# Comparison between DeepESNs and gated RNNs on multivariate time-series prediction

arXiv: https://arxiv.org/abs/1812.11527 · HTML: https://mlsanity.com/paper/1812.11527/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2018.

## Built on by (most cited first, 4 of 4)

- [Echo State Speech Recognition](https://mlsanity.com/paper/2102.09114/) (2021)
- [Modelling Neuronal Behaviour with Time Series Regression: Recurrent Neural Networks on C. Elegans Data](https://mlsanity.com/paper/2107.06762/) (2021)
- [Predicting Citi Bike Demand Evolution Using Dynamic Graphs](https://mlsanity.com/paper/2212.09175/) (2022)
- [Multiscale autonomous forecasting of plasma systems' dynamics using neural networks](https://mlsanity.com/paper/2502.11203/) (2025)

## Abstract (arXiv)

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.