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Ridge Regresyona Dayalı Tip 1 Bulanık Regresyon Fonksiyonları Yaklaşımı ile Yabancılara Yapılan Aylık Konut Satışı Öngörüsü

Year 2022, Volume: 12 Issue: 2, 571 - 583, 15.12.2022

Minel Demirkan Pişkin Eren Baş

https://doi.org/10.31466/kfbd.1074832

Abstract

Yapay sinir ağları, bulanık çıkarım sistemleri ve bu yöntemlerin birlikte kullanıldığı melez yöntemler öngörü probleminde sıklıkla kullanılmaktadır. Bulanık çıkarım sistemleri öngörü problemlerinde oldukça etkili sonuçlar üretmesine rağmen birçok klasik bulanık çıkarım sisteminin kural tabanına bağlı olması bu yöntemlerin uygulanmasını güçleştirmektedir. Kural tabanına bağlı olmayan ve birçok bulanık çıkarım sisteminden daha basit bir yapıya sahip olan Tip 1 bulanık regresyon fonksiyonları yaklaşımı öngörü probleminde sıklıkla kullanılmaktadır. Tip 1 bulanık regresyon fonksiyonları yaklaşımı, üstün öngörü performansına sahip olmasına rağmen bu yöntemin uygulanma sürecinde yöntemin çoklu bağlantı problemine sahip olduğu bilinmektedir. Bu problemi ortadan kaldırmak amacı ile önerilen ridge regresyona dayalı Tip 1 bulanık regresyon fonksiyonları yaklaşımı, hem Tip 1 bulanık regresyon fonksiyonları yaklaşımının sahip olduğu çoklu bağlantı problemini ortadan kaldırmış hem de Tip 1 bulanık regresyon fonksiyonları yaklaşımından daha iyi öngörü sonuçları üretmiştir. Bu çalışmada yabancılara yapılan konut satışı öngörüsü ilk defa ridge regresyona dayalı Tip 1 bulanık regresyon fonksiyonları yaklaşımı ile gerçekleştirilmiş ve elde edilen analiz sonuçları literatürde önerilen birçok yöntem ile karşılaştırılmıştır. Yapılan analizler sonucunda ridge regresyona dayalı Tip 1 bulanık regresyon fonksiyonları yaklaşımı ile elde edilen öngörü sonuçlarının literatürdeki diğer bazı birçok yöntemden daha iyi sonuçlar ürettiğine varılmıştır.

Keywords

Bulanık çıkarım sistemleri Tip 1 bulanık regresyon fonksiyonları yaklaşımı ridge regresyon Öngörü konut satışı

References

  • Aktürk, E., and Tekman, N. (2016). Konut talebi ve Erzurum kent merkezinde tüketicilerin konut edinme kararlarını etkileyen faktörler. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 30(2), 423-440.
  • Aladag, C.H., Turksen, I.B., Dalar, A.Z., Egrioglu, E., Yolcu, U. (2014). Application of Type 1 fuzzy functions approach for time series forecasting. Turkish J. Syst., 5(1), 1–9.
  • Aladag, C.H., Yolcu, U., Egrioglu, E., Turksen, I.B. (2016). Type-1 fuzzy time series function method based on binary particle swarm optimisation. International Journal of Data Analysis Techniques and Strategies, 8(1), 02-13.
  • Bas, E., Egrioglu, E. (2022). A fuzzy regression functions approach based on Gustafson-Kessel clustering algorithm. Information Sciences, 592, 206-214.
  • Bas, E., Egrioglu, E., Aladag, C. H., and Yolcu, U. (2015). Fuzzy-time-series network used to forecast linear and nonlinear time series. Applied Intelligence, 43(2), 343-355.
  • Bas, E., Egrioglu, E., Yolcu, U., and Grosan, C. (2019). Type 1 fuzzy function approach based on ridge regression for forecasting. Granular Computing, 4(4), 629-637.
  • Baser, F., Demirhan, H. (2017). A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation. Energy 123, 229-240.
  • Bayar, F. (2008). Küreselleşme kavramı ve küreselleşme sürecinde Türkiye. Uluslararası Ekonomik Sorunlar Dergisi, 32, 25-34.
  • Bezdek, J. C., Coray, C., Gunderson, R., and Watson, J. (1981). Detection and characterization of cluster substructure i. linear structure: Fuzzy c-lines. SIAM Journal on Applied Mathematics, 40(2), 339-357.
  • Chakravarty, S., Demirhan, H., Baser, F. (2020). Fuzzy regression functions with a noise cluster and the impact of outliers on mainstream machine learning methods in the regression setting. Applied Soft Computing Journal, 96, art. no. 106535.
  • Chakravarty, S., Demirhan, H., Baser, F. (2022). Modified fuzzy regression functions with a noise cluster against outlier contamination. Expert Systems with Applications, 205, art. no. 117717.
  • Chakravarty, S., Demirhan, H., Baser, F. (2022). Robust wind speed estimation with modified fuzzy regression functions with a noise cluster. Energy Conversion and Management 266, art. no. 115815.
  • Chen, S. M. (1996). Forecasting enrollments based on fuzzy time series. Fuzzy sets and Systems, 81(3), 311-319.
  • Dalar, A.Z. Egrioglu, E. (2018). Bootstrap type-1 fuzzy functions approach for time series forecasting. in: Trends and Perspectives in Linear Statistical Inference, Springer, 69–87.
  • Ecer, F. (2014). Türkiye’deki konut fiyatlarının tahmininde hedonik regresyon yöntemi ile yapay sinir ağlarının karşılaştırılması. In International Conference on Eurasian Economies 1-10.
  • Egrioglu, E., Fildes, R., Bas, E. (2022). Recurrent fuzzy time series functions approaches for forecasting. Granular Computing, 7(1), 163-170.
  • Egrioglu, E., Yolcu, U., Aladag, C. H., and Bas, E. (2015). Recurrent multiplicative neuron model artificial neural network for non-linear time series forecasting. Neural Processing Letters, 41(2), 249-258.
  • Egrioglu, E., Yolcu, U., and Bas, E. (2019). Intuitionistic high-order fuzzy time series forecasting method based on pi-sigma artificial neural networks trained by artificial bee colony. Granular Computing, 4(4), 639-654.
  • Goudarzi, S., Khodabakhshi, M.B., Moradi, M.H. (2016). Interactively recurrent fuzzy functions with multi objective learning and its application to chaotic time series prediction. Journal of Intelligent & Fuzzy Systems, 30(2), 1157-1168.
  • Hoerl, A. E., and Kennard, R. W. (1976). Ridge regression iterative estimation of the biasing parameter. Communications in Statistics-Theory and Methods, 5(1), 77-88.
  • Jang, J. S. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685.
  • Lebe, F., and Akbaş, Y. (2014). Türkiye’nin konut talebinin analizi: 1970-2011. Atatürk Üniversitesi Iktisadi Ve Idari Bilimler Dergisi, 28(1), 57-83.
  • Mamdani, E. H., and Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1-13.
  • Nghiep, N., and Al, C. (2001). Predicting housing value: A comparison of multiple regression analysis and artificial neural networks. Journal of Real Estate Research, 22(3), 313-336.
  • Özaktaş, F. D. (2019). Yabancılara konut satışı ve reel efektif döviz kuru: Türkiye örneği ampirik çalışma. Ekonomik ve Sosyal Araştırmalar Dergisi, 15(1), 131-147.
  • Öztürk, N., and Fitöz, E. (2009). Türkiye’de konut piyasasının belirleyicileri: Ampirik bir uygulama. Uluslararası Yönetim İktisat ve İşletme Dergisi, 5(10), 21-46.
  • Pehlivan, N.Y., Turksen, I.B. (2021). A novel multiplicative fuzzy regression function with a multiplicative fuzzy clustering algorithm. Romanian Journal of Information Science and Technology, 24(1), 79-98.
  • Tak, N. (2018). Meta fuzzy functions: Application of recurrent type-1 fuzzy functions. Applied Soft Computing, 73, 1-13.
  • Tak, N. (2020). Grey wolf optimizer based recurrent fuzzy regression functions for financial datasets. Öneri Dergisi, 15(54), 350-366.
  • Tak, N. (2020). Type-1 possibilistic fuzzy forecasting functions. Journal of Computational and Applied Mathematics, 370, 112653.
  • Tak, N., İnan, D. (2022). Type-1 fuzzy forecasting functions with elastic net regularization. Expert Systems with Applications, 199, 116916.
  • Takagi, T., and Sugeno M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 1, 116-132.
  • Temür, A. S., Akgün, M., and Temür, G. (2019). Predicting housing sales in Turkey using ARIMA, LSTM and hybrid models. Journal of Business Economics and Management, 20(5), 920-938.
  • Turkşen, I. B. (2008). Fuzzy functions with LSE. Applied Soft Computing, 8(3), 1178-1188.
  • Uysal, D., and Yiğit, M. (2016). Türkiye’de konut talebinin belirleyicileri (1970-2015): Ampirik bir çalışma. Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksek Okulu Dergisi, 19(1), 185-209.
  • Yılmaz, H., and Tosun, Ö. (2020). Aylık konut satışlarının modellenmesi ve Antalya örneği. Kafkas Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 11(21), 141-158.
  • Yılmaz, O., Bas, E., and Egrioglu, E. (2021). The training of pi-sigma artificial neural networks with differential evolution algorithm for forecasting. Computational Economics, 1-13.
  • Yılmazel, Ö., Afşar, A., and Yılmazel, S. (2018). Konut fiyat tahmininde yapay sinir ağları yönteminin kullanılması. Uluslararası İktisadi ve İdari İncelemeler Dergisi, (20), 285-300.
  • Yolcu, U., Egrioglu, E., and Aladag, C. H. (2013). A new linear & nonlinear artificial neural network model for time series forecasting. Decision Support Systems, 54(3), 1340-1347.
  • Zainun, N. Y. B., Rahman, I. A., and Eftekhari, M. (2010). Forecasting low-cost housing demand in Johor Bahru, Malaysia using artificial neural networks (ANN). Journal of Mathematics Research, 2(1), 14-19.

Forecasting Monthly Housing Sales to Foreigners with Type 1 Fuzzy Regression Functions Approach Based on Ridge Regression

Year 2022, Volume: 12 Issue: 2, 571 - 583, 15.12.2022

Minel Demirkan Pişkin Eren Baş

https://doi.org/10.31466/kfbd.1074832

Abstract

Artificial neural networks, fuzzy inference systems, and hybrid methods where these methods are used together have been frequently used in forecasting problems. Although fuzzy inference systems produce very effective results in forecasting problems, the fact that many classical fuzzy inference systems depend on the rule base makes it difficult to implement these methods. The type 1 fuzzy regression functions approach, which is not dependent on the rule base and has a simpler structure than many fuzzy inference systems, is frequently used in forecasting problems. Although the Type 1 fuzzy regression functions approach has superior forecasting performance, it is known that the method has a multicollinearity problem in the application process of this method. The type 1 fuzzy regression functions approach based on ridge regression both eliminates the multicollinearity problem of the Type 1 fuzzy regression functions approach and produce better forecasting results than the Type 1 fuzzy regression functions approach. In this study, the forecasting of monthly house sales to foreigners is carried out for the first time with the Type 1 fuzzy regression functions approach based on ridge regression, and the results of the analysis are compared with many methods suggested in the literature. As a result of the analysis, it is concluded that the forecasting results obtained with the Type 1 fuzzy regression functions approach based on ridge regression produce better results than some other methods in the literature.

Keywords

Fuzzy inference systems Type 1 fuzzy regression functions approach Ridge regression Forecasting Housing sales

References

  • Aktürk, E., and Tekman, N. (2016). Konut talebi ve Erzurum kent merkezinde tüketicilerin konut edinme kararlarını etkileyen faktörler. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 30(2), 423-440.
  • Aladag, C.H., Turksen, I.B., Dalar, A.Z., Egrioglu, E., Yolcu, U. (2014). Application of Type 1 fuzzy functions approach for time series forecasting. Turkish J. Syst., 5(1), 1–9.
  • Aladag, C.H., Yolcu, U., Egrioglu, E., Turksen, I.B. (2016). Type-1 fuzzy time series function method based on binary particle swarm optimisation. International Journal of Data Analysis Techniques and Strategies, 8(1), 02-13.
  • Bas, E., Egrioglu, E. (2022). A fuzzy regression functions approach based on Gustafson-Kessel clustering algorithm. Information Sciences, 592, 206-214.
  • Bas, E., Egrioglu, E., Aladag, C. H., and Yolcu, U. (2015). Fuzzy-time-series network used to forecast linear and nonlinear time series. Applied Intelligence, 43(2), 343-355.
  • Bas, E., Egrioglu, E., Yolcu, U., and Grosan, C. (2019). Type 1 fuzzy function approach based on ridge regression for forecasting. Granular Computing, 4(4), 629-637.
  • Baser, F., Demirhan, H. (2017). A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation. Energy 123, 229-240.
  • Bayar, F. (2008). Küreselleşme kavramı ve küreselleşme sürecinde Türkiye. Uluslararası Ekonomik Sorunlar Dergisi, 32, 25-34.
  • Bezdek, J. C., Coray, C., Gunderson, R., and Watson, J. (1981). Detection and characterization of cluster substructure i. linear structure: Fuzzy c-lines. SIAM Journal on Applied Mathematics, 40(2), 339-357.
  • Chakravarty, S., Demirhan, H., Baser, F. (2020). Fuzzy regression functions with a noise cluster and the impact of outliers on mainstream machine learning methods in the regression setting. Applied Soft Computing Journal, 96, art. no. 106535.
  • Chakravarty, S., Demirhan, H., Baser, F. (2022). Modified fuzzy regression functions with a noise cluster against outlier contamination. Expert Systems with Applications, 205, art. no. 117717.
  • Chakravarty, S., Demirhan, H., Baser, F. (2022). Robust wind speed estimation with modified fuzzy regression functions with a noise cluster. Energy Conversion and Management 266, art. no. 115815.
  • Chen, S. M. (1996). Forecasting enrollments based on fuzzy time series. Fuzzy sets and Systems, 81(3), 311-319.
  • Dalar, A.Z. Egrioglu, E. (2018). Bootstrap type-1 fuzzy functions approach for time series forecasting. in: Trends and Perspectives in Linear Statistical Inference, Springer, 69–87.
  • Ecer, F. (2014). Türkiye’deki konut fiyatlarının tahmininde hedonik regresyon yöntemi ile yapay sinir ağlarının karşılaştırılması. In International Conference on Eurasian Economies 1-10.
  • Egrioglu, E., Fildes, R., Bas, E. (2022). Recurrent fuzzy time series functions approaches for forecasting. Granular Computing, 7(1), 163-170.
  • Egrioglu, E., Yolcu, U., Aladag, C. H., and Bas, E. (2015). Recurrent multiplicative neuron model artificial neural network for non-linear time series forecasting. Neural Processing Letters, 41(2), 249-258.
  • Egrioglu, E., Yolcu, U., and Bas, E. (2019). Intuitionistic high-order fuzzy time series forecasting method based on pi-sigma artificial neural networks trained by artificial bee colony. Granular Computing, 4(4), 639-654.
  • Goudarzi, S., Khodabakhshi, M.B., Moradi, M.H. (2016). Interactively recurrent fuzzy functions with multi objective learning and its application to chaotic time series prediction. Journal of Intelligent & Fuzzy Systems, 30(2), 1157-1168.
  • Hoerl, A. E., and Kennard, R. W. (1976). Ridge regression iterative estimation of the biasing parameter. Communications in Statistics-Theory and Methods, 5(1), 77-88.
  • Jang, J. S. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685.
  • Lebe, F., and Akbaş, Y. (2014). Türkiye’nin konut talebinin analizi: 1970-2011. Atatürk Üniversitesi Iktisadi Ve Idari Bilimler Dergisi, 28(1), 57-83.
  • Mamdani, E. H., and Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1-13.
  • Nghiep, N., and Al, C. (2001). Predicting housing value: A comparison of multiple regression analysis and artificial neural networks. Journal of Real Estate Research, 22(3), 313-336.
  • Özaktaş, F. D. (2019). Yabancılara konut satışı ve reel efektif döviz kuru: Türkiye örneği ampirik çalışma. Ekonomik ve Sosyal Araştırmalar Dergisi, 15(1), 131-147.
  • Öztürk, N., and Fitöz, E. (2009). Türkiye’de konut piyasasının belirleyicileri: Ampirik bir uygulama. Uluslararası Yönetim İktisat ve İşletme Dergisi, 5(10), 21-46.
  • Pehlivan, N.Y., Turksen, I.B. (2021). A novel multiplicative fuzzy regression function with a multiplicative fuzzy clustering algorithm. Romanian Journal of Information Science and Technology, 24(1), 79-98.
  • Tak, N. (2018). Meta fuzzy functions: Application of recurrent type-1 fuzzy functions. Applied Soft Computing, 73, 1-13.
  • Tak, N. (2020). Grey wolf optimizer based recurrent fuzzy regression functions for financial datasets. Öneri Dergisi, 15(54), 350-366.
  • Tak, N. (2020). Type-1 possibilistic fuzzy forecasting functions. Journal of Computational and Applied Mathematics, 370, 112653.
  • Tak, N., İnan, D. (2022). Type-1 fuzzy forecasting functions with elastic net regularization. Expert Systems with Applications, 199, 116916.
  • Takagi, T., and Sugeno M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 1, 116-132.
  • Temür, A. S., Akgün, M., and Temür, G. (2019). Predicting housing sales in Turkey using ARIMA, LSTM and hybrid models. Journal of Business Economics and Management, 20(5), 920-938.
  • Turkşen, I. B. (2008). Fuzzy functions with LSE. Applied Soft Computing, 8(3), 1178-1188.
  • Uysal, D., and Yiğit, M. (2016). Türkiye’de konut talebinin belirleyicileri (1970-2015): Ampirik bir çalışma. Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksek Okulu Dergisi, 19(1), 185-209.
  • Yılmaz, H., and Tosun, Ö. (2020). Aylık konut satışlarının modellenmesi ve Antalya örneği. Kafkas Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 11(21), 141-158.
  • Yılmaz, O., Bas, E., and Egrioglu, E. (2021). The training of pi-sigma artificial neural networks with differential evolution algorithm for forecasting. Computational Economics, 1-13.
  • Yılmazel, Ö., Afşar, A., and Yılmazel, S. (2018). Konut fiyat tahmininde yapay sinir ağları yönteminin kullanılması. Uluslararası İktisadi ve İdari İncelemeler Dergisi, (20), 285-300.
  • Yolcu, U., Egrioglu, E., and Aladag, C. H. (2013). A new linear & nonlinear artificial neural network model for time series forecasting. Decision Support Systems, 54(3), 1340-1347.
  • Zainun, N. Y. B., Rahman, I. A., and Eftekhari, M. (2010). Forecasting low-cost housing demand in Johor Bahru, Malaysia using artificial neural networks (ANN). Journal of Mathematics Research, 2(1), 14-19.
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Details

Primary Language English
Journal Section Articles
Authors

Minel Demirkan Pişkin This is me 0000-0002-8166-1331

Eren Baş 0000-0002-0263-8804

Publication Date December 15, 2022
Published in Issue Year 2022 Volume: 12 Issue: 2

Cite

APA Demirkan Pişkin, M., & Baş, E. (2022). Forecasting Monthly Housing Sales to Foreigners with Type 1 Fuzzy Regression Functions Approach Based on Ridge Regression. Karadeniz Fen Bilimleri Dergisi, 12(2), 571-583. https://doi.org/10.31466/kfbd.1074832
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