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 hyper-parameters. 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.
CITATION STYLE
Atencia, M., Gallicchio, C., Joya, G., & Micheli, A. (2020). Time Series Clustering with Deep Reservoir Computing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12397 LNCS, pp. 482–493). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-61616-8_39
Mendeley helps you to discover research relevant for your work.