo experiments and a real data example. Our results show that the methodology proposed in this work substantially improves the hard partitioning clustering alternative.

在这项工作中，提出了一种聚类大型时间序列集的新方法。所提出的方法考虑了时间序列之间的依赖关系，以获得一组观测值的模糊划分。提供了一个两步过程来完成此操作。首先，获得基于时间序列线性交叉相关性度量的共表距离。其次，这些距离被用作非欧式模糊关系聚类算法的输入。因此，我们获得了一个鲁棒的模糊程序，能够检测具有不同类型交叉依赖性的时间序列组。我们通过一些蒙特卡罗实验和一个真实的数据示例来说明所述方法的实用性。我们的结果表明，这项工作中提出的方法大大改进了硬分区聚类替代方案。