You are here

Home » Content

Yüksek Dereceli Bulanık Zaman Serisi Yaklaşımı İle Türkiye Enflasyon Öngörüsü

Forecasting Inflation Rates with High Order Fuzzy Time Series Approach

Journal Name:

  • Dokuz Eylül Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi

Publication Year:

  • 2012

Key Words:

Keywords (Original Language):

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
To obtain inflation forecasts is an important economic issue. The more accurate forecasts we get implies the more precise decisions we make. The central Bank reports inflation rates in certain periods of every year. In this reports the results of inflation expectation survey are presented. In this study we use an approach in which relationship is determined by artificial neural network in high order fuzzy time series model. Time series of consumer price index is estimated by both the artificial neural network based method and some fuzzy approaches which is common in the literature. The results are compared to the results of inflation expectation survey analysis conducted by Central Bank of the Republic of Turkey in the aspect of forecasts accuracy.
Bookmark/Search this post with
Abstract (Original Language): 
Enflasyon öngörülerinin elde edilmesi önemli bir ekonomik problemdir. Öngörülerin daha doğru elde edilmesi daha doğru kararlara neden olacaktır. T.C. Merkez bankası her yılın belirli dönemlerinde enflasyon raporları yayınlamaktadır. Raporlarda enflasyon beklentisi anketi sonuçları yer almaktadır. Bu çalışmada tüketici fiyat endeksi yüksek dereceli bulanık zaman serisi yaklaşımı ile öngörülmüştür. Yüksek dereceli bulanık zaman serisi modelinde ilişkilerin belirlenmesi yapay sinir ağları ile yapılmaktadır. Tüketici fiyat endeksi zaman serisi, ayrıca literatürde yer alan bazı bulanık zaman serisi yaklaşımları ile tahmin edilerek, öngörü doğruluğu açısından T.C. Merkez Bankası enflasyon beklentisi anketi sonuçları ile karşılaştırılmıştır.
FULL TEXT (PDF): 
PDF icon arastrmx_190707_27_pp_85-95.pdf
  • 1
85-95
  • Turkish

JEL Codes:

REFERENCES

References: 

Aladag C. H., Basaran M.A., Egrioglu E., 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, 4228-4231.
Chen, S. M. & Hwang, J. R. (2000), “Temperature prediction using fuzzy
time series”, IEEE ransaction on Systems, Man and Cybernetics, Part B, 30 (2),
263-275.
Chen, S. M. (1996), “Forecasting enrollments based on fuzzy time-series”,
Fuzzy Sets and Systems, 81, 311-319.
Chen, S. M. (2002), “Forecasting enrollments based on high-order fuzzy
time series”, Cybernetics and Systems An International Journal, 33, 1-16.
Huarng, K. & Yu, H.K. (2006), “The application of neural networks to
forecast fuzzy time series”, Physica A, 363, 481-491.
Huarng, K. (2001), “Heuristic models of fuzzy time series for forecasting”
Fuzzy Sets and Systems, 123 (3), 369-386.
Hwang, J. R., Chen, S. M., & Lee, C. H. (1998), “Handling forecasting
problems using fuzzy time series”, Fuzzy Sets and Systems, 100, 217-228.
Song, Q. & Chissom, B.S., (1994), “Forecasting enrollments with fuzzy
time series- Part II”, Fuzzy Sets and Systems, 62, 1-8.
Song, Q. and Chissom, B.S. (1993a), “Fuzzy time series and its models”,
Fuzzy Sets and Systems, 54, 269-277.
Song, Q. and Chissom, B.S. (1993b), “Forecasting enrollments with fuzzy
time series- Part I”, Fuzzy Sets and Systems, 54, 1-10.
Sullivan, J., & Woodall, W. H. (1994), “A comparison of fuzzy forecasting
and Markov modeling”, Fuzzy Sets and Systems, 64, 279-293.
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.

Thank you for copying data from http://www.arastirmax.com