Comparison of Model-Free Algorithms For Clustering GARCH Processes

Abstract

In this paper, we evaluate several model-free algorithms for clustering time series datasets generated by GARCH processes. In extensive experiments, we generate synthetic datasets in different scenarios. Then, we compare K-Means (for Euclidian and dynamic time warping distance), K-Shape, and Kernel K-Means models with different clustering metrics. Several experiments show that the K-Means model with dynamic time warping distance archives comparably better results. However, the considered models have significant shortcomings in improving the clustering accuracy when the amount of information (the minimum length of the time series) increases, and in performing accurate clustering when data is unbalanced or clusters are overlapping.