Bidirectional deep echo state networks
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In this work we propose a deep architecture for the classification of multivariate time series. By means of a recurrent and untrained reservoir we generate a vectorial representation that embeds the temporal relationships in the data. To overcome the limitations of the reservoir vanishing memory, we introduce a bidirectional reservoir, whose last state captures also the past dependencies in the input. We apply dimensionality reduction to the final reservoir states to obtain compressed fixed size representations of the time series. These are subsequently fed into a deep feedforward network, which is trained to perform the final classification. We test our architecture on benchmark datasets and on a real-world use-case of blood samples classification. Results show that our method performs better than a standard echo state network, and it can be trained much faster than a fully-trained recurrent network.
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