Open Access
2014 Deterministic Echo State Networks Based Stock Price Forecasting
Jingpei Dan, Wenbo Guo, Weiren Shi, Bin Fang, Tingping Zhang
Abstr. Appl. Anal. 2014(SI11): 1-6 (2014). DOI: 10.1155/2014/137148


Echo state networks (ESNs), as efficient and powerful computational models for approximating nonlinear dynamical systems, have been successfully applied in financial time series forecasting. Reservoir constructions in standard ESNs rely on trials and errors in real applications due to a series of randomized model building stages. A novel form of ESN with deterministically constructed reservoir is competitive with standard ESN by minimal complexity and possibility of optimizations for ESN specifications. In this paper, forecasting performances of deterministic ESNs are investigated in stock price prediction applications. The experiment results on two benchmark datasets (Shanghai Composite Index and S&P500) demonstrate that deterministic ESNs outperform standard ESN in both accuracy and efficiency, which indicate the prospect of deterministic ESNs for financial prediction.


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Jingpei Dan. Wenbo Guo. Weiren Shi. Bin Fang. Tingping Zhang. "Deterministic Echo State Networks Based Stock Price Forecasting." Abstr. Appl. Anal. 2014 (SI11) 1 - 6, 2014.


Published: 2014
First available in Project Euclid: 6 October 2014

zbMATH: 07021784
Digital Object Identifier: 10.1155/2014/137148

Rights: Copyright © 2014 Hindawi

Vol.2014 • No. SI11 • 2014
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