Top > JACIII > PSO Based Deterministic ESN Models for Stock Price Forecasting

single-jc.php

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

Paper:

Views over last 60 days: 952

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.
Data files:
References
  1. [1] H. Jaeger and H. Haas, “Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication,” Science, Vol.304, No.5667, pp. 78-80, 2004.
  2. [2] Q. Song, X. Liu, and X. Zhao, “Short-term Traffic Flow and Hourly Electric Load Forecasting Algorithm based on Echo State Neural Networks,” Int. J. of Digital Content Technology & its Applications, Vol.6, No.4, 2012.
  3. [3] Z. Shi and M. Han, “Support vector echo-state machine for chaotic time-series prediction,” IEEE Trans. on Neural Networks, Vol.18, No.2, pp. 359-372, 2007.
  4. [4] C. Sheng, J. Zhao, Y. Liu, and W. Wang. “Prediction for noisy nonlinear time series by echo state network based on dual estimation,” Neurocomputing, Vol.82, pp. 186-195, 2012.
  5. [5] Y. Kara, M. A. Boyacioglu, and Ö. K. Baykan, “Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange,” Expert systems with Applications, Vol.38, No.5, pp. 5311-5319, 2011.
  6. [6] F. Zhai, X. Lin, Z. Yang, and Y. Song, “Financial time series prediction based on echo state network,” In Natural Computation (ICNC), 2010 6th Int. Conf. on, IEEE, Vol.8, pp. 3983-3987, 2010.
  7. [7] H. Zhang, J. Liang, and Z. Chai, “Stock Prediction Based on Phase Space Reconstruction and Echo State Networks,” J. of Algorithms & Computational Technology, Vol.7, No.1, pp. 87-100, 2013.
  8. [8] X. Lin, Z. Yang, and Y. Song, “Short-term stock price prediction based on echo state networks,” Expert systems with applications, Vol.36, No.3, pp. 7313-7317, 2009.
  9. [9] X. Lin, Z. Yang, and Y. Song, “Intelligent stock trading system based on improved technical analysis and Echo State Network,” Expert systems with Applications, Vol.38, No.9, pp. 11347-11354, 2011.
  10. [10] A. Rodan and P. Tivno, “Minimum complexity echo state network,” IEEE Trans. on Neural Networks, Vol.22, No.1, pp. 131-144, 2011.
  11. [11] A. Rodan and P. Tivno, “Simple deterministically constructed cycle reservoirs with regular jumps,” Neural computation, Vol.24, No.7, pp. 1822-1852, 2012.
  12. [12] A. Rodan and P. Tivno, “A. Short term memory in input-driven linear dynamical systems,” Neurocomputing, Vol.112, pp. 58-63, 2013.
  13. [13] J. Kennedy and R. Eberhart, “Particle swarm optimization,” Proc. of the 1995 IEEE Int. Conf. on Neural Networks, 1995.
  14. [14] M. R. Sierra and C. C. Coello, “Multi-objective particle swarm optimizers: a survey of the state-of-the-art,” Int. J. of Computational Intelligence Research, Vol.23, pp. 287-308, 2006.
  15. [15] M. Clerc and J. Kennedy, “The particle swarm explosion, stability, and convergence in a multidimensional complex space,” IEEE Trans. on Evolutionary Computation, Vol.6, No.1, pp. 58-73, 2002.
  16. [16] A. Engelbrecht, “Fundamentals of Computational swarm Intelligence,” John Wiley & Sons, 2005.
  17. [17] R. Hassan, B. Cohanim, O. de Weck, and G. Venter, “A comparison of particle swarm optimization and the genetic algorithm,” Proc. of the 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conf., No.AIAA-2005-1897, 2005.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on May. 14, 2025