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oai:riuma.uma.es:10630/226412026-02-03T11:58:39Zcom_10630_2254col_10630_37959

Time Series Clustering with Deep Reservoir Computing Atencia-Ruiz, Miguel Alejandro Gallicchio, Claudio Joya-Caparrós, Gonzalo Micheli, Alessio Análisis cluster -- Programas de ordenador This paper proposes a method for clustering of time series, based upon the ability of deep Reservoir Computing networks to grasp the dynamical structure of the series that is presented as input. A standard clustering algorithm, such as k-means, is applied to the network states, rather than the input series themselves. Clustering is thus embedded into the network dynamical evolution, since a clustering result is obtained at every time step, which in turn serves as initialisation at the next step. We empirically assess the performance of deep reservoir systems in time series clustering on benchmark datasets, considering the influence of crucial hyperparameters. Experimentation with the proposed model shows enhanced clustering quality, measured by the silhouette coefficient, when compared to both static clustering of data, and dynamic clustering with a shallow network. 2021-07-14T07:56:18Z 2021-07-14T07:56:18Z 2021 2020 conference output Atencia M., Gallicchio C., Joya G., Micheli A. (2020) Time Series Clustering with Deep Reservoir Computing. In: Farkaš I., Masulli P., Wermter S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2020. ICANN 2020. Lecture Notes in Computer Science, vol 12397. Springer, Cham. https://doi.org/10.1007/978-3-030-61616-8_39 https://hdl.handle.net/10630/22641 eng 29th International Conference on Artificial Neural Networks (ICANN 2020) Bratislava, Eslovaquia Septiembre 2020 open access Springer