2026-06-01T22:07:44Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/177712026-02-03T12:33:27Zcom_10630_2254col_10630_37959

Dynamic clustering of time series with Echo State Networks Atencia-Ruiz, Miguel Alejandro Stoean, Catalin Stoean, Ruxandra Rodriguez Labrada, Roberto Joya-Caparrós, Gonzalo Redes neuronales artificiales Congresos y conferencias In this paper we introduce a novel methodology for unsupervised analysis of time series, based upon the iterative implementation of a clustering algorithm embedded into the evolution of a recurrent Echo State Network. The main features of the temporal data are captured by the dynamical evolution of the network states, which are then subject to a clustering procedure. We apply the proposed algorithm to time series coming from records of eye movements, called saccades, which are recorded for diagnosis of a neurodegenerative form of ataxia. This is a hard classification problem, since saccades from patients at an early stage of the disease are practically indistinguishable from those coming from healthy subjects. The unsupervised clustering algorithm implanted within the recurrent network produces more compact clusters, compared to conventional clustering of static data, and provides a source of information that could aid diagnosis and assessment of the disease. 2019-06-05T09:51:07Z 2019-06-05T09:51:07Z 2019 2019-06-05 conference output https://hdl.handle.net/10630/17771 eng 15th International Work-Conference on Artificial Neural Networks Gran Canaria Junio 2019 open access