Öz
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.
Anahtar Kelimeler
Kaynakça
- 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.
Öz
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.
Anahtar Kelimeler
Bulanık zaman serileri Geri beslemeli ağlar Öngörü Yapay sinir ağları
Kaynakça
- 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.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Bölüm | Araştırma Makalesi |
| Yazarlar | |
| Yayımlanma Tarihi | 27 Aralık 2010 |
| Yayımlandığı Sayı | Yıl 2010 Cilt: 11 Sayı: 2 |
