Abstract
This paper proposes a new fuzzy time-series model for promoting the stock price forecasting, which provides two refined approaches, a frequency-weighted method, and the concept of Fibonacci sequence in forecasting processes. In empirical analysis, two different types of financial datasets, TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) stock index and HSI (Hong Kong Heng Seng Index) stock index are used as model verification. By comparing the forecasting results with those derived from Chen’s, Yu’s, and Hurang’s models, the authors conclude that the research goal has been reached.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Song, Q., Chissom, B.S.: Forecasting enrollments with fuzzy time-series - Part I. Fuzzy Sets and Systems 54, 1–10 (1993)
Song, Q., Chissom, B.S.: Forecasting enrollments with fuzzy time-series - Part II. Fuzzy Sets and Systems 62, 1–8 (1994)
Chen, S.M.: Forecasting enrollments based on fuzzy time-series. Fuzzy Sets and Systems 81, 311–319 (1996)
Chen, S.M.: Forecasting Enrollments Based on High-Order Fuzzy Time Series. Cybernetics and Systems 33, 1–16 (2002)
Chen, S.M., Hsu, C.C.: A New Method to Forecast Enrollments Using Fuzzy Time Series. Applied Science and Eng. 2, 234–244 (2004)
Chen, S.M., Chung, N.Y.: Forecasting Enrollments Using High-Order Fuzzy Time Series and Genetic Algorithms. International Journal of Intelligent Systems 21, 485–501 (2006)
Dourra, H., Pepe, S.: Investment using technical analysis and fuzzy logic. Fuzzy Sets and Systems 127, 221–240 (2002)
Faff, R.W., Brooks, R.D., Ho, Y.K.: New evidence on the impact of financial leverage on beta risk: A time-series approach. North American Journal of Economics and Finance 13, 1–20 (2002)
Wang, Y.F.: Predicting stock price using fuzzy grey prediction system. Experts Systems with Applications 22, 33–39 (2002)
Yu, H.K.: Weighted fuzzy time-series models for TAIEX forecasting. Physica A 349, 609–624 (2004)
Huarng, K.H., Yu, H.K.: A Type 2 fuzzy time-series model for stock index forecasting. Physica A 353, 445–462 (2005)
Leon Lee, C.H., Liu, Alan, Chen, W.S.: Pattern Discovery of Fuzzy Time Series for Financial Prediction. IEEE Transactions on Knowledge and Data Engineering 18, 613–625 (2006)
Fischer, R.: The New Fibonacci Trader: Tools and Strategies for Trading Success. John Wiley & Sons, New York (2001)
Collins, C.J., Frost, A.J., Robert Jr., R.: Prechter: Elliott Wave Principle: Key to Market Behavior. John Wiley & Sons, New York (2003)
Jordan, K.: An Introduction to the Elliott Wave Principle. Alchemist 40, 12–14 (2004)
Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning I. Information Science 8, 199–249 (1975)
Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning II. Information Science 8, 301–357 (1975)
Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning III. Information Science 9, 43–80 (1976)
Huarng, K.H.: Effective lengths of intervals to improve forecasting in fuzzy time-series. Fuzzy Sets and Systems 123, 387–394 (2001)
Huarng, K.H.: Heuristic models of fuzzy time-series for forecasting. Fuzzy Sets and Systems 123, 137–154 (2001)
Miller, G.A.: The magical number seven, plus or minus two: some limits on our capacity of processing information. The Psychological Review 63, 81–97 (1956)
Yu, H.K.: A refined fuzzy time-series model for forecasting. Physica A 346, 657–681 (2005)
Huarng, K.H., Yu, T.H.-K.: The application of neural networks to forecast fuzzy time series. Physica A 336, 481–491 (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Teoh, H.J., Chen, TL., Cheng, CH. (2007). Frequency-Weighted Fuzzy Time-Series Based on Fibonacci Sequence for TAIEX Forecasting. In: Washio, T., et al. Emerging Technologies in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77018-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-540-77018-3_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77016-9
Online ISBN: 978-3-540-77018-3
eBook Packages: Computer ScienceComputer Science (R0)