Top > JACIII > Rough Sets Based Prediction Model of Tick-Wise Price Fluctuati ...

single-jc.php

JACIII Vol.15 No.4 pp. 449-453
doi: 10.20965/jaciii.2011.p0449
(2011)

Paper:

Views over last 60 days: 578

Rough Sets Based Prediction Model of Tick-Wise Price Fluctuations

Yoshiyuki Matsumoto* and Junzo Watada**

*Department of Economics, Shimonoseki City University, 2-1-1 Daigaku-cho, Shimonoseki, Yamaguchi 751-8510, Japan

**Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu-shi, Fukuoka 808-0135, Japan

Received:
January 7, 2011
Accepted:
March 1, 2011
Published:
June 20, 2011
Creative Commons License
Keywords:
rough sets, tick-wise price, time-series data
Abstract
Rough sets theory was proposed by Z. Pawlak in 1982. This theory enables us to mine knowledge granules through a decision rule from a database, a web base, a set and so on. We can apply the decision rule to reason, estimate, evaluate, or forecast unknown objects. In this paper, the rough set model is used to analyze of time series data of tick-wise price fluctuation, where knowledge granules are mined from the data set of tick-wise price fluctuations.
Cite this article as:
Y. Matsumoto and J. Watada, “Rough Sets Based Prediction Model of Tick-Wise Price Fluctuations,” J. Adv. Comput. Intell. Intell. Inform., Vol.15 No.4, pp. 449-453, 2011.
Data files:
References
  1. [1] Y. Matsumoto and J. Watada, “Improvement of Chaotic Short-term Forecasting on Fuzzy Reasoning and Tuning on Genetic Algorithm,” Japan Society J. of fuzzy Theory and Intelligent Informatics, Vol.16, No.1, pp. 44-52, 2004.
  2. [2] Z. Pawlak, “Rough Sets,” Int. J. of Computer and Information Science, Vol.11, No.5, pp. 341-356, 1982.
  3. [3] Z. Pawlak, “Rough Sets – Theoretical Aspects of Reasoning about Data,” Kluwer Academic Publishers, 1991.
  4. [4] S. Tan, X. Cheng, and H. Xu, “An efficient global optimization approach for rough set based dimensionality reduction,” Int. J. of Innovative Computing, Information and Control, Vol.3, No.3, pp. 725-736, 2007.
  5. [5] C. Goh and R. Law, “Incorporation the rough sets theory,” Chemometrics and Intelligent Laboratory Systems, Vol.47, No.1, pp. 1-16, 2003.
  6. [6] M. J. Beynon and M. J. Peel, “Variable precision rough set theory and data discrimination: an application to corporate failure prediction,” Omega, Vol.29, No.6, pp. 561-576, 2001.
  7. [7] R. Azibi and D. Vanderpooten, “Construction of rule-based assignment models,” European J. of Operational Research, Vol.138, No.2, pp. 274-293, 2002.
  8. [8] R. Li and Z. O. Wang, “Mining classification rules using rough set and neural networks,” European J. of Operational Research, Vol.157, No.2, pp. 439-448, 2004.
  9. [9] M. Quafafou, “α-RST: a generalization of rough set theory,” Information Sciences, Vol.124, No.4, pp. 301-316, 2000.
  10. [10] S. Greco, B. Matarazzo, and R. Slowinski, “Rough sets theory for multi-criteria decision analysis,” European J. of Operational Research, Vol.129, No.1, pp. 1-47, 2001.
  11. [11] Y. Jhieh, G. Tzeng, and F. Wang, “Rough set Theory in Analyzing the Attributes of Combination Values for insurance market,” Expert System with Applications, Vol.32, No.1, 2007.
  12. [12] T. Harada and R. Tanaka, “Analysis of Specifications for Web Screen-Design Using Rough Sets,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.10, No.5, 2006.
  13. [13] D. Kim and S. Y. Bang, “IRIS Data Classification Using Tolerant Rough Sets,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.4, No.5, 2000.
  14. [14] B. Walczak and D. L. Massart, “Rough set theory,” Chemometrics and Intelligent Laboratory, Vol.47, No.1, pp. 1-16, 1999.
  15. [15] B. Predki, R. Slowinski, R. Stefanowski, and Sz. Wilk, “ROSESoftware Implementation of the rough set theory,” In: L. Polkowski and A. Skowron (Eds.), Rough set and Current Trends in computing, Lecture Notes in Artificial Intelligence, Spronger-Verlag, Berlin, 1998.
  16. [16] B. Predki and Sz. Wilk, “Rough set Based Data Exploration Using ROSE System,” In: Z. W. Ras and A. Skowron (Eds.), Foundations of Intelligent Systems, Lecture Notes in Artificial Intelligence, Poland, Warsaw: Springer-Verlag, pp. 172-180, 1999.
  17. [17] Z. Pawlak, “Rough classification,” Int. J. Human-Computer Studies, Vol.51, No.15, pp. 369-383, 1999.
  18. [18] K. Gronhaug and M. C. Gilly, “A transaction cost approach to consumer dissatisfaction and complaint action,” J. of economic psychology, Vol.12, No.1, pp. 165-183, 1991.
  19. [19] C. Lin, J. Watada, and G. Tzeng, “Rough sets theory and its application to management engineering,” Proc., Int. Symposium of Management Engineering, Kitakyushu, Japan, pp. 170-176, 2008.

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 Apr. 19, 2024