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EKONOMETRİK ZAMAN SERİLERİ TAHMİNİNDE BULANIK ZAMAN SERİLERİ YÖNTEMLERİNİN KARŞILAŞTIRILMASI

Year 2019, Volume: 6 Issue: 3, 307 - 320, 28.07.2019
https://doi.org/10.15637/jlecon.6.020

Abstract

Bulanık Zaman Serileri (BZS) yöntemleri, istatistiksel
yöntemlerin aksine, hiçbir varsayım gerektirmemesi, az sayıda gözlemle
çalışabilmesi, eksik, belirsiz ve dilsel veriyi işleyebilme yeteneğine sahip
olması gibi avantajlarından dolayı zaman serisi analizinde son zamanlarda
sıklıkla kullanılmaktadır. Şu ana kadar çok sayıda BZS yöntemi önerilmiştir. Bu
yöntemlerden bir kısmı bulanıklaştırma adımında bulanık kümeleme
algoritmalarının kullanımına dayanmaktadır.
Ancak
bu yöntemlerin ekonometrik zaman serilerinin tahmininde performanslarının
karşılaştırılmasına dayanan bir çalışma bulunmamaktadır. Bu çalışmada,
bulanıklaştırma adımında sırasıyla Bulanık C-Ortalamalar (BCO),
Gustafson-Kessel (GK) ve Bulanık K-Medoidler (BKM) kümeleme algoritmalarını
kullanan 3 BZS yöntemi 454 ekonometrik zaman serisine uygulanmış ve elde edilen
tahmin sonuçları Ortalama Mutlak Yüzde Hata (OMYH), Hata Kareler Ortalamasının
Karekökü (HKOK), Varyans Hesabı (VF) uyum iyiliği kriterlerine göre
karşılaştırılmıştır. Karşılaştırmalar sonucunda, BKM algoritmasına dayanan BZS
yönteminin tüm zaman serilerinin OMYH kriterine göre %72.25’inde, HKOK
kriterine göre %65.9’unda, VH kriterine göre ise %59.3’ünde en iyi tahmin
sonuçlarını sağladığı görülmüştür. 

References

  • ALADAĞ, C.H., BASARAN, M.A, EĞRİOĞLU, E., YOLCU, U., USLU, V.R., (2009), Forecasting in High Order Fuzzy Time Series by Using Neural Networks to Define Fuzzy Relations, Expert Systems with Applications, 36, 4228-4231.
  • ALADAĞ, H., EĞRİOĞLU, E., GÜNAY, S., YOLCU, U., (2010), Yüksek Dereceli Bulanık Zaman Serisi Modeli ve IMKB Uygulaması, Anadolu Üniversitesi Bilim ve Teknoloji Dergisi, 11(2), 95-101.
  • BEZDEK, J., EHRLICH, R., FULL, W., (1984), FCM: The fuzzy C-means Clustering Algorithm, Computers & Geosciences, 10(2-3), 191-203.
  • BOX, G. E. P., JENKINS, G. M., (1970), Time Series Analysis: Forecasting and Control, San Francisco: Holden-Day.
  • CHEN, S. M., (1996), Forecasting Enrollments Based on Fuzzy Time-Series, Fuzzy Sets and Systems, 81, 311-319.
  • CHENG, C. H., CHENG, G. W., WANG, J. W., (2008), Multi-Attribute Fuzzy Time Series Method Based on Fuzzy Clustering, Expert Systems with Applications, 34, 1235-1242.
  • DAVARI, S., ZARANDI, M. H. F., TURKSEN, I. B., (2009), An Improved Fuzzy Time Series Forecasting Model Based on Particle Swarm Intervalization, The 28th North American Fuzzy Information Processing Society Annual Conferences (NAFIPS), 14-17.
  • EĞRIOGLU, E., ALADAG, C. H., YOLCU, U., (2013), Fuzzy Time Series Method Based on Multiplicative Neruin Model and Membership Values, American Journal of Intelligent Systems, 3(1), 33-39.
  • EĞRIOGLU, E., ALADAG, C. H., YOLCU, U., USLU, V. R., ERILLI, N. A., (2011), Fuzzy Time Series Forecasting Method Based on Gustafson-Kessel Fuzzy Clustering, Expert Systems with Applications, 38, 10355-10357.
  • FURONG, Y., LIMING, Z., DEFU, Z., HAMIDO, F., ZHIGUO, G. (2016), A Novel Forecasting Method Based on Multi-Order Fuzzy Time Series and Technical Analysis, Information Sciences, 367-368, 41-57.
  • GUSTAFSON, D. E., KESSEL, W. C., (1979), Fuzzy Clustering with Fuzzy Covariance Matrix, In Proceedings of the IEEE CDC, 761–766.
  • GÜLER, D. N., AKKUŞ, Ö., (2018), A New Fuzzy Clustering Based on Robust Clustering for Forecasting of Air Pollution, Ecological Informatics, 43:157-164.
  • HSU, L.Y., HORNG, S. J., KAO, T. W., CHEN, Y. H., RUN, R. S., CHEN, R. J., LAI, J. L., KUO, I. H., (2010), Temperature Prediction and TAIFEX Forecasting Based on Fuzzy Relationships and MTPSO Techniques, Expert Systems with Applications, 37, 2756-2770.
  • HUARNG, K., (2001a), Heuristic Models of Fuzzy Time Series for Forecasting, Fuzzy Sets and Systems, 123(3), 369-386.
  • HUARNG, K., (2001b), Effective Lengths of Interval to Improve Forecasting in Fuzzy Time Series, Fuzzy Sets and Systems, 123, 387-394.
  • HWANG, J. R., CHEN, S. M., LEE, C. H., (1998), Handling Forecasting Problems Using Fuzzy Time Series, Fuzzy Sets and Systems, 100, 217-228.
  • INCEOĞLU, F. E., (2010), Bulanık Zaman Serisi Yöntemleri ile IMKB Öngörüsü, Ondokuz Mayıs Üniversitesi Fen Bilimler Enstitüsü, Yüksek Lisans Tezi, Samsun.
  • KAHRAMAN, C., YAVUZ, M., KAYA, I., (2010), Fuzzy and Grey Forecasting Techniques and Their Applications in Production Systems, in Production Engineering and Management under Fuzziness Studies in Fuzziness and Soft Computing, Verlag Berlin Heidelberg, Springer, 1-24.
  • KOÇAK, C., (2011), Bulanık Zaman Serileri Öngörüsü için Yeni Bir Model Sınıfı, Ondokuz Mayıs Üniversitesi Fen Bilimler Enstitüsü, Doktora Tezi, Samsun.
  • KRISHNAPURAM R., JOSHI A., YI L., (1999), A Fuzzy relative of the k-medoids algorithm with application to document and snippet clustering, Proocedings IEEE International Conference on Fuzzy Systems. Seoul, South Korea.
  • KUO, I. H., HORNG, S. J., CHEN, Y. H., RUN, R. S., KAO, T. W., CHEN, R. J., LAI, J. L., LIN, T. L., (2010), Forecasting TAIFEX Based on Fuzzy Time Series And Particle Swarm Optimization, Expert Systems with Applications, 37, 1494-1502.
  • LEE, L. W., WANG, L. H., CHEN, S. M., (2007), Temperature Prediction and TAIFEX Forecasting Based on Fuzzy Logical Relationships and Genetic Algorithms, Expert Systems with Applications, 33(3), 539–550.
  • LI, S. T., CHENG, Y. C., LIN, S. Y., (2008), A FCM-Based Deterministic Forecasting Model for Fuzzy Time Series, Computers and Mathematics with Applications, 56, 3052–3063.
  • LIU, Z., ZHANG, T., (2019), A Second-Order Fuzzy Time Series Model for Stock Price Analysis, Journal of Applied Statistics, doi. https://doi.org/10.1080/02664763.2019.1601163
  • PARK, J. I., LEE, D. J., SONG, C. K., CHUN, M. G., (2010), TAIFEX and KOSPI 200 Forecasting Based on Two Factors High Order Fuzzy Time Series and Particle Swarm Optimization, Expert Systems with Applications, 37, 959-967.
  • SEVÜKTEKİN M., NARGELEÇEKENLER M., (2010), Ekonometrik Zaman Serileri Analizi-EViews Uygulamalı, Ankara, Nobel, 591p
  • SONG, Q., CHISSOM, B. S., (1993a), Fuzzy Time Series and its Models, Fuzzy Sets and Systems, 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.
  • SUN, B., GUO, H., KARIMI, H. R., GE, Y., XIONG, S., (2015), Prediction of Stock Index Futures Prices Based on Fuzzy Sets and Multivariate Fuzzy Time Series, Neurocomputing, 151, Kısım 3, 1528-1536.
  • USLU, V. R., ALADAG, C. H., YOLCU, U., EGRIOGLU, E., (2010), A New Hybrid Approach for Forecasting a Seasonal Fuzzy Time Series, Proceedings of the 1st International Symposium on Computing In Science & Engineering, Izmır -Turkey.
  • UYAR, H., (2015), BIST Verilerinin Çeşitli Bulanık Zaman Serileri Yaklaşımları ile Öngörülerinin Karşılaştırılması, Akdeniz Üniversitesi Sosyal Bilimler Enstitüsü, Yüksek Lisans Tezi, Antalya.
  • WANG, N. Y, CHEN, S. M., (2009), Temperature prediction and TAIFEX Forecasting Based on Automatic Clustering Techniques and Two-Factors High-Order Fuzzy Time Series, Expert Systems with Applications, 36, 2143-2154.
  • YOLCU, U., (2011), Bulanık Zaman Serilerinde Çok Değişkenli Çözümleme, Ondokuz Mayıs Üniversitesi Fen Bilimler Enstitüsü, Doktora Tezi, Samsun.

COMPARISON OF THE PERFORMANCE OF FUZZY TIME SERIES METHODS BASED ON CLUSTERING IN THE ECONOMETRIC TIME SERIES ESTIMATION

Year 2019, Volume: 6 Issue: 3, 307 - 320, 28.07.2019
https://doi.org/10.15637/jlecon.6.020

Abstract

Fuzzy Time Series
(FTS) methods are used frequently in time series analysis due to their
advantages such as having no assumptions, having few observations, being able
to process incomplete, uncertain and linguistic data. The FTS consists of 6
steps, each of which has a significant impact on forecasting performance. A
number of methods have been developed to improve these steps and hence improve
the performance of FTS. Some of these studies are based on the use of fuzzy
clustering algorithms in the blurring step of FTS. However, so far, there is no
study based on comparing the performance of these methods in the estimation of
econometric time series. In this study, 3 FTS methods using the Fuzzy C-Means
(FCM), Gustafson-Kessel (GK) and Fuzzy K-Medoids (FKM) clustering algorithms
were applied to the 454 econometric time series in the blurring step and the
predicted results were compared according to the criterion of conformity 3. As
a result of the comparisons, it was concluded that the performance of the FTS
method based on BKM algorithm is better.

References

  • ALADAĞ, C.H., BASARAN, M.A, EĞRİOĞLU, E., YOLCU, U., USLU, V.R., (2009), Forecasting in High Order Fuzzy Time Series by Using Neural Networks to Define Fuzzy Relations, Expert Systems with Applications, 36, 4228-4231.
  • ALADAĞ, H., EĞRİOĞLU, E., GÜNAY, S., YOLCU, U., (2010), Yüksek Dereceli Bulanık Zaman Serisi Modeli ve IMKB Uygulaması, Anadolu Üniversitesi Bilim ve Teknoloji Dergisi, 11(2), 95-101.
  • BEZDEK, J., EHRLICH, R., FULL, W., (1984), FCM: The fuzzy C-means Clustering Algorithm, Computers & Geosciences, 10(2-3), 191-203.
  • BOX, G. E. P., JENKINS, G. M., (1970), Time Series Analysis: Forecasting and Control, San Francisco: Holden-Day.
  • CHEN, S. M., (1996), Forecasting Enrollments Based on Fuzzy Time-Series, Fuzzy Sets and Systems, 81, 311-319.
  • CHENG, C. H., CHENG, G. W., WANG, J. W., (2008), Multi-Attribute Fuzzy Time Series Method Based on Fuzzy Clustering, Expert Systems with Applications, 34, 1235-1242.
  • DAVARI, S., ZARANDI, M. H. F., TURKSEN, I. B., (2009), An Improved Fuzzy Time Series Forecasting Model Based on Particle Swarm Intervalization, The 28th North American Fuzzy Information Processing Society Annual Conferences (NAFIPS), 14-17.
  • EĞRIOGLU, E., ALADAG, C. H., YOLCU, U., (2013), Fuzzy Time Series Method Based on Multiplicative Neruin Model and Membership Values, American Journal of Intelligent Systems, 3(1), 33-39.
  • EĞRIOGLU, E., ALADAG, C. H., YOLCU, U., USLU, V. R., ERILLI, N. A., (2011), Fuzzy Time Series Forecasting Method Based on Gustafson-Kessel Fuzzy Clustering, Expert Systems with Applications, 38, 10355-10357.
  • FURONG, Y., LIMING, Z., DEFU, Z., HAMIDO, F., ZHIGUO, G. (2016), A Novel Forecasting Method Based on Multi-Order Fuzzy Time Series and Technical Analysis, Information Sciences, 367-368, 41-57.
  • GUSTAFSON, D. E., KESSEL, W. C., (1979), Fuzzy Clustering with Fuzzy Covariance Matrix, In Proceedings of the IEEE CDC, 761–766.
  • GÜLER, D. N., AKKUŞ, Ö., (2018), A New Fuzzy Clustering Based on Robust Clustering for Forecasting of Air Pollution, Ecological Informatics, 43:157-164.
  • HSU, L.Y., HORNG, S. J., KAO, T. W., CHEN, Y. H., RUN, R. S., CHEN, R. J., LAI, J. L., KUO, I. H., (2010), Temperature Prediction and TAIFEX Forecasting Based on Fuzzy Relationships and MTPSO Techniques, Expert Systems with Applications, 37, 2756-2770.
  • HUARNG, K., (2001a), Heuristic Models of Fuzzy Time Series for Forecasting, Fuzzy Sets and Systems, 123(3), 369-386.
  • HUARNG, K., (2001b), Effective Lengths of Interval to Improve Forecasting in Fuzzy Time Series, Fuzzy Sets and Systems, 123, 387-394.
  • HWANG, J. R., CHEN, S. M., LEE, C. H., (1998), Handling Forecasting Problems Using Fuzzy Time Series, Fuzzy Sets and Systems, 100, 217-228.
  • INCEOĞLU, F. E., (2010), Bulanık Zaman Serisi Yöntemleri ile IMKB Öngörüsü, Ondokuz Mayıs Üniversitesi Fen Bilimler Enstitüsü, Yüksek Lisans Tezi, Samsun.
  • KAHRAMAN, C., YAVUZ, M., KAYA, I., (2010), Fuzzy and Grey Forecasting Techniques and Their Applications in Production Systems, in Production Engineering and Management under Fuzziness Studies in Fuzziness and Soft Computing, Verlag Berlin Heidelberg, Springer, 1-24.
  • KOÇAK, C., (2011), Bulanık Zaman Serileri Öngörüsü için Yeni Bir Model Sınıfı, Ondokuz Mayıs Üniversitesi Fen Bilimler Enstitüsü, Doktora Tezi, Samsun.
  • KRISHNAPURAM R., JOSHI A., YI L., (1999), A Fuzzy relative of the k-medoids algorithm with application to document and snippet clustering, Proocedings IEEE International Conference on Fuzzy Systems. Seoul, South Korea.
  • KUO, I. H., HORNG, S. J., CHEN, Y. H., RUN, R. S., KAO, T. W., CHEN, R. J., LAI, J. L., LIN, T. L., (2010), Forecasting TAIFEX Based on Fuzzy Time Series And Particle Swarm Optimization, Expert Systems with Applications, 37, 1494-1502.
  • LEE, L. W., WANG, L. H., CHEN, S. M., (2007), Temperature Prediction and TAIFEX Forecasting Based on Fuzzy Logical Relationships and Genetic Algorithms, Expert Systems with Applications, 33(3), 539–550.
  • LI, S. T., CHENG, Y. C., LIN, S. Y., (2008), A FCM-Based Deterministic Forecasting Model for Fuzzy Time Series, Computers and Mathematics with Applications, 56, 3052–3063.
  • LIU, Z., ZHANG, T., (2019), A Second-Order Fuzzy Time Series Model for Stock Price Analysis, Journal of Applied Statistics, doi. https://doi.org/10.1080/02664763.2019.1601163
  • PARK, J. I., LEE, D. J., SONG, C. K., CHUN, M. G., (2010), TAIFEX and KOSPI 200 Forecasting Based on Two Factors High Order Fuzzy Time Series and Particle Swarm Optimization, Expert Systems with Applications, 37, 959-967.
  • SEVÜKTEKİN M., NARGELEÇEKENLER M., (2010), Ekonometrik Zaman Serileri Analizi-EViews Uygulamalı, Ankara, Nobel, 591p
  • SONG, Q., CHISSOM, B. S., (1993a), Fuzzy Time Series and its Models, Fuzzy Sets and Systems, 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.
  • SUN, B., GUO, H., KARIMI, H. R., GE, Y., XIONG, S., (2015), Prediction of Stock Index Futures Prices Based on Fuzzy Sets and Multivariate Fuzzy Time Series, Neurocomputing, 151, Kısım 3, 1528-1536.
  • USLU, V. R., ALADAG, C. H., YOLCU, U., EGRIOGLU, E., (2010), A New Hybrid Approach for Forecasting a Seasonal Fuzzy Time Series, Proceedings of the 1st International Symposium on Computing In Science & Engineering, Izmır -Turkey.
  • UYAR, H., (2015), BIST Verilerinin Çeşitli Bulanık Zaman Serileri Yaklaşımları ile Öngörülerinin Karşılaştırılması, Akdeniz Üniversitesi Sosyal Bilimler Enstitüsü, Yüksek Lisans Tezi, Antalya.
  • WANG, N. Y, CHEN, S. M., (2009), Temperature prediction and TAIFEX Forecasting Based on Automatic Clustering Techniques and Two-Factors High-Order Fuzzy Time Series, Expert Systems with Applications, 36, 2143-2154.
  • YOLCU, U., (2011), Bulanık Zaman Serilerinde Çok Değişkenli Çözümleme, Ondokuz Mayıs Üniversitesi Fen Bilimler Enstitüsü, Doktora Tezi, Samsun.
There are 33 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Aytaç Pekmezci 0000-0003-4020-0069

Nevin Güler Dincer 0000-0003-0361-1803

Öznur İşçi Güneri 0000-0003-3677-7121

Publication Date July 28, 2019
Published in Issue Year 2019 Volume: 6 Issue: 3

Cite

APA Pekmezci, A., Dincer, N. G., & İşçi Güneri, Ö. (2019). EKONOMETRİK ZAMAN SERİLERİ TAHMİNİNDE BULANIK ZAMAN SERİLERİ YÖNTEMLERİNİN KARŞILAŞTIRILMASI. Journal of Life Economics, 6(3), 307-320. https://doi.org/10.15637/jlecon.6.020