COMPARISON OF THE PERFORMANCE OF FUZZY TIME SERIES METHODS BASED ON CLUSTERING IN THE ECONOMETRIC TIME SERIES ESTIMATION
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.
Keywords: Fuzzy Clustering, Fuzzy Time Series, Time Series Analysis, Forecast
JEL Codes: C01,C22,C53
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.
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.
Copyright (c) 2019 Rating Academy
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
When the article is accepted for publication in the Journal of Life Economics, authors transfer all copyright in the article to the Rating Academy Ar-Ge Yazılım Yayıncılık Eğitim Danışmanlık ve Organizasyon Ticaret Ltd. Şti.The authors reserve all proprietary right other than copyright, such as patent rights.
Everyone who is listed as an author in this article should have made a substantial, direct, intellectual contribution to the work and should take public responsibility for it.
This paper contains works that have not previously published or not under consideration for publication in other journals.