Abstract
Fuzzy time series have been successfuIly used in recent years to analyze time series which include uncertainty such as air temperature and stock market data. In the literature, fuzzy time series approaches have been studied in order to reach more accurate forecasts. Delermining the length of interval correctly is one of the crucial points in obtaining accurate forecasts. Some methods have been proposed in the literature to solve this problem. Air quality data also include uncertainty due to its nature, and such time series should be analyzed by using fuzzy time series. In this study, Ankara air quality time series are analyzed by using the fozzy time series method for the first time. Six different fuzzy time series approaches, which employ different techniques for determining the length of interval, are used in the implementation, and the results are compared and discussed.
References
- Aladağ, C. H., Başaran, M. A., Eğrioglu, K, Yolcu, U., Uslu, V. R, 2009. Forecasting in High Order Fuzzy Times Series by Using Neural Networks to Define Fuzzy Relations. Expert Systems with Applications, 36 (3),4228-4231.
- Aladağ, C. R, Yolcu, U., Eğrioglu, K, 2010. A High Order Fuzzy Time Series Forecasting Model Based on Adaptive Expectation and Artificial Neural Networks. Mathematics and Computers in Simulation, 81, 875-882.
- Chen, S. M., 1996. Forecasting Enrollments Based on Fuzzy Time-series. Fuzzy Sets and Systems, 81, 311-319.
- Eğrioglu, E., Aladağ, C. H., Uslu, V. R., Başaran, M. A., Yolcu, U., 2009. A New Hybrid Approach Based on SARIMA and Partial High Order Bivariate Fuzzy Time Series Forecasting Model. Expert Systems with Applications, 36 (4), 7424-7434.
- Eğrioglu, E., Aladağ, C. H., Başaran, M. A., Yolcu, U., Uslu, V. R., 2011. A New Approach Based on the Optimization of the Length of İntervals in Fuzzy Time Series. Journal of İntelligent and Fuzzy Systems, 22,15-19.
- Huarng, K., 2001. Effective Length of Intervals to Improve Forecasting in Fuzzy Time Series. Fuzzy Sets and Systems, 123, 387-394.
- Huarng, K., Yu, H. K., 2006. The Application of Neural Networks to Forecast Fuzzy Time Series. Physica A, 363,481-491.
- Song, Q., Chissom, B. S., 1993a. Fuzzy Time Series and its Models. Fuzzy Sets and Systems, 54, 269-277.
- Song, Q., Chissom, B. S., 1993b. Forecasting Enrollments with Fuzzy Time Series Part I. Fuzzy Sets and Systems, 54, 1- 10.
- Song, Q., Chissom, B. S., 1994. Forecasting Enrollments with Fuzzy Time Series – Part II. Fuzzy Sets and Systems, 62 (1),1-8.
- Zadeh, L. A., 1965. Fuzzy Sets. Inforin and Control, 8, 338-353.
Abstract
Son yıllarda, hava sıcaklığı ve borsa verileri gibi belirsizlik içeren zaman serilerinin çözümlenmesinde bulanık zaman serileri başarıyla kullanılmaktadır. Literatürde, daha güvenilir öngörüler elde etmek amacıyla, bulanık zaman serisi yaklaşımları üzerinde çalışılmaktadır. Güvenilir öngörüler elde edilmesinde anahtar noktalardan biri, aralık uzunluğunun doğru olarak belirlenebilmesidir. Bu problemi çözmek amacıyla literatürde önerilen bazı yaklaşımlar mevcuttur. Hava kalitesi verileri de yapılarından dolayı belirsizlik içeren zaman serileridir ve bu tür zaman serilerinin çözümlenmesinde de bulanık zaman serilerinin kullanılması gerekmektedir. Yapılan bu çalışmada, Ankara hava kalitesi verisi ilk defa bulanık zaman serileri yöntemiyle çözümlenmiştir. Çözümlemede aralık uzunluğu için farklı teknikler kullanan altı farklı bulanık zaman serisi öngörü yöntemi kullanılmış ve elde edilen sonuçlar karşılaştırılarak yorumlanmıştır.
References
- Aladağ, C. H., Başaran, M. A., Eğrioglu, K, Yolcu, U., Uslu, V. R, 2009. Forecasting in High Order Fuzzy Times Series by Using Neural Networks to Define Fuzzy Relations. Expert Systems with Applications, 36 (3),4228-4231.
- Aladağ, C. R, Yolcu, U., Eğrioglu, K, 2010. A High Order Fuzzy Time Series Forecasting Model Based on Adaptive Expectation and Artificial Neural Networks. Mathematics and Computers in Simulation, 81, 875-882.
- Chen, S. M., 1996. Forecasting Enrollments Based on Fuzzy Time-series. Fuzzy Sets and Systems, 81, 311-319.
- Eğrioglu, E., Aladağ, C. H., Uslu, V. R., Başaran, M. A., Yolcu, U., 2009. A New Hybrid Approach Based on SARIMA and Partial High Order Bivariate Fuzzy Time Series Forecasting Model. Expert Systems with Applications, 36 (4), 7424-7434.
- Eğrioglu, E., Aladağ, C. H., Başaran, M. A., Yolcu, U., Uslu, V. R., 2011. A New Approach Based on the Optimization of the Length of İntervals in Fuzzy Time Series. Journal of İntelligent and Fuzzy Systems, 22,15-19.
- Huarng, K., 2001. Effective Length of Intervals to Improve Forecasting in Fuzzy Time Series. Fuzzy Sets and Systems, 123, 387-394.
- Huarng, K., Yu, H. K., 2006. The Application of Neural Networks to Forecast Fuzzy Time Series. Physica A, 363,481-491.
- Song, Q., Chissom, B. S., 1993a. Fuzzy Time Series and its Models. Fuzzy Sets and Systems, 54, 269-277.
- Song, Q., Chissom, B. S., 1993b. Forecasting Enrollments with Fuzzy Time Series Part I. Fuzzy Sets and Systems, 54, 1- 10.
- Song, Q., Chissom, B. S., 1994. Forecasting Enrollments with Fuzzy Time Series – Part II. Fuzzy Sets and Systems, 62 (1),1-8.
- Zadeh, L. A., 1965. Fuzzy Sets. Inforin and Control, 8, 338-353.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Applied Statistics |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | August 15, 2012 |
| Published in Issue | Year 2012 Volume: 9 Issue: 2 |
