  We propose an experimental comparison between Deep Echo State Networks
(DeepESNs) and gated Recurrent Neural Networks (RNNs) on multivariate
time-series prediction tasks. In particular, we compare reservoir and
fully-trained RNNs able to represent signals featured by multiple time-scales
dynamics. The analysis is performed in terms of efficiency and prediction
accuracy on 4 polyphonic music tasks. Our results show that DeepESN is able to
outperform ESN in terms of prediction accuracy and efficiency. Whereas, between
fully-trained approaches, Gated Recurrent Units (GRU) outperforms Long
Short-Term Memory (LSTM) and simple RNN models in most cases. Overall, DeepESN
turned out to be extremely more efficient than others RNN approaches and the
best solution in terms of prediction accuracy on 3 out of 4 tasks.
