RT Conference Proceedings
T1 Time Series Clustering with Deep Reservoir Computing
A1 Atencia-Ruiz, Miguel Alejandro
A1 Gallicchio, Claudio
A1 Joya-Caparrós, Gonzalo
A1 Micheli, Alessio
K1 Análisis cluster -- Programas de ordenador
AB This paper proposes a method for clustering of time series, based upon the ability of deep Reservoir Computing networks to graspthe 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.
PB Springer
YR 2020
FD 2020
LK https://hdl.handle.net/10630/22641
UL https://hdl.handle.net/10630/22641
LA eng
NO 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
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 25 ene 2026