BEYOND EUCLIDEAN: A METRIC-OPTIMIZED TYPE-1 FUZZY SVR FUNCTIONS ARCHITECTURE FOR TIME SERIES FORECASTING

İbrahim Cimşit; Ali Zafer Dalar; Ufuk Yolcu

doi:10.37880/cumuiibf.1885704

EN TR

BEYOND EUCLIDEAN: A METRIC-OPTIMIZED TYPE-1 FUZZY SVR FUNCTIONS ARCHITECTURE FOR TIME SERIES FORECASTING

Abstract

This paper introduces a distance-aware forecasting framework based on type-1 fuzzy support vector regression functions, designed to examine the impact of distance metric selection on fuzzy time series prediction performance. The proposed approach extends fuzzification mechanisms by incorporating nine alternative distance functions into a fuzzy clustering-driven regression structure. These distance measures determine the formation of fuzzy partitions and directly influence the weighting scheme used in cluster-specific regression models. A unified optimization strategy is employed to jointly tune both fuzzification and regression components using a grid search procedure. This integrated optimization allows the framework to dynamically adapt to varying data distributions, temporal dependencies, and structural characteristics of time series data. The proposed method is validated through extensive experiments conducted on four real-world datasets from production and financial domains. The empirical findings reveal that distance metric choice significantly affects forecasting accuracy. In many experimental scenarios, non-Euclidean distance measures outperform classical Euclidean-based metrics. Distance functions emphasizing directional similarity, scale normalization, or maximum deviation frequently produce superior results. Chebyshev, standardized Euclidean, and Cosine distances achieve the lowest prediction errors across several datasets and evaluation periods. The results indicate that no single distance metric is universally optimal, underscoring the importance of metric flexibility and data-aware selection. The study demonstrates that integrating distance metric optimization into type-1 fuzzy support vector regression function-based architectures leads to consistent improvements in forecasting accuracy. The proposed architecture offers a generalizable solution for time series forecasting.

Keywords

ÖKLİDİN ÖTESİNDE: ZAMAN SERİSİ ÖNGÖRÜSÜ İÇİN METRİK-OPTİMİZE TİP-1 BULANIK DVR FONKSİYONLARI MİMARİSİ

Öz

Bu çalışma, bulanık zaman serisi tahmin performansı üzerinde uzaklık metriği seçiminin etkisini incelemek amacıyla, tip-1 bulanık destek vektör regresyon fonksiyonlarına dayalı, uzaklık metriklerine duyarlı bir tahmin çerçevesi önermektedir. Önerilen yaklaşım, bulanıklaştırma mekanizmalarını genişleterek, dokuz farklı uzaklık fonksiyonunu bulanık kümeleme temelli bir regresyon yapısına entegre etmektedir. Bu uzaklık metrikleri, bulanık bölümlerin oluşumunu belirlemekte ve kümeye özgü regresyon modellerinde kullanılan ağırlıklandırma şemasını doğrudan etkilemektedir. Bulanıklaştırma ve regresyon bileşenlerinin birlikte eniyilenmesi amacıyla, ızgara araması temelli birleşik bir optimizasyon stratejisi uygulanmıştır. Bu bütünleşik optimizasyon süreci, önerilen yapının farklı veri dağılımlarına, zamansal bağımlılıklara ve zaman serisi verilerinin yapısal özelliklerine dinamik biçimde uyum sağlamasına olanak tanımaktadır. Önerilen yöntem, üretim ve finans alanlarını temsil eden dört farklı zamansal davranış özelliklerine sahip gerçek hayat veri setleri üzerinde gerçekleştirilerek doğrulanmıştır. Bulgular, uzaklık metriği seçiminin tahmin doğruluğu üzerinde belirleyici bir etkiye sahip olduğunu ortaya koymaktadır. Birçok senaryoda, Öklid dışındaki uzaklık metriklerinin, Öklid tabanlı metriklere kıyasla daha başarılı sonuçlar ürettiği gözlemlenmiştir. Yönlülüğe duyarlı, ölçek normalizasyonunu dikkate alan ve maksimum sapmayı vurgulayan uzaklık fonksiyonları daha düşük tahmin hataları sağlamaktadır. Çebişev, standartlaştırılmış Öklid ve Kosinüs uzaklıkları, farklı veri kümeleri ve değerlendirme dönemleri boyunca en düşük tahmin hatalarını elde etmiştir. Elde edilen sonuçlar, tek bir uzaklık metriğinin tüm durumlar için evrensel olarak en iyi seçenek olmadığını göstermekte ve metrik esnekliği ile veri odaklı metrik seçiminin önemini ortaya koymaktadır. Çalışma, uzaklık metriği optimizasyonunun tip-1 bulanık destek vektör regresyon fonksiyonları tabanlı mimarilere entegre edilmesinin tahmin doğruluğunda tutarlı iyileşmeler sağladığını göstermekte ve zaman serisi tahmini için genellenebilir bir çözüm sunmaktadır.

Anahtar Kelimeler

References

Aktoprak, M. R., & Cagcag Yolcu, O. (2025). A New Approach for Time Series Prediction: Fuzzy Regression Network Functions. International Journal of Advances in Engineering and Pure Sciences, 37(1), 36–52. https://doi.org/10.7240/jeps.1573839
Aladag, C. H., Basaran, M. A., Egrioglu, E., Yolcu, U., & Uslu, V. R. (2009). Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations. Expert Systems with Applications, 36(3), 4228–4231.
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 Journal of Fuzzy Systems, 5(1), 1–9.
Aladag, C. H., Yolcu, U., Egrioglu, E., & Bas, E. (2014). Fuzzy lagged variable selection in fuzzy time series with genetic algorithms. Applied Soft Computing, 22, 465–473. https://doi.org/10.1016/j.asoc.2014.03.028
Aladag, C. H., Yolcu, U., Egrioglu, E., & Dalar, A. Z. (2012). A new time invariant fuzzy time series forecasting method based on particle swarm optimization. Applied Soft Computing, 12(10), 3291–3299. https://doi.org/10.1016/j.asoc.2012.05.002
Arora, J., Khatter, K., & Tushir, M. (2019). Fuzzy c-Means Clustering Strategies: A Review of Distance Measures. In M. N. Hoda, N. Chauhan, S. M. K. Quadri, & P. R. Srivastava (Eds.), Software Engineering (pp. 153–162). Singapore: Springer Singapore. https://doi.org/10.1007/978-981-10-8848-3_15
Bas, E. (2022). Robust fuzzy regression functions approaches. Information Sciences, 613, 419–434. https://doi.org/10.1016/j.ins.2022.09.047
Bas, E., & Egrioglu, E. (2022). A fuzzy regression functions approach based on Gustafson-Kessel clustering algorithm. Information Sciences, 592. https://doi.org/10.1016/j.ins.2022.01.057

Bas, E., Egrioglu, E., Aladag, C. H., & Yolcu, U. (2015). Fuzzy-time-series network used to forecast linear and nonlinear time series. Applied Intelligence, 43(2), 343–355. https://doi.org/10.1007/s10489-015-0647-0
Bas, E., Egrioglu, E., Yolcu, U., & Grosan, C. (2019). Type 1 fuzzy function approach based on ridge regression for forecasting. Granular Computing, 4(4), 629–637. https://doi.org/10.1007/s41066-018-0115-4
Bas, E., Uslu, V. R., Yolcu, U., & Egrioglu, E. (2014). A modified genetic algorithm for forecasting fuzzy time series. Applied Intelligence, 41(2), 453–463. https://doi.org/10.1007/s10489-014-0529-x
Bas, E., Yolcu, U., & Egrioglu, E. (2020). Picture fuzzy regression functions approach for financial time series based on ridge regression and genetic algorithm. Journal of Computational and Applied Mathematics, 370, 112656. https://doi.org/10.1016/j.cam.2019.112656
Bas, E., Yolcu, U., & Egrioglu, E. (2021). Intuitionistic fuzzy time series functions approach for time series forecasting. Granular Computing, 6, 619–629. https://doi.org/10.1007/s41066-020-00220-8
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. https://doi.org/10.1016/j.energy.2017.02.008
Beyhan, S., & Alci, M. (2010). Fuzzy functions based ARX model and new fuzzy basis function models for nonlinear system identification. Applied Soft Computing, 10(2), 439–444. https://doi.org/10.1016/j.asoc.2009.08.015
Bezdek, J. C., Ehrlich, R., & Full, W. E. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2–3), 191–203. https://doi.org/10.1016/0098-3004(84)90020-7
Bose, M., & Mali, K. (2019). Designing fuzzy time series forecasting models: A survey. International Journal of Approximate Reasoning, 111, 78–99. https://doi.org/10.1016/j.ijar.2019.05.002
Cagcag Yolcu, O., Yolcu, U., Egrioglu, E., & Aladag, C. H. (2016). High order fuzzy time series forecasting method based on an intersection operation. Applied Mathematical Modelling, 40(19), 8750–8765. https://doi.org/10.1016/j.apm.2016.05.012
Cagcag Yolcu, Ozge & Yolcu, Ufuk. (2024). A new ensemble intuitionistic fuzzy-deep forecasting model: Consolidation of the IFRFs-bENR with LSTM. Information Sciences. https://doi.org/10.1016/j.ins.2024.121007
Castillo, O., & Melin, P. (2020). Forecasting of COVID-19 time series for countries in the world based on a hybrid approach combining the fractal dimension and fuzzy logic. Chaos, Solitons & Fractals, 140, 110242. https://doi.org/10.1016/j.chaos.2020.110242
Celikyilmaz, A., & Turksen, I. B. (2007). Fuzzy functions with support vector machines. Information Sciences, 177(23), 5163–5177. https://doi.org/10.1016/j.ins.2007.06.022
Chakravarty, S., Demirhan, H., & Baser, F. (2022). Modified fuzzy regression functions with a noise cluster against outlier contamination. Expert Systems with Applications, 205. https://doi.org/10.1016/j.eswa.2022.117717
Chen, S. M., & Jian, W. S. (2017). Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups, similarity measures and PSO techniques. Information Sciences, 391–392. https://doi.org/10.1016/j.ins.2016.11.004
Chen, S.-M. (1996). Forecasting enrollments based on fuzzy time series. Fuzzy Sets and Systems, 81(3), 311–319.
Chen, S.-M. (2002). Forecasting enrollments based on high-order fuzzy time series. Cybernetics and Systems, 33(1), 1–16.
Chen, S.-M., & Kao, P.-Y. (2013). TAIEX forecasting based on fuzzy time series, particle swarm optimization techniques and support vector machines. Information Sciences, 247, 62–71. https://doi.org/10.1016/j.ins.2013.06.005
Chen, S.-M., & Phuong, B. D. H. (2017). Fuzzy time series forecasting based on optimal partitions of intervals and optimal weighting vectors. Knowledge-Based Systems, 118, 204–216. https://doi.org/10.1016/j.knosys.2016.11.019
Chen, S.-M., & Tanuwijaya, K. (2011). Fuzzy forecasting based on high-order fuzzy logical relationships and automatic clustering techniques. Expert Systems With Applications, 38(12), 15425–15437. https://doi.org/10.1016/j.eswa.2011.06.019
Dalar, A. Z., & Egrioglu, E. (2018). Bootstrap Type-1 Fuzzy Functions Approach for Time Series Forecasting. In Contributions to Statistics. Trends and Perspectives in Linear Statistical Inference (1st ed., pp. 69–87). Springer Cham. https://doi.org/10.1007/978-3-319-73241-1_5
Dalar, A. Z., & Egrioglu, E. (2025). Blending traditional and novel techniques: Hybrid type-1 fuzzy functions for forecasting. Engineering Applications of Artificial Intelligence, 148, 110445. https://doi.org/10.1016/j.engappai.2025.110445
Dalar, A. Z., Egrioglu, E., Yolcu, O. C., & Aladag, C. H. (2015). The Type-1 Fuzzy Function Approach Based on Artificial Neural Network for Forecasting. Conference of the International Journal of Arts & Sciences, 09(01), 191–192.
De Lima E Silva, P. C., Severiano, C. A., Alves, M. A., Silva, R., Weiss Cohen, M., & Guimarães, F. G. (2020). Forecasting in non-stationary environments with fuzzy time series. Applied Soft Computing, 97, 106825. https://doi.org/10.1016/j.asoc.2020.106825
Demirhan, H., & Baser, F. (2024). Hierarchical fuzzy regression functions for mixed predictors and an application to real estate price prediction. Neural Computing and Applications, 36(19), 11545–11561. https://doi.org/10.1007/s00521-024-09673-3
Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A., & Vapnik, V. (1996). Support Vector Regression Machines. In M. C. Mozer, M. Jordan, & T. Petsche (Eds.), Advances in Neural Information Processing Systems (Vol. 9). MIT Press. Retrieved from https://proceedings.neurips.cc/paper/1996/file/d38901788c533e8286cb6400b40b386d-Paper.pdf
Egrioglu, E., Bas, E., & Chen, M.-Y. (2024). A fuzzy Gaussian process regression function approach for forecasting problem. Granular Computing, 9. https://doi.org/10.1007/s41066-024-00475-5
Egrioglu, Erol, Aladag, C. H., & Yolcu, U. (2013). Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks. Expert Systems with Applications, 40(3), 854–857. https://doi.org/10.1016/j.eswa.2012.05.040
Egrioglu, Erol, Aladag, C. H., Yolcu, U., & Bas, E. (2014). A new adaptive network based fuzzy inference system for time series forecasting. Aloy Journal of Soft Computing and Applications, 2(1), 25–32.
Egrioglu, Erol, & Bas, E. (2023). Robust intuitionistic fuzzy regression functions approaches. Information Sciences, 638. https://doi.org/10.1016/j.ins.2023.118992
Egrioglu, Erol, Yolcu, U., & 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. https://doi.org/10.1007/s41066-018-00143-5
Investing.com. (2024a). Dow Jones Industrial Average (DJI) Historical Data. Retrieved from https://tr.investing.com/indices/us-30-historical-data (accessed 2025-04-28)
Investing.com. (2024b). VNI30 Index Historical Data. Retrieved from https://tr.investing.com/indices/vn-30-historical-data (accessed 2025-04-28)
Jang, J. S. R. (1993). ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665–685. https://doi.org/10.1109/21.256541
Khammar, A. H., Areﬁ, M., & Akbari, M. G. (2020). A robust least squares fuzzy regression model based on kernel function. Iranian Journal of Fuzzy Systems, 17(4), 105–119.
Kocak, C., Egrioglu, E., & Bas, E. (2021). A new deep intuitionistic fuzzy time series forecasting method based on long short-term memory. The Journal of Supercomputing, 77(6), 6178–6196. https://doi.org/10.1007/s11227-020-03503-8
Kumar, V., Chhabra, J. K., & Kumar, D. (2014). Impact of distance measures on the performance of clustering algorithms. 183–190. Springer. https://doi.org/10.1007/978-81-322-1665-0_17
Lin, Y., Liu, S., & Yu, J. (2013). Pricing Mortality Securities With Correlated Mortality Indexes. Journal of Risk and Insurance, 80(4), 921–948. https://doi.org/10.1111/j.1539-6975.2012.01481.x
López-Oriona, Á., Weiß, C. H., & Vilar, J. A. (2023). Two novel distances for ordinal time series and their application to fuzzy clustering. Fuzzy Sets and Systems, 468, 108590. https://doi.org/10.1016/j.fss.2023.108590
Lucas, P. O., Orang, O., Silva, P. C. L., Mendes, E. M. A. M., & Guimarães, F. G. (2022). A Tutorial on Fuzzy Time Series Forecasting Models: Recent Advances and Challenges. Learning and Nonlinear Models, 19(2), 29–50. https://doi.org/10.21528/lnlm-vol19-no2-art3
Luo, C., Tan, C., Wang, X., & Zheng, Y. (2019). An evolving recurrent interval type-2 intuitionistic fuzzy neural network for online learning and time series prediction. Applied Soft Computing, 78, 150–163. https://doi.org/10.1016/j.asoc.2019.02.032
Luo, J., Hong, T., Gao, Z., & Fang, S. C. (2023). A robust support vector regression model for electric load forecasting. International Journal of Forecasting, 39(2). https://doi.org/10.1016/j.ijforecast.2022.04.001
Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1–13. https://doi.org/10.1016/S0020-7373(75)80002-2
Ojha, V., Abraham, A., & Snášel, V. (2019). Heuristic design of fuzzy inference systems: A review of three decades of research. Engineering Applications of Artificial Intelligence, 85, 845–864. https://doi.org/10.1016/j.engappai.2019.08.010
Packt Publishing. (2022). The code repository for Practical Time-Series Analysis. Retrieved from https://github.com/PacktPublishing/Practical-Time-Series-Analysis (accessed 2025-04-28)
Palomero, L., García, V., & Sánchez, J. S. (2022). Fuzzy-Based Time Series Forecasting and Modelling: A Bibliometric Analysis. Applied Sciences, 12(14), 6894. https://doi.org/10.3390/app12146894
Panigrahi, S., & Behera, H. S. (2020). Fuzzy Time Series Forecasting: A Survey. In H. S. Behera, J. Nayak, B. Naik, & D. Pelusi (Eds.), Computational Intelligence in Data Mining (pp. 641–651). Singapore: Springer Singapore. https://doi.org/10.1007/978-981-13-8676-3_54
Pattanayak, R. M., Behera, H. S., & Panigrahi, S. (2021). A novel probabilistic intuitionistic fuzzy set based model for high order fuzzy time series forecasting. Engineering Applications of Artificial Intelligence, 99, 104136. https://doi.org/10.1016/j.engappai.2020.104136
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … others. (2011). Scikit-learn: Machine learning in Python. The Journal of Machine Learning Research, 12, 2825–2830.
Peng, H. W., Wu, S. F., Wei, C. C., & Lee, S. J. (2015). Time series forecasting with a neuro-fuzzy modeling scheme. Applied Soft Computing, 32. https://doi.org/10.1016/j.asoc.2015.03.059
Sadaei, H. J., De Lima E Silva, P. C., Guimarães, F. G., & Lee, M. H. (2019). Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series. Energy, 175, 365–377. https://doi.org/10.1016/j.energy.2019.03.081
Sharma, D. K., Hota, H. S., & Rababaah, A. R. (2024). Forecasting US stock price using hybrid of wavelet transforms and adaptive neuro fuzzy inference system. International Journal of System Assurance Engineering and Management, 15(2), 591–608. https://doi.org/10.1007/s13198-021-01217-5
Singh, P., & Dhiman, G. (2018). A hybrid fuzzy time series forecasting model based on granular computing and bio-inspired optimization approaches. Journal of Computational Science, 27, 370–385. https://doi.org/10.1016/j.jocs.2018.05.008
Song, Q., & Chissom, B. S. (1993a). Forecasting enrollments with fuzzy time series—Part I. Fuzzy Sets and Systems, 54(1), 1–9. https://doi.org/10.1016/0165-0114(93)90355-L
Song, Q., & Chissom, B. S. (1993b). Fuzzy time series and its models. Fuzzy Sets and Systems, 54(3), 269–277. https://doi.org/10.1016/0165-0114(93)90372-O
Song, Q., & Chissom, B. S. (1994). Forecasting enrollments with fuzzy time series—Part II. Fuzzy Sets and Systems, 62(1), 1–8. https://doi.org/10.1016/0165-0114(94)90067-1
Soto, J., Castillo, O., Melin, P., & Pedrycz, W. (2019). A New Approach to Multiple Time Series Prediction Using MIMO Fuzzy Aggregation Models with Modular Neural Networks. International Journal of Fuzzy Systems, 21(5), 1629–1648. https://doi.org/10.1007/s40815-019-00642-w
Svozil, D., Kvasnicka, V., & Pospichal, J. (1997). Introduction to multi-layer feed-forward neural networks. Chemometrics and Intelligent Laboratory Systems, 39(1), 43–62. https://doi.org/10.1016/S0169-7439(97)00061-0
Taiwan Stock Exchange. (2024). TAIEX Index Historical Data. Retrieved from https://www.twse.com.tw/en/indices/taiex/mi-5min-hist.html (accessed 2025-04-28)
Tak, N. (2018). Meta fuzzy functions: Application of recurrent type-1 fuzzy functions. Applied Soft Computing, 73, 1–13. https://doi.org/10.1016/j.asoc.2018.08.009
Tak, N. (2020a). Type-1 possibilistic fuzzy forecasting functions. Journal of Computational and Applied Mathematics, 370. https://doi.org/10.1016/j.cam.2019.112653
Tak, N. (2020b). Type-1 Recurrent Intuitionistic Fuzzy Functions for Forecasting. Expert Systems With Applications, 140, 112913. https://doi.org/10.1016/j.eswa.2019.112913
Tak, N. (2022). A Novel ARMA Type Possibilistic Fuzzy Forecasting Functions Based on Grey-Wolf Optimizer (ARMA-PFFs). Computational Economics, 59(4), 1539–1556. https://doi.org/10.1007/s10614-021-10132-7
Tak, N., Evren, A. A., Tez, M., & Egrioglu, E. (2018). Recurrent type-1 fuzzy functions approach for time series forecasting. Applied Intelligence, 48(1), 68–77. https://doi.org/10.1007/s10489-017-0962-8
Tak, N., & Inan, D. (2022). Type-1 fuzzy forecasting functions with elastic net regularization. Expert Systems with Applications, 199. https://doi.org/10.1016/j.eswa.2022.116916
Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, SMC-15(1), 116–132. https://doi.org/10.1109/TSMC.1985.6313399
Thrun, M. C. (2021). Distance-based clustering challenges for unbiased benchmarking studies. Scientific Reports, 11(1). https://doi.org/10.1038/s41598-021-98126-1
Turksen, I. B. (2008). Fuzzy functions with LSE. Applied Soft Computing, 8(3), 1178–1188. https://doi.org/10.1016/j.asoc.2007.12.004
Vapnik, V. N. (2000). The Nature of Statistical Learning Theory. New York, NY: Springer New York. https://doi.org/10.1007/978-1-4757-3264-1
Velez-Falconi, M., Marin, J., Jimenez, S., & Guachi-Guachi, L. (2020). Comparative Study of Distance Measures for the Fuzzy C-means and K-means Non-Supervised Methods Applied to Image Segmentation. CEUR Workshop Proceedings, 1–14. Nigeria. Retrieved from https://ceur-ws.org/Vol-2714/icaiw_waai_1.pdf
Wan, Y., & Si, Y.-W. (2017). Adaptive neuro fuzzy inference system for chart pattern matching in financial time series. Applied Soft Computing, 57, 1–18. https://doi.org/10.1016/j.asoc.2017.03.023
Yager, R. R., & Filev, D. P. (1994). Generation of Fuzzy Rules by Mountain Clustering. Journal of Intelligent & Fuzzy Systems, 2(3), 209–219. https://doi.org/10.3233/IFS-1994-2301
Yang, J., Gao, X., Zhang, D., & Yang, J. (2005). Kernel ICA: An alternative formulation and its application to face recognition. Pattern Recognition, 38(10), 1784–1787.
Ye, F., Zhang, L., Zhang, D., Fujita, H., & Gong, Z. (2016). A novel forecasting method based on multi-order fuzzy time series and technical analysis. Information Sciences, 367–368, 41–57. https://doi.org/10.1016/j.ins.2016.05.038
Yolcu, U., Egrioglu, E., & Aladag, C. H. (2013). A new linear & nonlinear artificial neural network model for time series forecasting. Decision Support Systems, 54(3), 1340–1347. https://doi.org/10.1016/j.dss.2012.12.006
Yolcu, U., Yolcu, O. C., Aladag, C. H., & Egrioglu, E. (2014). An enhanced fuzzy time series forecasting method based on artificial bee colony. Journal of Intelligent & Fuzzy Systems, 26(6), 2627–2637. https://doi.org/10.3233/Ifs-130933
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Zarandi, M. H. F., Zarinbal, M., Ghanbari, N., & Turksen, I. B. (2013). A new fuzzy functions model tuned by hybridizing imperialist competitive algorithm and simulated annealing. Application: Stock price prediction. Information Sciences, 222, 213–228. https://doi.org/10.1016/j.ins.2012.08.002
Zhang, R., Ashuri, B., & Deng, Y. (2017). A novel method for forecasting time series based on fuzzy logic and visibility graph. Advances in Data Analysis and Classification, 11(4), 759–783. https://doi.org/10.1007/s11634-017-0300-3
Zhao, X., Li, Y., & Zhao, Q. (2015). Mahalanobis distance based on fuzzy clustering algorithm for image segmentation. Digital Signal Processing, 43, 8–16. https://doi.org/10.1016/j.dsp.2015.04.009

Details

Primary Language

English

Subjects

Time-Series Analysis

Journal Section

Research Article

Authors

İbrahim Cimşit ^*
0000-0002-7345-8093
Türkiye

Ali Zafer Dalar
0000-0002-8574-461X
Türkiye

Ufuk Yolcu
0000-0002-0172-3353
Türkiye

Publication Date

April 26, 2026

Submission Date

February 10, 2026

Acceptance Date

April 13, 2026

Published in Issue

Year 2026 Volume: 27 Number: 2

DOI

https://doi.org/10.37880/cumuiibf.1885704

IZ

https://izlik.org/JA92PN43BL

Cite

RIS / Bibtex

APA

Cimşit, İ., Dalar, A. Z., & Yolcu, U. (2026). BEYOND EUCLIDEAN: A METRIC-OPTIMIZED TYPE-1 FUZZY SVR FUNCTIONS ARCHITECTURE FOR TIME SERIES FORECASTING. Cumhuriyet Üniversitesi İktisadi Ve İdari Bilimler Dergisi, 27(2), 639-672. https://doi.org/10.37880/cumuiibf.1885704