Fuzzy clustering to classify several time series models with fractional Brownian motion errors
Mohammad Reza Mahmoudi, Dumitru Bǎleanu, Sultan Noman Qasem, Amirhosein Mosavi, Shahab S. Band · Alexandria Engineering Journal · 2020
In real world problems, scientists aim to classify and cluster several time series processes that can be used for a dataset. In this research, for the first time, based on fuzzy clustering method, an approach is applied to classify and cluster several time series models with fractional Brownian motion errors as candidates to fit on a dataset. The ability of the introduced technique is studied using simulation and real world example.