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ARIMA based Interval Type-2 Fuzzy Model for Forecasting

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International Journal of Computer Applications
© 2011 by IJCA Journal
Number 3 - Article 3
Year of Publication: 2011
Authors:
Saima H.
J. Jaafar
S. Belhaouari
T.A. Jillani
10.5120/3369-4652

Saima H., J Jaafar, S Belhaouari and T A Jillani. Article: ARIMA based Interval Type-2 Fuzzy Model for Forecasting. International Journal of Computer Applications 28(3):17-21, August 2011. Full text available. BibTeX

@article{key:article,
	author = {Saima H. and J. Jaafar and S. Belhaouari and T.A. Jillani},
	title = {Article: ARIMA based Interval Type-2 Fuzzy Model for Forecasting},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {28},
	number = {3},
	pages = {17-21},
	month = {August},
	note = {Full text available}
}

Abstract

To solve the chaotic and uncertain problems, researchers are focusing on the extensions of classical fuzzy model. At present Interval Type-2 Fuzzy logic Systems (IT2-FLS) are extensively used after the thriving exploitation of Type-2 FLS. Fuzzy time series models have been used for forecasting stock and FOREX indexes, enrollments, temperature, disease diagnosing and weather. In this paper a hybrid fuzzy time series model is proposed that will develop an Interval type 2 fuzzy model based on ARIMA. The proposed model will use ARIMA to select appropriate coefficients from the observed dataset. IT2-FLS is utilized here for handling the uncertainty in the time series data so that it may yield a more accurate forecasting result.

Reference

  • R. John, S. Coupland. “Type-2 fuzzy logic : A historical view,” IEEE Computational Intelligence Magazine , February 2007, vol. 2, pp. 57-62.
  • S.M. Chen, "Forecasting enrollments based on fuzzy time series,” Fuzzy Sets and Systems 1996, vol. 81, pp 311-319.
  • F.M. Tseng, G.-H. Tzeng, H.-C. Yu, BJ.C. Yuan," Fuzzy ARIMA model for forecasting the foreign exchange market,” Fuzzy Sets and Systems, 2001, vol. 118, pp 9-19.
  • Q. Song, B.S. Chissom, "Forecasting enrollments with fuzzy time series-part 1," Fuzzy Sets and Systems, 1993, vol. 54, pp. l-9.
  • Q. Song, B.S. Chissom, "Forecasting enrollments with fuzzy time series-part 2,” Fuzzy Sets and Systems 1994, vol. 62, pp. 1-8.
  • K. Yu, “Weighted fuzzy time series models for TAlEX forecasting,” Physical A, 2005, vol. 349, pp. 609-624.
  • K. Huamg, Hui-Kuang Yu, “A Type 2 fuzzy time series model for stock index forecasting,” Physical A 2005, vol. 353, pp. 445-462.
  • S.-M. Chen, J.R. Hwang, “Temperature prediction using fuzzy time series," IEEE Trans. Syst. Man, Cybem. 2000, Part B vol. 30 (2), pp. 263-275.
  • D. Delen, G. Waller, A. Kadam, “Predicting Breast Cancer Survivability: A Comparison of Three Data Mining Methods,” Artificial Intelligence in Medicine, 2005, vol. 34, pp. 113-127.
  • M. Tektaş, “Weather forecasting using ANFIS and ARIMA models. A case study for Istanbul,” Environmental Research, Engineering and Management, 2010, vol. 51, pp. 5-10.
  • N. Merh, V.P. Saxena, K. R. Pardasani, “A comparison between Hybrid Approaches of ANN and ARIMA for Indian stock trend forecasting,” Business Intelligence Journal 2010, vol. 3, No 2, pp. 23-43.
  • E. Cadenas, W. Rivera, “Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA_ANN model,” Renewable Energy 2010, vol. 35, pp 2732-2738.
  • D. Wu, “A brief Tutorial on Interval type-2 fuzzy sets and systems,” in Fuzzy Sets and Systems.
  • J.M. Mendal, R.I. John and F. Liu, “Interval type 2 fuzzy logic systems made simple,” IEEE Trans. On Fuzzy Systems, Dec. 2006, vol. 14, pp. 808-821.
  • N.N. Karnik and J.M. Mendel, “An Introduction to Type-2 Fuzzy Logic Systems,” Tech. Rep., University of Southern California, 1998.
  • R.I. John, “Type 2 Fuzzy Sets: An Appraisal of Theory and Applications,” International Journal of Uncertainty, Fuzziness and Knowledge Based Systems 1998, vol. 6, no. 6, pp. 563–576.
  • G.P. Box, G.M. Jenkins, Time Series Analysis: Forecasting and Control, Holden-day Inc., San Francisco, CA, 1976.
  • F.M. Tseng, G.H. Tzeng, H.C. Yu, B.J.C. Yuan, “Fuzzy ARIMA model for forecasting the foreign exchange market,” Fuzzy Sets and Systems 2000, vol. 118, pp. 9-19.
  • N.N. Karnik and J.M Mendel, “Applications of type-2 fuzzy logic systems to forecasting of time-series,” Information Sciences, 1999, vol. 120, pp. 89-111.
  • Q. Song and B.S. Chissom, “New models for forecasting enrollments: fuzzy time series and neural network approaches, ” ERIC, 1993 p. 27, http://www.eric.ed.gov
  • K. Huarng (2002), “Heuristic models of fuzzy time series for forecasting,” Fuzzy Sets and Systems, vol. 123, no. 3, pp.369-386.
  • T.A. Jilani and S. Burney, “Multivariate stochastic fuzzy forecasting models,” Expert Systems with Applications, vol.35, 2008, pp. 691–700.
  • T.A. Jilani and S. Burney, “A refined fuzzy time series model for stock market forecasting”, Physica-A- Statistical mechanics and its applications, 387, 2008, pp. 2857-2862.
  • Mamdani, E. H. and S. Assilian, “An experiment in linguistic synthesis with a fuzzy logic controller,” Int. J. Man-machine Studies, Vol. 7, 1–13, 1975.
  • Takagi, T. and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE Trans. Systems, Man, and Cybernetics, Vol. 15, 116–132, 1985.
  • H. Wu, J.M. Mendel, “Uncertainty bounds and their use in the design of interval type-2 fuzzy logic systems,” IEEE Transactions on Fuzzy Systems October 2002, pp. 622-639.
  • N.N. Karnik, J.M. Mendel, “Centroid of a type-2 fuzzy set,” Information Sciences 2001, vol.132, pp. 195-220.