Echo state networks and extreme learning machines: a comparative study on seasonal streamflow series prediction

Authors:
Hugo Siqueira;Levy Boccato;Romis Attux;Christiano Lyra
Affiliations:
Systems Engineering Department (DENSIS), School of Electrical and Computer Engineering, University of Campinas, Campinas, SP, Brazil;Department of Computer Engineering and Industrial Automation (DCA), School of Electrical and Computer Engineering, University of Campinas, Campinas, SP, Brazil;Department of Computer Engineering and Industrial Automation (DCA), School of Electrical and Computer Engineering, University of Campinas, Campinas, SP, Brazil;Systems Engineering Department (DENSIS), School of Electrical and Computer Engineering, University of Campinas, Campinas, SP, Brazil
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Year:
2012

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Abstract

Extreme Learning Machines (ELMs) and Echo State Networks (ESNs) represent promising alternatives in time series forecasting in view of their intrinsic trade-off between performance and mathematical tractability. Both approaches share a key feature: their supervised parameter adaptation is restricted to the output layer, the remaining synaptic weights being chosen according to a priori unsupervised schemes. This work performs a comparative investigation regarding the performances of a classic ELM and ESNs in the context of the prediction of monthly seasonal streamflow series associated with Brazilian hydroelectric plants. With respect to the ESN, two possible reservoir design approaches are tested, as well as the novel architecture of Boccato et al., which is characterized by the use a Volterra filter and PCA in the readout. Additionally, a MLP is included to establish a base for comparison. Results show the relevance of these architectures in modeling seasonal streamflow series.