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Abstract and Applied Analysis
Volume 2014, Article ID 137148, 6 pages
Research Article

Deterministic Echo State Networks Based Stock Price Forecasting

1College of Computer Science, Chongqing University, Chongqing 400044, China
2School of Automation, Chongqing University, Chongqing 400044, China
3College of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China

Received 15 April 2014; Accepted 21 May 2014; Published 26 June 2014

Academic Editor: Fuding Xie

Copyright © 2014 Jingpei Dan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.