4.7 Article

Hierarchical Echo State Network With Sparse Learning: A Method for Multidimensional Chaotic Time Series Prediction

Journal

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2022.3157830

Keywords

Reservoirs; Time series analysis; Computational modeling; Task analysis; Predictive models; Convergence; Analytical models; Echo state network; hierarchical strategy; multidimensional; sparse learning; time series prediction

Ask authors/readers for more resources

This study proposes a new method called HESN-SL for multidimensional chaotic time series prediction. The method employs stacked reservoirs to mine and capture hidden latent evolution patterns, and uses sparse learning and variable selection capability to train the output layer. Experimental results demonstrate that HESN-SL outperforms other methods in prediction performance.
Echo state network (ESN), a type of special recurrent neural network with a large-scale randomly fixed hidden layer (called a reservoir) and an adaptable linear output layer, has been widely employed in the field of time series analysis and modeling. However, when tackling the problem of multidimensional chaotic time series prediction, due to the randomly generated rules for input and reservoir weights, not only the representation of valuable variables is enriched but also redundant and irrelevant information is accumulated inevitably. To remove the redundant components, reduce the approximate collinearity among echo-state information, and improve the generalization and stability, a new method called hierarchical ESN with sparse learning (HESN-SL) is proposed. The HESN-SL mines and captures the latent evolution patterns hidden from the dynamic system by means of layer-by-layer processing in stacked reservoirs, and leverage monotone accelerated proximal gradient algorithm to train a sparse output layer with variable selection capability. Meanwhile, we further prove that the HESN-SL satisfies the echo state property, which guarantees the stability and convergence of the proposed model when applied to time series prediction. Experimental results on two synthetic chaotic systems and a real-world meteorological dataset illustrate the proposed HESN-SL outperforms both original ESN and existing hierarchical ESN-based models for multidimensional chaotic time series prediction.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available