Öz
Grey theory is an effective theory that deals with systems which have imperfect data or weak information. Effective and very accurate estimates can be created for the future by utilizing a small number of data in this theory. The grey modeling method is a sub-branch of the grey systems theory and the modeling process is done by using the related differential equations. The least squares approach plays an important role on accuracy of the results. Using the GM(1,1) modeling method, which is the basis of grey prediction models with its accuracy and usefulness, the tax income to be obtained in the following years are estimated. These estimates are very useful for economic policies, especially for local governments.
Anahtar Kelimeler
Grey Estimation Model Grey System Theory Forecasting Accuracy Least Squares Method
Kaynakça
- [1] J.L.Deng, Control problem of grey system, Syst. Control Lett. 5 (1982) 288-294.
- [2] Yong-Huang Lin, Pin-Chan Lee, Ta-Peng Chang, Adaptive and high-precision grey forecasting model, Expert Syst. Appl. 36 (2009) 9658-9662.
- [3] S.F. Liu, Y.G. Dang, Z.G. Fang, Grey System Theory and its Application, third ed., Science Press, Beijing, 2004 (Chapter 5).
- [4] W. Zhou, J.-M. He, Generalized gm (1,1) model and its application in forecasting of fuel production, Applied Mathematical Modelling 37 (9) (2013) 6234-6243
- [5] S. Javed, S. Liu, Predicting the research output/growth of selected countries: application of even gm(1,1) and ndgm models, Scientometrics. 115 (1) (2018) 395-413.
- [6] S.-L. Ou, Forecasting agricultural output with an improved grey forecasting model based on the genetic algorithml, Comput. Electron. Agric. 85 (85) 80 (2012) 33-39.
- [7] B. Zeng, Y. Tan, H. Xu, J. Quan, L. Wang, X. Zhou, Forecasting the electricity consumption of commercial sector in Hong Kong using a novel grey dynamic prediction model, J. Grey Syst. 30 (1) (2018) 157-172.
- [8] S. Ene, N. Ozturk, Grey modelling based forecasting system for return How of end-of-life vehicles, Technological Forecasting and Social Change 117 (2018) 155-166.
- [9] X. Ma, Z. Liu, Application of a novel time-delayed polynomial grey model to predict the natural gas consumption in china, Journal of Computational and Applied Mathematics. 324 (2017) 17-24.
- [10] J.Cui, S.F.Liu, B.Zeng, N.M. Xie, A novel grey forecasting model and its optimization, Applied Mathematical Modelling 37 (2013) 4399–4406
- [11] X. Ma, Z. Liu, Y. Wang, Application of a novel nonlinear multivariate grey bernoulli model to predict the tourist income of china, J. Comput. Appl. Math. 347 (2019) 84-94.
- [12] Z. X. Wang, Q. Li, L. L. Pei, A seasonal gm(1,1) model for forecasting the electricity consumption of the primary economic sectors, Energy 154 (2018) 522-534.
- [13] D. Akay, M. Atak, Grey prediction with rolling mechanism for electricity demand forecasting of turkey, Energy 32 (9) (2007) 1670-1675.
- [14] http://www.tuik.gov.tr/UstMenu.do?metod=temelist
Öz
Gri teori, zayıf bilgi veya bilgilerin eksik olduğu sistemlerle
ilgilenen etkili bir teoridir. Bu teoride az sayıda veriden faydalanarak
gelecek zamanlar için etkin ve oldukça yaklaşık tahminler
oluşturulabilmektedir. Gri modelleme yöntemi, gri sistemler teorisinin bir alt
dalı olup, modelleme işlemi ilgili fark denklemleri ve diferansiyel denklemler kullanılarak
yapılır. En küçük kareler yaklaşımı sonuçların uygunluğu üzerinde önemli bir
rol oynamaktadır. Kesinliği ve kullanışlılığı ile gri tahmin modellerinin
temelini oluşturan GM(1,1) modelleme yöntemi kullanılarak, Aksaray ilinden
ileriki yıllarda elde edilecek vergi gelirlerinin tahmini yapılmıştır. Bu
tahminler başta yerel yönetimler olmak üzere ekonomik politikalar için oldukça
faydalıdır.
Anahtar Kelimeler
Gri Tahmin Modeli Gri Sistem Teorisi Tahmin Doğruluğu En Küçük Kareler Yöntemi
Kaynakça
- [1] J.L.Deng, Control problem of grey system, Syst. Control Lett. 5 (1982) 288-294.
- [2] Yong-Huang Lin, Pin-Chan Lee, Ta-Peng Chang, Adaptive and high-precision grey forecasting model, Expert Syst. Appl. 36 (2009) 9658-9662.
- [3] S.F. Liu, Y.G. Dang, Z.G. Fang, Grey System Theory and its Application, third ed., Science Press, Beijing, 2004 (Chapter 5).
- [4] W. Zhou, J.-M. He, Generalized gm (1,1) model and its application in forecasting of fuel production, Applied Mathematical Modelling 37 (9) (2013) 6234-6243
- [5] S. Javed, S. Liu, Predicting the research output/growth of selected countries: application of even gm(1,1) and ndgm models, Scientometrics. 115 (1) (2018) 395-413.
- [6] S.-L. Ou, Forecasting agricultural output with an improved grey forecasting model based on the genetic algorithml, Comput. Electron. Agric. 85 (85) 80 (2012) 33-39.
- [7] B. Zeng, Y. Tan, H. Xu, J. Quan, L. Wang, X. Zhou, Forecasting the electricity consumption of commercial sector in Hong Kong using a novel grey dynamic prediction model, J. Grey Syst. 30 (1) (2018) 157-172.
- [8] S. Ene, N. Ozturk, Grey modelling based forecasting system for return How of end-of-life vehicles, Technological Forecasting and Social Change 117 (2018) 155-166.
- [9] X. Ma, Z. Liu, Application of a novel time-delayed polynomial grey model to predict the natural gas consumption in china, Journal of Computational and Applied Mathematics. 324 (2017) 17-24.
- [10] J.Cui, S.F.Liu, B.Zeng, N.M. Xie, A novel grey forecasting model and its optimization, Applied Mathematical Modelling 37 (2013) 4399–4406
- [11] X. Ma, Z. Liu, Y. Wang, Application of a novel nonlinear multivariate grey bernoulli model to predict the tourist income of china, J. Comput. Appl. Math. 347 (2019) 84-94.
- [12] Z. X. Wang, Q. Li, L. L. Pei, A seasonal gm(1,1) model for forecasting the electricity consumption of the primary economic sectors, Energy 154 (2018) 522-534.
- [13] D. Akay, M. Atak, Grey prediction with rolling mechanism for electricity demand forecasting of turkey, Energy 32 (9) (2007) 1670-1675.
- [14] http://www.tuik.gov.tr/UstMenu.do?metod=temelist
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Bölüm | Araştırma Makalesi |
| Yazarlar | |
| Yayımlanma Tarihi | 30 Aralık 2019 |
| Gönderilme Tarihi | 6 Nisan 2019 |
| Kabul Tarihi | 4 Kasım 2019 |
| Yayımlandığı Sayı | Yıl 2019 |