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JACIII Vol.19 No.2 pp. 312-318
doi: 10.20965/jaciii.2015.p0312
(2015)

Paper:

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PSO Based Deterministic ESN Models for Stock Price Forecasting

Jingpei Dan*1,†, Wenbo Guo*2, Weiren Shi*3, Bin Fang*1, and Tingping Zhang*4

*1College of Computer Science, Chongqing University
No.174 Shazhengjie, Shapingba, Chongqing 400044, China

*2College of Computing and Software Systems, University of Washington Bothell
18504 126th Ave NE Apt 1913, Bothell, WA 98011, United States

*3School of Automation, Chongqing University
No.174 Shazhengjie, Shapingba, Chongqing 400044, China

*4College of Information Science and Engineering, Chongqing Jiaotong University
No.66 Xuefu Road Nan’an District, Chongqing 400074, China

†Corresponding author

Received:
June 15, 2014
Accepted:
December 22, 2014
Published:
March 20, 2015
Creative Commons License
Keywords:
echo state networks, stock price forecasting, time series, particle swarm optimization
Abstract
Deterministic echo state network (ESN) models integrated with particle swarm optimization (PSO) are proposed to improve the accuracy and efficiency of stock price forecasting. ESNs have been successfully applied to financial time series forecasting because of their efficient and powerful computational ability in approximating nonlinear dynamical systems. However, reservoir construction in standard ESNs is primarily driven by a series of randomized model-building stages, because of which both researchers and practitioners have to rely on a series of trials and errors to determine parameters. An ESN with a deterministically constructed reservoir is comparable in performance to a standard ESN and has minimal complexity as well as potential for optimizations with regard to ESN parameters. In this paper, forecasting performances of the proposed PSO-DESN models are compared with those of standard ESNs for stock price prediction on the benchmark dataset of S&P 500. The comparison results demonstrate that the proposed PSO-DESNs exhibit better performance in stock price forecasting in terms of both accuracy and efficiency, thereby verifying the potential of PSO-DESNs for financial predictions.
Cite this article as:
J. Dan, W. Guo, W. Shi, B. Fang, and T. Zhang, “PSO Based Deterministic ESN Models for Stock Price Forecasting,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.2, pp. 312-318, 2015.
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