Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014, Article ID 610594, 10 pages
http://dx.doi.org/10.1155/2014/610594
Research Article

Multilayer Stock Forecasting Model Using Fuzzy Time Series

Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia

Received 31 August 2013; Accepted 17 November 2013; Published 29 January 2014

Academic Editors: L. Koczy and Z. Wang

Copyright © 2014 Hossein Javedani Sadaei and Muhammad Hisyam Lee. 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.

Linked References

  1. D. Xiao and J. Wang, “Modeling stock price dynamics by continuum percolation system and relevant complex systems analysis,” Physica A, vol. 391, pp. 4827–4838, 2012. View at Google Scholar
  2. M. T. Leung, H. Daouk, and A.-S. Chen, “Forecasting stock indices: a comparison of classification and level estimation models,” International Journal of Forecasting, vol. 16, no. 2, pp. 173–190, 2000. View at Google Scholar · View at Scopus
  3. D. G. McMillan, “Non-linear forecasting of stock returns: does volume help?” International Journal of Forecasting, vol. 23, no. 1, pp. 115–126, 2007. View at Publisher · View at Google Scholar · View at Scopus
  4. D. Önkal and G. Muradoǧlu, “Effects of feedback on probabilistic forecasts of stock prices,” International Journal of Forecasting, vol. 11, no. 2, pp. 307–319, 1995. View at Google Scholar · View at Scopus
  5. A.-S. Chen, M. T. Leung, and H. Daouk, “Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index,” Computers & Operations Research, vol. 30, no. 6, pp. 901–923, 2003. View at Publisher · View at Google Scholar · View at Scopus
  6. E. Guresen, G. Kayakutlu, and T. U. Daim, “Using artificial neural network models in stock market index prediction,” Expert Systems with Applications, vol. 38, no. 8, pp. 10389–10397, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. J.-Z. Wang, J.-J. Wang, Z.-G. Zhang, and S.-P. Guo, “Forecasting stock indices with back propagation neural network,” Expert Systems with Applications, vol. 38, no. 11, pp. 14346–14355, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. C.-F. Huang, “A hybrid stock selection model using genetic algorithms and support vector regression,” Applied Soft Computing Journal, vol. 12, no. 2, pp. 807–818, 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. M. Versace, R. Bhatt, O. Hinds, and M. Shiffer, “Predicting the exchange traded fund DIA with a combination of genetic algorithms and neural networks,” Expert Systems with Applications, vol. 27, no. 3, pp. 417–425, 2004. View at Publisher · View at Google Scholar · View at Scopus
  10. T. Chavarnakul and D. Enke, “A hybrid stock trading system for intelligent technical analysis-based equivolume charting,” Neurocomputing, vol. 72, no. 16–18, pp. 3517–3528, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. W. Leigh, M. Paz, and R. Purvis, “An analysis of a hybrid neural network and pattern recognition technique for predicting short-term increases in the NYSE composite index,” Omega, vol. 30, no. 2, pp. 69–76, 2002. View at Publisher · View at Google Scholar · View at Scopus
  12. R. Tsaih, Y. Hsu, and C. C. Lai, “Forecasting S&P 500 stock index futures with a hybrid AI system,” Decision Support Systems, vol. 23, no. 2, pp. 161–174, 1998. View at Google Scholar · View at Scopus
  13. H. Dourra and P. Siy, “Investment using technical analysis and fuzzy logic,” Fuzzy Sets and Systems, vol. 127, no. 2, pp. 221–240, 2002. View at Publisher · View at Google Scholar · View at Scopus
  14. W. Huang, Y. Nakamori, and S.-Y. Wang, “Forecasting stock market movement direction with support vector machine,” Computers & Operations Research, vol. 32, no. 10, pp. 2513–2522, 2005. View at Publisher · View at Google Scholar · View at Scopus
  15. T.-L. Chen, C.-H. Cheng, and H. Jong Teoh, “Fuzzy time-series based on Fibonacci sequence for stock price forecasting,” Physica A, vol. 380, no. 1-2, pp. 377–390, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. C. H. Cheng, T. L. Chen, and C. H. Chiang, “Trend-weighted fuzzy time-series model for TAIEX forecasting,” in Neural Information Processing, I. King, J. Wang, L. W. Chan, and D. Wang, Eds., vol. 4234, pp. 469–477, Springer, Berlin, Germany, 2006. View at Google Scholar
  17. K. Huarng and H.-K. Yu, “A type 2 fuzzy time series model for stock index forecasting,” Physica A, vol. 353, no. 1–4, pp. 445–462, 2005. View at Publisher · View at Google Scholar · View at Scopus
  18. T. A. Jilani and S. M. A. Burney, “A refined fuzzy time series model for stock market forecasting,” Physica A, vol. 387, no. 12, pp. 2857–2862, 2008. View at Publisher · View at Google Scholar · View at Scopus
  19. L.-W. Lee, L.-H. Wang, and S.-M. Chen, “Temperature prediction and TAIFEX forecasting based on high-order fuzzy logical relationships and genetic simulated annealing techniques,” Expert Systems with Applications, vol. 34, no. 1, pp. 328–336, 2008. View at Publisher · View at Google Scholar · View at Scopus
  20. J.-W. Liu, T.-L. Chen, C.-H. Cheng, and Y.-H. Chen, “Adaptive-expectation based multi-attribute FTS model for forecasting TAIEX,” Computers & Mathematics with Applications, vol. 59, no. 2, pp. 795–802, 2010. View at Publisher · View at Google Scholar · View at Scopus
  21. H. J. Teoh, T.-L. Chen, C.-H. Cheng, and H.-H. Chu, “A hybrid multi-order fuzzy time series for forecasting stock markets,” Expert Systems with Applications, vol. 36, no. 4, pp. 7888–7897, 2009. View at Publisher · View at Google Scholar · View at Scopus
  22. H.-K. Yu, “Weighted fuzzy time series models for TAIEX forecasting,” Physica A, vol. 349, no. 3-4, pp. 609–624, 2005. View at Publisher · View at Google Scholar · View at Scopus
  23. T. H.-K. Yu and K.-H. Huarng, “A bivariate fuzzy time series model to forecast the TAIEX,” Expert Systems with Applications, vol. 34, no. 4, pp. 2945–2952, 2008. View at Publisher · View at Google Scholar · View at Scopus
  24. Q. Song and B. S. Chissom, “Forecasting enrollments with fuzzy time series—part I,” Fuzzy Sets and Systems, vol. 54, no. 1, pp. 1–9, 1993. View at Google Scholar · View at Scopus
  25. Q. Song and B. S. Chissom, “Fuzzy time series and its models,” Fuzzy Sets and Systems, vol. 54, no. 3, pp. 269–277, 1993. View at Google Scholar · View at Scopus
  26. S.-M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy Sets and Systems, vol. 81, no. 3, pp. 311–319, 1996. View at Google Scholar · View at Scopus
  27. K. Huarng, “Effective lengths of intervals to improve forecasting in fuzzy time series,” Fuzzy Sets and Systems, vol. 123, no. 3, pp. 387–394, 2001. View at Publisher · View at Google Scholar · View at Scopus
  28. S.-T. Li and Y.-P. Chen, “Natural partitioning-based forecasting model for fuzzy time-series,” in Proceedings of the IEEE International Conference on Fuzzy Systems, vol. 3, pp. 1355–1359, July 2004. View at Publisher · View at Google Scholar · View at Scopus
  29. H.-K. Yu, “A refined fuzzy time-series model for forecasting,” Physica A, vol. 346, no. 3-4, pp. 657–681, 2005. View at Publisher · View at Google Scholar · View at Scopus
  30. S.-M. Chen and N.-Y. Chung, “Forecasting enrollments using high-order fuzzy time series and genetic algorithms,” International Journal of Intelligent Systems, vol. 21, no. 5, pp. 485–501, 2006. View at Publisher · View at Google Scholar · View at Scopus
  31. C.-H. Cheng, J.-R. Chang, and C.-A. Yeh, “Entropy-based and trapezoid fuzzification-based fuzzy time series approaches for forecasting IT project cost,” Technological Forecasting and Social Change, vol. 73, no. 5, pp. 524–542, 2006. View at Publisher · View at Google Scholar · View at Scopus
  32. K. Huarng and T. H.-K. Yu, “Ratio-based lengths of intervals to improve fuzzy time series forecasting,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 36, no. 2, pp. 328–340, 2006. View at Publisher · View at Google Scholar · View at Scopus
  33. S.-T. Li and Y.-C. Cheng, “Deterministic fuzzy time series model for forecasting enrollments,” Computers & Mathematics with Applications, vol. 53, no. 12, pp. 1904–1920, 2007. View at Publisher · View at Google Scholar · View at Scopus
  34. S.-T. Li, Y.-C. Cheng, and S.-Y. Lin, “A FCM-based deterministic forecasting model for fuzzy time series,” Computers & Mathematics with Applications, vol. 56, no. 12, pp. 3052–3063, 2008. View at Publisher · View at Google Scholar · View at Scopus
  35. N.-Y. Wang and S.-M. Chen, “Temperature prediction and TAIFEX forecasting based on automatic clustering techniques and two-factors high-order fuzzy time series,” Expert Systems with Applications, vol. 36, no. 2, pp. 2143–2154, 2009. View at Publisher · View at Google Scholar · View at Scopus
  36. C. H. Aladag, M. A. Basaran, E. Egrioglu, U. Yolcu, and V. R. Uslu, “Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations,” Expert Systems with Applications, vol. 36, no. 3, pp. 4228–4231, 2009. View at Publisher · View at Google Scholar · View at Scopus
  37. C.-H. Su, T.-L. Chen, C.-H. Cheng, and Y.-C. Chen, “Forecasting the stock market with linguistic rules generated from the minimize entropy principle and the cumulative probability distribution approaches,” Entropy, vol. 12, no. 12, pp. 2397–2417, 2010. View at Publisher · View at Google Scholar · View at Scopus
  38. E. Egrioglu, C. H. Aladag, U. Yolcu, V. R. Uslu, and M. A. Basaran, “Finding an optimal interval length in high order fuzzy time series,” Expert Systems with Applications, vol. 37, no. 7, pp. 5052–5055, 2010. View at Publisher · View at Google Scholar · View at Scopus
  39. C.-H. Cheng, T.-L. Chen, H. J. Teoh, and C.-H. Chiang, “Fuzzy time-series based on adaptive expectation model for TAIEX forecasting,” Expert Systems with Applications, vol. 34, no. 2, pp. 1126–1132, 2008. View at Publisher · View at Google Scholar · View at Scopus
  40. S.-M. Chen and C.-D. Chen, “TAIEX forecasting based on fuzzy time series and fuzzy variation groups,” IEEE Transactions on Fuzzy Systems, vol. 19, no. 1, pp. 1–12, 2011. View at Google Scholar
  41. Q. Song and B. S. Chissom, “Forecasting enrollments with fuzzy time series—part II,” Fuzzy Sets and Systems, vol. 62, no. 1, pp. 1–8, 1994. View at Google Scholar · View at Scopus
  42. L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965. View at Google Scholar · View at Scopus
  43. N. J. Gonedes and H. V. Roberts, “Differencing of random walks and near random walks,” Journal of Econometrics, vol. 6, no. 3, pp. 289–308, 1977. View at Google Scholar · View at Scopus
  44. S. G. Koreisha and T. M. Pukkila, “New approaches for determining the degree of differencing necessary to induce stationarity in ARIMA models,” Journal of Statistical Planning and Inference, vol. 36, no. 2-3, pp. 399–412, 1993. View at Google Scholar · View at Scopus
  45. T.-L. Chen, C.-H. Cheng, and H.-J. Teoh, “High-order fuzzy time-series based on multi-period adaptation model for forecasting stock markets,” Physica A, vol. 387, no. 4, pp. 876–888, 2008. View at Publisher · View at Google Scholar · View at Scopus
  46. U. Yolcu, E. Egrioglu, V. R. Uslu, M. A. Basaran, and C. H. Aladag, “A new approach for determining the length of intervals for fuzzy time series,” Applied Soft Computing Journal, vol. 9, no. 2, pp. 647–651, 2009. View at Publisher · View at Google Scholar · View at Scopus
  47. S. R. Singh, “A computational method of forecasting based on fuzzy time series,” Mathematics and Computers in Simulation, vol. 79, no. 3, pp. 539–554, 2008. View at Publisher · View at Google Scholar · View at Scopus
  48. H. A. Sturges, “The choice of a class interval,” Journal of the American Statistical Association, vol. 21, pp. 65–66, 1926. View at Google Scholar
  49. Y. Leu and T.-I. Chiu, “An effective stock portfolio trading strategy using genetic algorithms and weighted fuzzy time series,” in Proceedings of the 16th North-East Asia Symposium on Nano, Information Technology and Reliability (NASNIT '11), pp. 70–75, Macao, China, October 2011. View at Publisher · View at Google Scholar · View at Scopus