Abstract
The purpose of this chapter is to present an original concept of financial fuzzy time series models based on financial data in the form of Japanese Candlestick Charts. In this approach the Japanese Candlesticks are modeled using Ordered Fuzzy Numbers (OFN) called further Ordered Fuzzy Candlesticks (OFC). The use of ordered fuzzy numbers allows modeling uncertainty associated with financial data. Thanks to well-defined arithmetic of ordered fuzzy numbers, one can construct models of fuzzy time series, such as e.g. an autoregressive process, where all input values are OFC, while the coefficients and output values are arbitrary OFN, in the form of classical equations, without using rule-based systems. Finally, several applications of these models for modeling and forecasting selected financial time series are presented.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Dubois, D., Prade, H.: Operations on fuzzy numbers. Int. J. System Science 9, 576–578 (1978)
Kao, C., Chyu, C.-L.: Least-squares estimates in fuzzy regression analysis. European Journal of Operational Research 148, 426–435 (2003)
Kosiński, W., Piechór, K., Prokopowicz, K., Tyburem, K.: On algorithmic approach to operations on fuzzy numbers. In: Burczyński, T., Cholewa, W. (eds.) Methods of Artificial Intelligence in Mechanics and Mechanical Engineering, pp. 95–98. PACM, Gliwice (2001)
Kosiński, W., Prokopowicz, P., Ślęzak, D.: Drawback of fuzzy arithmetic - New intuitions and propositions. In: Burczyński, T., Cholewa, W., Moczulski, W. (eds.) Proc. Methods of Artificial Intelligence, pp. 231–237. PACM, Gliwice (2002)
Kosiński, W., Prokopowicz, P., Ślęzak, D.: On algebraic operations on fuzzy numbers. In: Klopotek, M., Wierzchoń, S.T., Trojanowski, K. (eds.) Intelligent Information Processing and Web Mining, Proc. Int. Symp. IIS: IIPWM 2003, Zakopane, Poland, pp. 353–362. Physica Verlag, Heidelberg (2003)
Kosiński, W., Prokopowicz, P., Ślęzak, D.: Ordered fuzzy numbers. Bull. Polish Acad. Sci., Ser. Sci. Math. 51(3), 327–338 (2003)
Kosiński, W., Prokopowicz, P.: Algebra of fuzzy numbers. Matematyka Stosowana. Matematyka dla Spoeczestwa 5(46), 37–63 (2004) (in Polish)
Kosiński, W.: On soft computing and modelling. Image Processing Communications 11(1), 71–82 (2006)
Lee, C.L., Liu, A., Chen, W.: Pattern Discovery of Fuzzy Time Series for Financial Prediction. IEEE Trans. on Knowledge and Data Engineering 18(5) (2006)
Łachwa, A.: Fuzzy World of Sets, Numbers, Relations, Fazts, Rules and Decisions. EXIT, Warsaw (2001) (in Polish)
Łęski, J.: Neuro-fuzzy systems. WNT, Warsaw (2008) (in Polish)
Murphy, J.J.: Technical Analysis of the Financial Markets. New York Institute of Finance, New York (1999)
Nison, S.: Japanese Candlestick Charting Techniques. New York Institute of Finance, New York (1991)
Tanaka, H., Uejima, S., Asia, K.: Linear regression analysis with Fuzzy model. IEEE Trans. Systems Man. Cybernet. 12, 903–907 (1982)
Tsay, R.S.: Analysis of Financial Time Series, 2nd edn. John Wiley & Sons, Inc., Hoboken (2005)
Wagenknecht, M.: On the approximate treatment of fuzzy arithmetics by inclusion, linear regression and information content estimation. In: Chojcan, J., Łęski, J. (eds.) Fuzzy Sets and Their Applications, pp. 291–310. Silesian University of Technology Press, Gliwice (2001)
Wagenknecht, M., Hampel, R., Schneider, V.: Computational aspects of fuzzy arithmetic based on Archimedean t-norms. Fuzzy Sets Syst. 123(1), 49–62 (2001)
Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning, Part I. Inf. Sci. 8(3), 199–249 (1975)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Marszałek, A., Burczyński, T. (2013). Financial Fuzzy Time Series Models Based on Ordered Fuzzy Numbers. In: Pedrycz, W., Chen, SM. (eds) Time Series Analysis, Modeling and Applications. Intelligent Systems Reference Library, vol 47. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33439-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-33439-9_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33438-2
Online ISBN: 978-3-642-33439-9
eBook Packages: EngineeringEngineering (R0)