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## A NEW APPROACH BASED ON OPTIMIZATION OF RATIO FOR SEASONAL FUZZY TIME SERIES | ||

Iranian Journal of Fuzzy Systems | ||

مقاله 3، دوره 13، شماره 2، تیر 2016، صفحه 19-36
اصل مقاله (533.53 K)
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نوع مقاله: Research Paper | ||

شناسه دیجیتال (DOI): 10.22111/ijfs.2016.2357 | ||

نویسنده | ||

Ufuk Yolcu ^{}
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^{}Department of Statistics, Faculty of Science, Ankara University, 06100 Ankara, Turkey | ||

چکیده | ||

In recent years, many studies have been done on forecasting fuzzy time series. First-order fuzzy time series forecasting methods with first-order lagged variables and high-order fuzzy time series forecasting methods with consecutive lagged variables constitute the considerable part of these studies. However, these methods are not effective in forecasting fuzzy time series which contain seasonal structures. In this respect, it would be more appropriate to use methods that consider the seasonal relations in seasonal fuzzy time series forecasting. Although seasonal fuzzy time series forecasting methods exist in literature, these methods use equal interval lengths in partition of the universe of discourse. This situation incapacitates the performance of the method in forecasting time series including seasonality and trend. In this study, a new fuzzy time series forecasting method in which intervals constituting partition of the universe of discourse increase in time at a rate that obtained based on optimization was proposed. The proposed method was applied to two real time series and obtained results were compared with other methods and the superior performance of the proposed method was proved. | ||

کلیدواژهها | ||

Seasonal fuzzy time series؛ Optimization؛ Forecasting؛ Feed forward neural networks | ||

مراجع | ||

[1] C. H. Aladag, M. A. Basaran, E. Egrioglu, U. Yolcu and V. R. Uslu, Forecasting in high order fuzzy time series by using neural networks to dene fuzzy relations, Expert Systems with Applications, 36 (2009), 4228-4231. [2] C. H. Aladag, U. Yolcu and E. Egrioglu, A high order fuzzy time series forecasting model based on adaptive expectation and articial neural networks, Mathematics and Computers in Simulation, 81 (2010), 875-882. [3] C. H. Aladag, E. Egrioglu, U. Yolcu and V. R. Uslu, A high order seasonal fuzzy time series model and application to international tourism demand of Turkey, Journal of Intelligent and Fuzzy Systems, 26 (2014), 295-302. [4] C. H. Aladag, U. Yolcu, E. Egrioglu and E. Bas, Fuzzy lagged variable selection in fuzzy time series with genetic algorithms, Applied Soft Computing, 22 (2014), 465-473. [5] F. Alpaslan, O. Cagcag, C. H. Aladag, U. Yolcu and E. Egrioglu, A novel seasonal fuzzy time series method, Hacettepe Journal of Mathematics and Statistics, 41 (2012), 375-385. [6] E. Bas, V. R. Uslu, U. Yolcu and E. Egrioglu, A modied genetic algorithm for forecasting fuzzy time series, Applied Intelligence, 41 (2014), 453-463. [7] G. E. P. Box and G. M. Jenkins, Time series analysis: Forecasting and control. CA: Holdan- Day, San Francisco, 1976. [8] O. Cagcag Yolcu, A Hybrid Fuzzy Time Series Approach Based on Fuzzy Clustering and Articial Neural Network with Single Multiplicative Neuron Model, Mathematical Problems in Engineering, Article ID 560472, 2013 (2013), 9 pages. [9] S. M. Chen, Forecasting enrollments based on fuzzy time-series, Fuzzy Sets and Systems, 81 (1996), 311-31. [10] S. M. Chen, Forecasting enrolments based on high order fuzzy time series, Cybernetics and Systems, 33 (2002), 1-16. [11] S. M. Chen and N. Y. Chung, Forecasting enrolments using high order fuzzy time series and genetic algorithms, International Journal of Intelligent Systems, 21 (2006), 485-501. [12] C. H. Cheng, T. L. Chen, H. J. Teoh and C. H. Chiang, Fuzzy time-series based on adaptive expectation model for TAIEX forecasting, Expert Systems with Applications, 34 (2008), 1126-1132. [13] C. H. Cheng, G. W. Cheng and J. W. Wang, Multi-attribute fuzzy time series method based on fuzzy clustering, Expert Systems with Applications, 34 (2008), 1235-1242. [14] S. Davari, M. H. F. Zarandi and I. B. Turksen, An Improved fuzzy time series forecasting model based on particle swarm intervalization, The 28th North American Fuzzy Information Processing Society Annual Conferences (NAFIPS 2009), Cincinnati, Ohio, USA, June 14-17, 2009. [15] E. Egrioglu, PSO-based high order time invariant fuzzy time series method: Application to stock exchange data, Economic Modelling, 38 (2014), 633-639. [16] E. Egrioglu, C. H. Aladag, U. Yolcu, M. A. Basaran and V. R. Uslu, A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model, Expert Systems with Applications, 36 (2009), 7424-7434. [17] E. Egrioglu, C. H. Aladag, U. Yolcu, V. R. Uslu and M. A. Basaran, A new approach based on articial neural networks for high order multivariate fuzzy time series, Expert Systems with Applications, 36 (2009), 10589-10594. [18] E. Egrioglu, C. H. Aladag, U. Yolcu, V. R. Uslu and M. A. Basaran, Finding an optimal interval length in high order fuzzy time series, Expert Systems with Applications, 37 (2010), 5052-5055. [19] E. Egrioglu, C. H. Aladag, M. A. Basaran, V. R. Uslu and U. Yolcu, A New Approach Based on the Optimization of the Length of Intervals in Fuzzy Time Series, Journal of Intelligent and Fuzzy Systems, 22 (2011), 15-19. [20] E. Egrioglu, C. H. Aladag, U. Yolcu, V. R. Uslu and N. A. Erilli, Fuzzy Time Series Forecast- ing Method Based on Gustafson-Kessel Fuzzy Clustering, Expert Systems with Applications, 38 (2011), 10355-10357. [21] E. Egrioglu, U. Yolcu, C. H. Aladag and C. Kocak, An ARMA Type Fuzzy Time Series Forecasting Method Based on Particle Swarm Optimization, Mathematical Problems in En- gineering, Article ID 935815, 2013 (2013), 12 pages. [22] S. Gunay, E. Egrioglu and C. H. Aladag, Introduction to univariate time series analysis. Hacettepe University Press, Ankara Turkey, 2007. [23] L. Y. Hsu, S. J. Horng, T. W. Kao, Y. H. Chen, R. S. Run, R. J. Chen, J. L. Lai and I. H. Kuo, Temperature prediction and TAIFEX forecasting based on fuzzy relationships and MTPSO techniques, Expert Systems with Application, 37 (2010), 2756-2770. [24] K. Huarng, Eective length of intervals to improve forecasting in fuzzy time-series, Fuzzy Sets and Systems, 123 (2001a), 387-394. [25] K. Huarng and H. K. Yu, Ratio-based lengths of intervals to improve fuzzy time series fore- casting, IEEE Trans. Syst. Man Cybern. B, Cybern., 36 (2006), 328-340. [26] K. Huarng and H. K. Yu, The application of neural networks to forecast fuzzy time series, Physica A, 363 (2006), 481-491. [27] M. Khashei, S. R. Hejazi and M. Bijari, A new hybrid articial neural networks and fuzzy regression model for time series forecasting, Fuzzy Sets and Systems, 159(7) (2008), 769-786. [28] I. H. Kuo, S. J. Horng, T. W. Kao, T. L. Lin, C. L. Lee and Y. Pan, An improved method for forecasting enrollments based on fuzzy time series and particle swarm optimization, Expert Systems with Application, 36 (2009), 6108-6117. [29] I. H. Kuo, S. J. Horng, Y. H. Chen, R. S. Run, T. W. Kao, R. J. Chen, J. L. Lai and T. L. Lin, Forecasting TAIFEX based on fuzzy time series and particle swarm optimization, Expert Systems with Application, 37 (2010), 1494-1502. [30] L. W. Lee, L. H. Wang and S. M. Chen, Temperature prediction and TAIFEX forecasting based on fuzzy logical relationships and genetic algorithms, Expert Systems with Applications, 33 (2007), 539-550. [31] K. Levenberg, A Method for the Solution of Certain Non-Linear Problems in Least Squares, The Quarterly of Applied Mathematics, 2 (1944), 164-168. [32] D. W. Marquardt, An algorithm for least-squares estimation of nonlinear parameters, Journal of the Society for Industrial and Applied Mathematics, 11 (1963), 431-441. [33] J. I. Park, D. J. Lee, C. K. Song and M. G. Chun, TAIFEX and KOSPI 200 forecasting based on two factors high order fuzzy time series and particle swarm optimization, Expert Systems with Application, 37 (2010), 959-967. [34] Q. Song, Seasonal forecasting in fuzzy time series, Fuzzy Sets and Systems, 107 (1999), 235-236. [35] Q. Song and B. S. Chissom, Fuzzy time series and its models, Fuzzy Sets and Systems, 54 (1993), 269-277. [36] Q. Song and B. S. Chissom, Forecasting enrollments with fuzzy time series- Part I, Fuzzy Sets and Systems, 54 (1993), 1-10. [37] Q. Song and B. S. Chissom, Forecasting enrollments with fuzzy time series- Part II, Fuzzy Sets and Systems, 62 (1994), 1-8. [38] U. Yolcu, E. Egrioglu, V. R. Uslu, M. A. Basaran and C. H. Aladag, A new approach for determining the length of intervals for fuzzy time series, Applied Soft Computing, 9(2) (2009), 647-651. [39] U. Yolcu, C. H. Aladag, E. Egrioglu and V. R. Uslu, Time series forecasting with a novel fuzzy time series approach: an example for Istanbul stock market, Journal of Statistical Computation and Simulation, 83(4) (2013), 597-610. [40] H. K. Yu, Weighted fuzzy time series models for TAIEX forecasting, Physica A, 349 (2005), 609- 624. [41] H. K. Yu and K. Huarng, A bivariate fuzzy time series model to forecast TAIEX, Expert Systems with Applications, 34 (2008), 2945-2952. [42] H. K. Yu and K. Huarng, A neural network- based fuzzy time series model to improve fore- casting, Expert Systems with Application, 37 (2010), 3366-3372. [43] L. A. Zadeh, Fuzzy Sets, Inform and Control, 8 (1965), 338-353. [44] G. P., Zhang, B. E., Patuwo and Y. M. Hu, Forecasting with articial neural networks: The state of the art, International Journal of Forecasting, 14 (1998), 35{62. [45] J. M. Zurada, Introduction of articial neural systems. St. Paul: West Publishing, (1992), 26-27. | ||

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