Abstract
Knowledge is usually employed by domain experts to solve domain-specific problems. Huarng was the first to embed knowledge into forecasting fuzzy time series (2001). His model involved simple calculations and offers better prediction results once more supporting information has been supplied. On the other hand, Chen first proposed a high-order fuzzy time series model to overcome the drawback of existing fuzzy first-order forecasting models. Chen’s model involved limited computing and came with higher accuracy than some other models. For this reason, the study is focused on these two types of models. The first model proposed here, which is referred to as a weighted model, aims to overcome the deficiency of the Huarng’s model. Second, we propose another fuzzy time series model, called knowledge based high-order time series model, to deal with forecasting problems. This model aims to overcome the deficiency of the Chen’s model, which depends strongly on highest-order fuzzy time series to eliminate ambiguities at forecasting and requires a vast memory for data storage. Experimental study of enrollment of University Alabama and the forecasting of a future’s index show that the proposed models reflect fluctuations in fuzzy time series and provide forecast results that are more accurate than the ones obtained when using the to two referenced models.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Box, G.E.P., Jenkins, G.M.: Time series analysis: forecasting and control. Prentice-Hall (1976)
Chen, M.-Y., Fan, M.-H., Chen, C.-C.: Forecasting stock price based on fuzzy time-series with equal-frequency partitioning and fast fourier transform algorithm. In: Computing, Communication and Application Conference, pp. 238–243 (2012)
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)
Cheng, C.H., Chen, T.L., Teoh, H.J., Chiang, C.H.: Fuzzy time-series based on adaptive model for TAIEX forecasting. Expert Systems with Applications 34, 1126–1132 (2008)
Huarng, K.: Knowledge models of fuzzy time series for forecasting. Fuzzy Sets and Systems 123, 369–386 (2001)
Huarng, K.H., Yu, K.H.: A type 2 fuzzy time series model for stock index forecasting. Physica A: Statistical Mechanics and its Applications 353, 445–462 (2005)
Huarng, K.H., Yu, K.H.: Ratio-based lengths of intervals to improve fuzzy time series forecasting. IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics 32, 328–340 (2006a)
Huarng, K.H., Yu, K.H.: The application of neural networks to forecast fuzzy time series. Physica A: Statistical Mechanics and its Applications 363, 481–491 (2006b)
Huarng, K.H., Yu, K.H., Hsu, Y.W.: A multivariate knowledge model for fuzzy time-series forecasting. IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics 37, 836–846 (2007)
Hwang, J.R., Chen, S.M., Lee, C.H.: Handling forecasting problems using fuzzy time series. Fuzzy Sets and Systems 100, 217–228 (1998)
Janacek, G., Swift, L.: Time series forecasting, simulation, applications. Ellis Harwood (1993)
Jilani, T.A., Burney, S.M.A., Amjad, U., Siddiqui, T.A.: A particle swarm intelligence based fuzzy time series forecasting model. International Journal of Computer Applications 38, 47–52 (2011)
Leu, Y., Lee, C.P., Jou, Y.Z.: A distance-based fuzzy time series model for exchange rates forecasting. Expert Systems with Applications 36, 8107–8114 (2009)
Song, Q., Chissom, B.S.: Fuzzy time series and its models. Fuzzy Sets and Systems 54, 269–377 (1979)
Song, Q., Chissom, B.S.: Forecasting enrollments with fuzzy time series - part I, Fuzzy Sets and Systems. Fuzzy Sets and Systems 54, 1–9 (1993)
Song, Q., Chissom, B.S.: Forecasting enrollments with fuzzy time series- part II. Fuzzy Sets and Systems 62, 1–8 (1994)
Sullivan, J., Woodall, W.H.: A comparison of fuzzy forecasting and markov modeling. Fuzzy Sets and Systems 64, 279–293 (1994)
Tanuwijaya, K., Chen, S.M.: A new method to forecast enrollments using fuzzy time series and clustering techniques. In: Proceedings of the 2009 International Conference on Machine Learning and Cybernetics, Baoding, Hebei, China (2009a)
Wang, L.X.: A course in fuzzy systems and control. Prentice-Hall, Inc. (1997)
Yu, H.K., Huarng, K.H.: A bivariate fuzzy time series model to forecast the TAIEX. Expert Systems with Applications 34, 2945–2952 (2008)
Yu, T.H.K., Hurang, K.H.: Corrigendum to a bivariate fuzzy time series model to forecast the TAIFEX. Expert Systems with Applications 34, 2945–2952 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Own, CM. (2013). An Application of Enhanced Knowledge Models to Fuzzy Time Series. 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_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-33439-9_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33438-2
Online ISBN: 978-3-642-33439-9
eBook Packages: EngineeringEngineering (R0)