Heinrich-Heine-Universität Düsseldorf

Nowadays, time series data are ubiquitous, encompassing various domains like medicine, economics, energy, climate science and the Internet of Things. One crucial task in analysing these data is clustering, aiming to find patterns that indicate previously undiscovered relationships among features or specific groups of objects. In this work, we present a novel framework for the clustering of multiple multivariate time series over time that utilises multi-objective optimisation to determine the temporal clustering solution for each time point. To highlight the strength of our framework, we conduct a comparison with alternative solutions using multiple labelled real-world datasets. Our results reveal that our method not only provides better results but also enables a comparison between datasets with regard to their temporal dependencies.