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HIGH ORDER FUZZY TIME SERIES MODEL AND ITS APLICATION TO IMKB

Year 2010, Volume: 11 Issue: 2, 95 - 101, 27.12.2010

Abstract

The observations of some real time series such as temperature and stock market can take different
values in a day. Instead of representing the observations of these time series by real numbers, employing
linguistic values or fuzzy sets can be more appropriate. In recent years, many approaches have
been introduced to analyze time series consisting of observations which are fuzzy sets and such time
series are called fuzzy time series. In this study, a novel approach is proposed to analyze high order
fuzzy time series model. The proposed method is applied to IMKB data and the obtained results are
discussed. IMKB data is also analyzed by using some other fuzzy time series methods available in the
literature and obtained results are compared to results obtained from the proposed method. As a result
of the comparison, it is seen that the proposed method produce accurate forecasts.

References

  • Aladag, C.H., Basaran, M.A., Egrioglu, E., Yolcu, U. ve Uslu, V.R. (2009). Forecast- ing in high order fuzzy times series by us- ing neural networks to define fuzzy rela- tions. Expert Systems with Applications 36 (3), 4228–4231.
  • Chen, S.M. (1996). Forecasting enrollments based on fuzzy time-series. Fuzzy Sets and Systems 81, 311-319.
  • Chen, S.M. ve Hwang, J.R. (2000). Temperature prediction using fuzzy time series. IEEE Transaction on Systems. Man and Cyber- netics Part B 30 (2), 263-275.
  • Chen, S.M. (2002). Forecasting Enrollments based on high-order fuzzy time series. Cybernetics and Systems an International Journal 33, 1-16.
  • Huarng, K. (2001). Heuristic models of fuzzy time series for forecasting. Fuzzy Sets and Systems 123 (3), 369-386.
  • Huarng, K. ve Yu, H.K. (2006). The application of neural networks to forecast fuzzy time series. Physica A 363, 481-491.
  • Hwang, J.R., Chen, S.M. ve Lee, C.H. (1998). Handling forecasting problems using fuzzy time series. Fuzzy Sets and Systems 100, 217-228.
  • Song, Q. ve Chissom, B.S. (1993a). Fuzzy time series and its models. Fuzzy Sets and Sys- tems 54, 269-277.
  • Song, Q. ve Chissom, B.S. (1993b). Forecasting enrollments with fuzzy time series- Part I. Fuzzy Sets and Systems 54, 1-10.
  • Song, Q. ve Chissom, B.S. (1994). Forecasting enrollments with fuzzy time series- Part II. Fuzzy Sets and Systems 62, 1-8.
  • Sullivan, J. ve Woodall, W.H. (1994). A com- parison of fuzzy forecasting and Markov modeling. Fuzzy Sets and Systems 64, 279-293.
  • Yolcu, U., Egrioğlu, E., Uslu, V.R., Basaran, M.A. ve Aladağ, C.H. (2009). A new ap- proach for determining the length of in- tervals for fuzzy time series. Applied Soft Computing 9 (2), 647-651.
  • Yu, H.K. (2005a). A refined fuzzy time series model for forecasting. Physica A 346, 657-681.
  • Yu, H.K. (2005b). Weighted fuzzy time series models for TAIEX forecasting. Physica A 349, 609-624.
  • Zadeh L.A. (1965). Fuzzy Sets. Inform and Control 8, 338-353.

YÜKSEK DERECELİ BULANIK ZAMAN SERİSİ MODELİ VE IMKB UYGULAMASI

Year 2010, Volume: 11 Issue: 2, 95 - 101, 27.12.2010

Abstract

Gerçek hayatta karşılaşılan sıcaklık, borsa gibi bazı zaman serilerinin gözlemleri gün içinde birden çok değer alabilmektedir. Bu tür zaman serilerinin gözlemlerini gerçel sayılarla belirtmek yerine, dilsel değerlerle ya da bulanık kümeler ile belirtmek daha uygun olabilir. Gözlemleri bulanık kümeler olan ve bulanık zaman serisi olarak adlandırılan zaman serilerinin çözümlenmesi için son yıllarda çok sayıda yöntem geliştirilmiştir. Bu çalışmada, yüksek dereceli bulanık zaman serisi modelinin çözümlenmesi için yeni bir yaklaşım önerilmiştir. Önerilen yaklaşım IMKB verilerine uygulanmış ve elde edilen sonuçlar tartışılmıştır. IMKB verileri literatürdeki diğer bulanık zaman serileri modelleriyle de çözülmüş ve sonuçlar önerilen yöntemle karşılaştırılmıştır. Yapılan karşılaştırma sonucu önerilen yöntemin oldukça iyi öngörüler ürettiği görülmüştür.

References

  • Aladag, C.H., Basaran, M.A., Egrioglu, E., Yolcu, U. ve Uslu, V.R. (2009). Forecast- ing in high order fuzzy times series by us- ing neural networks to define fuzzy rela- tions. Expert Systems with Applications 36 (3), 4228–4231.
  • Chen, S.M. (1996). Forecasting enrollments based on fuzzy time-series. Fuzzy Sets and Systems 81, 311-319.
  • Chen, S.M. ve Hwang, J.R. (2000). Temperature prediction using fuzzy time series. IEEE Transaction on Systems. Man and Cyber- netics Part B 30 (2), 263-275.
  • Chen, S.M. (2002). Forecasting Enrollments based on high-order fuzzy time series. Cybernetics and Systems an International Journal 33, 1-16.
  • Huarng, K. (2001). Heuristic models of fuzzy time series for forecasting. Fuzzy Sets and Systems 123 (3), 369-386.
  • Huarng, K. ve Yu, H.K. (2006). The application of neural networks to forecast fuzzy time series. Physica A 363, 481-491.
  • Hwang, J.R., Chen, S.M. ve Lee, C.H. (1998). Handling forecasting problems using fuzzy time series. Fuzzy Sets and Systems 100, 217-228.
  • Song, Q. ve Chissom, B.S. (1993a). Fuzzy time series and its models. Fuzzy Sets and Sys- tems 54, 269-277.
  • Song, Q. ve Chissom, B.S. (1993b). Forecasting enrollments with fuzzy time series- Part I. Fuzzy Sets and Systems 54, 1-10.
  • Song, Q. ve Chissom, B.S. (1994). Forecasting enrollments with fuzzy time series- Part II. Fuzzy Sets and Systems 62, 1-8.
  • Sullivan, J. ve Woodall, W.H. (1994). A com- parison of fuzzy forecasting and Markov modeling. Fuzzy Sets and Systems 64, 279-293.
  • Yolcu, U., Egrioğlu, E., Uslu, V.R., Basaran, M.A. ve Aladağ, C.H. (2009). A new ap- proach for determining the length of in- tervals for fuzzy time series. Applied Soft Computing 9 (2), 647-651.
  • Yu, H.K. (2005a). A refined fuzzy time series model for forecasting. Physica A 346, 657-681.
  • Yu, H.K. (2005b). Weighted fuzzy time series models for TAIEX forecasting. Physica A 349, 609-624.
  • Zadeh L.A. (1965). Fuzzy Sets. Inform and Control 8, 338-353.
There are 15 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Çağdaş Aladağ

Erol Eğrioğlu

Süleyman Günay This is me

Ufuk Yolcu This is me

Publication Date December 27, 2010
Published in Issue Year 2010 Volume: 11 Issue: 2

Cite

APA Aladağ, Ç., Eğrioğlu, E., Günay, S., Yolcu, U. (2010). HIGH ORDER FUZZY TIME SERIES MODEL AND ITS APLICATION TO IMKB. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering, 11(2), 95-101.
AMA Aladağ Ç, Eğrioğlu E, Günay S, Yolcu U. HIGH ORDER FUZZY TIME SERIES MODEL AND ITS APLICATION TO IMKB. AUJST-A. December 2010;11(2):95-101.
Chicago Aladağ, Çağdaş, Erol Eğrioğlu, Süleyman Günay, and Ufuk Yolcu. “HIGH ORDER FUZZY TIME SERIES MODEL AND ITS APLICATION TO IMKB”. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering 11, no. 2 (December 2010): 95-101.
EndNote Aladağ Ç, Eğrioğlu E, Günay S, Yolcu U (December 1, 2010) HIGH ORDER FUZZY TIME SERIES MODEL AND ITS APLICATION TO IMKB. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering 11 2 95–101.
IEEE Ç. Aladağ, E. Eğrioğlu, S. Günay, and U. Yolcu, “HIGH ORDER FUZZY TIME SERIES MODEL AND ITS APLICATION TO IMKB”, AUJST-A, vol. 11, no. 2, pp. 95–101, 2010.
ISNAD Aladağ, Çağdaş et al. “HIGH ORDER FUZZY TIME SERIES MODEL AND ITS APLICATION TO IMKB”. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering 11/2 (December 2010), 95-101.
JAMA Aladağ Ç, Eğrioğlu E, Günay S, Yolcu U. HIGH ORDER FUZZY TIME SERIES MODEL AND ITS APLICATION TO IMKB. AUJST-A. 2010;11:95–101.
MLA Aladağ, Çağdaş et al. “HIGH ORDER FUZZY TIME SERIES MODEL AND ITS APLICATION TO IMKB”. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering, vol. 11, no. 2, 2010, pp. 95-101.
Vancouver Aladağ Ç, Eğrioğlu E, Günay S, Yolcu U. HIGH ORDER FUZZY TIME SERIES MODEL AND ITS APLICATION TO IMKB. AUJST-A. 2010;11(2):95-101.