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Reseach Article

Prediction of Stock Market using C-means Clustering and Particle Filter

by Ahmed Haj Darwish, Aliaa Hilal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 2
Year of Publication: 2017
Authors: Ahmed Haj Darwish, Aliaa Hilal

Ahmed Haj Darwish, Aliaa Hilal . Prediction of Stock Market using C-means Clustering and Particle Filter. International Journal of Computer Applications. 179, 2 ( Dec 2017), 12-19. DOI=10.5120/ijca2017915876

@article{ 10.5120/ijca2017915876,
author = { Ahmed Haj Darwish, Aliaa Hilal },
title = { Prediction of Stock Market using C-means Clustering and Particle Filter },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2017 },
volume = { 179 },
number = { 2 },
month = { Dec },
year = { 2017 },
issn = { 0975-8887 },
pages = { 12-19 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2017915876 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T00:54:14.634670+05:30
%A Ahmed Haj Darwish
%A Aliaa Hilal
%T Prediction of Stock Market using C-means Clustering and Particle Filter
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 2
%P 12-19
%D 2017
%I Foundation of Computer Science (FCS), NY, USA

In this article, Particle Filter and C-means are used to predict a value of a point in a time series. Similar data in a time-series are grouped using C-means algorithm. Afterward, a number of particle filters are used as sub-predictors. These sub-predictors start from different points, which are the centers of clusters resulted from clustering algorithm. Outputs from all filters were used to obtain Final prediction result. A weighted average method is used to aggregate the outputs of the filters. Particle filters are used in here to model non-Gaussian time series. Benchmark datasets were used to evaluate the proposed algorithm. To measure its prediction performance, the results derived from the proposed model were compared with those of other algorithms. The comparison proved the effectiveness and accuracy of the proposed method.

  1. Brockwell, P. J. and Davis, R. A. Introduction to Time Series and Forecasting,3 th ed, Verlag New York: Springer, 2016, p. 449.Ding, W. and Marchionini, G. 1997 A Study on Video Browsing Strategies. Technical Report. University of Maryland at College Park.
  2. Samanta, B. "Prediction of chaotic time series using computational intelligence," Expert Systems with Applications, vol. 38, no. 9, pp. 11406-11411, 2011.Tavel, P. 2007 Modeling and Simulation Design. AK Peters Ltd.
  3. Sapkal, S., Barate, T., Kadbane, N., Pimple , M. and Kumbharkar, P. "Comparative Study and Analysis of Stock Market Prediction Algorithms," International Journal of Innovative Science, Engineering & Technology, vol. 3, March 2016.
  4. Adebiyi, A. A., Adewumi, A. O. and Ayo, C. K. "Stock Price Prediction Using the ARIMA Model," UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, pp. 106-112, 2014.
  5. Adebiyi, A. A, Adewumi, A. O and Ayo, C. K. "Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction," Journal of Applied Mathematics, vol. 2014, p. 7 pages, 2014.
  6. Abhishek, K., Khairwa, A. and Sur, T. P. "A Stock Market Prediction Model using Artificial Neural Network," International Conference on Computing, Communications and Networking Technologies (ICCCNT),IEEE-20180, 26 th -28 th July 2012.
  7. Somani, P., Talele, S. and Sawant, S. "Stock Market Prediction Using Hidden Markov Model," IEEE, 2014.
  8. LIN, Y., GUO, H. and HU, J. "An SVM-based Approach for Stock Market Trend Prediction," in Neural Networks (IJCNN), The 2013 International Joint Conference on.IEEE, pp. 1-7, 2013.
  9. Gupta, A. and Dhingra, B. "Stock Market Prediction Using Hidden Markov Models," Engineering and Systems (SCES) Students Conference on, IEEE, 2012.
  10. Gupta, A. and Sharma, S. D. "Clustering-Classification Based Prediction of Stock Market Future Prediction," (IJCSIT) International Journal of Computer Science and Information Technologies, vol. 5, no. 3, pp. 2806-2809, 2014.
  11. Nguyen, N. Thi. "Probabilistic Methods in Estimation and Prediction of Financial Models," Florida State University Libraries, PHD dissertation, p. 86, 2014.
  12. Liang-Ying, W. "A hyprid ANFIS model based on emprical model decomposition for stcok time series forecasting," Appl Soft Comput, 2016.
  13. Marzi, H., Haj Darwish, A. and Helfawi, H. "Training ANFIS using The Enhanced Bees Algorithm and Least Squares Estimation," Intelligent Automation & Soft Computing, 2016.
  14. Xu, Y. and Zhang, G. "Application of Kalman Filter in the Prediction of Stock Price," International Symposium on Knowledge Acquisition and Modeling (KAM 2015), 2015.
  15. Taylor, S. J. Modeling financial time series, 2nd ed., World Scientific Publishing, 2007, p. 297.
  16. Adhikari, R. and Agrawal, R. K. "An Introductory Study on Time Series Modeling and Forecasting," LAP Lambert Academic Publishing, p. 67, 26 Feb 2013.
  17. Chatfield, C. The Analysis of Time Series: An Introduction, 6 th ed., 2016, p. 352 pages.
  18. Jafar, O. A. M. and Sivakumar, R. "A Comparative Study of Hard and Fuzzy Data Clustering Algorithms with Cluster Validity Indices," Proceeding of International Conference on Emerging Research in Computing, Information, Communication and Applications, 2013.
  19. Velmurugan, T. "Performance Comparison between k-Means and Fuzzy C-Means Algorithms using Arbitrary Data Points," Wulfenia Journal, vol. 19, pp. 234-241, 2012.
  20. Lu, Y., Ma, T., Yin, C., Xie, X., Tian, W. and Zhong, S. "Implementation of the Fuzzy C-Means Clustering Algorithm in Meteorological Data," International Journal of Database Theory and Application, vol. 6, pp. 1-18, 2013.
  21. Kannan, S., Ramathilagam, S. and Chung, P. "Effective fuzzy c-means clustering algorithms for data clustering problems," Expert Systems with Applications, p. 6292–6300, 2012.
  22. Chadaporn, K., Baber, J. and Bakhtyar, M. "Simple Example of Applying Extended Kalman Filter," 1st International Electrical Engineering Congress (iEEECON2013), March 2014.
  23. Hilal, A. and Haj Darwish, A. "A Novel Method for Stock Price Prediction based on Fuzzy Clustering and Extended Kalman Filter," Res. J. of Aleppo Univ., Engineering Sciences Series (2), no. 136, 2017.
  24. De Bernardis, C., Vicente-Guijalba, F., Martinez-Marin, T. and Lopez-Sanchez, J. M. "Particle Filter Approach for Real-Time Estimation of Crop Phenological States Using Time Series of NDVI Images," Remote Sensing, 20 July 2016.
  25. Arulampalam, M. S., Maskell, S., Gordon, N. and Clapp, T. "A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking," IEEE TRANSACTIONS ON SIGNAL PROCESSING, vol. 50, FEBRUARY 2002.
  26. Murphy, J. "Bayesian methods for high frequency financial time series analysis," Cambridge Univ. Dept. of Eng., Cambridge, U.K, 2010.
  27. Yin, S. "Intelligent Particle Filter and Its Application on Fault Detection of Nonlinear System," IEEE Transactions on Industrial Electronics, p. 10, 2015.
  28. Capitaine, H. L. and Frelicot, C. "A cluster validity index combining an overlap measure and a separation measure based on fuzzy aggregation operators," IEEE Transactions on Fuzzy Systems, July 2011.
  29. Trujillo-Ortiz, A. and Hernández Walls, R. "Mskekur: Mardia's multivariate skewness and kurtosis coefficients and its hypotheses testing," 2003. [Online]. Available: [Accessed 13/ 9/ 2017].
  30. Trujillo-Ortiz, A., Hernández Walls, R. and Barba-Rojo, K. "HZmvntest: Henze-Zirkler's Multivariate Normality Test," 2007. [Online]. Available: objectId=17931. [Accessed 13/ 9/ 2017].
  31. Trujillo-Ortiz, A., Hernández Walls, R., Barba-Rojo, K. and Cupul-Magana, L. "Roystest: Royston's Multivariate Normality Test," 2007. [Online]. Available: [Accessed 13/ 9/ 2017].
  32. "YAHOO FINANCE," [Online]. Available: [Accessed 8/ 2017].
Index Terms

Computer Science
Information Sciences


Prediction Time Series C-means Particle filter Stock price Importance Resampling.