Aralıklı tip-2 bulanık c-ortalama ile zaman serilerinin bulanık dilsel özetlemesi: BIST100 örnek hisse uygulaması

İlyas Özdoğan; Fatih Emre Boran; Oktay Yıldız

doi:10.17341/gazimmfd.1263678

Araştırma Makalesi

Fuzzy linguistic summarization of time series with interval type-2 Fuzzy c-means: BIST100 sample stock application

Yıl 2025, Cilt: 40 Sayı: 3, 1659 - 1672

İlyas Özdoğan , Fatih Emre Boran , Oktay Yıldız

https://doi.org/10.17341/gazimmfd.1263678

Öz

Linguistic summarization, which has garnered significant attention in recent years, facilitates the derivation of human-understandable insights from vast amounts of data. One critical aspect of linguistic summarization involves determining the truth degree that reflects the extent to which the underlying data has been appropriately represented. In the extant literature, the numerical values of fuzzy sets employed to calculate truth degrees have been generated using the uniform partitioning method, which neglects the intervals of data concentration. To address this limitation, the present study proposes the Interval Type-2 Fuzzy C-Means (IT2FCM) partitioning method, which distributes fuzzy sets, taking into account the intervals of data density. By using the proposed method, interval type-2 fuzzy sets are created, and the truth degrees of linguistic summaries are calculated using fuzzy cardinality-based probability and possibility based approaches. The proposed approach is explicated step by step, and its outcomes are compared to those of the uniform partitioning method used in prior research. To evaluate the efficacy of the proposed IT2FCM partitioning approach, we apply it to financial time series covering the last decade of three stocks traded on the Borsa İstanbul (BIST). The findings indicate that the proposed IT2FCM partitioning method generates more stable outcomes compared to the uniform partitioning studies reported in the literature.

Anahtar Kelimeler

Linguistic Summarization, Interval Type-2 Fuzzy C-Means, Data Mining, Time Series

Kaynakça

1. Lecun, Y., Bengio, Y., Hinton, G., Deep learning, Nature. 2015.
2. Zhao, Z. Q., Zheng, P., Xu, S. T., Wu, X., Object Detection with Deep Learning: A Review, IEEE Trans Neural Netw Learn Syst. 2019.
3. Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., Platen, P. von, Ma, C., Jernite, Y., Plu, J., Xu, C., Scao, T. Le, Gugger, S., Drame, M., Lhoest, Q., Rush, A. M., Transformers: State-of-the-Art Natural Language Processing, 38-45, 2020.
4. Qiu, X. P., Sun, T. X., Xu, Y. G., Shao, Y. F., Dai, N., Huang, X. J., Pre-trained models for natural language processing: A survey, Sci China Technol Sci, 63 (10), 1872-1897, 2020.
5. Liu, P., Yuan, W., Jiang, Z., Hayashi, H., Neubig, G., Fu, J., Yuan, W., Jiang, Z., Hayashi, H., Neubig, G., Fu, J., Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing, ACM Comput Surv, 55 (9), 1-35, 2023.
6. Topol, E. J., High-performance medicine: the convergence of human and artificial intelligence, Nature Medicine, 25 (1), 44-56, 2019.
7. He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., Zhang, K., The practical implementation of artificial intelligence technologies in medicine, Nat Med, 25 (1), 30-36, 2019.
8. Fernandes, M., Vieira, S. M., Leite, F., Palos, C., Finkelstein, S., Sousa, J. M. C., Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: A Review, Artif Intell Med, 102, 101762, 2020.
9. Otter, D. W., Medina, J. R., Kalita, J. K., A Survey of the Usages of Deep Learning for Natural Language Processing, IEEE Trans Neural Netw Learn Syst, 2020.
10. Xi, L., Zhang, L., Liu, J., Li, Y., Chen, X., Yang, L., Wang, S., A virtual generation ecosystem control strategy for automatic generation control of interconnected microgrids, IEEE Access, 8, 94165-94175, 2020.
11. Xi, L., Zhou, L., Liu, L., Duan, D., Xu, Y., Yang, L., Wang, S., A deep reinforcement learning algorithm for the order optimization allocation of total power in the interconnected power grids, CSEE Journal of Power and Energy Systems, 6, 713-723, 2020.
12. Ribeiro, M. T., Wu, T., Guestrin, C., Singh, S., Beyond Accuracy: Behavioral Testing of NLP Models with CheckList, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 4902-4912, 2020.
13. Zadeh, L. A., Fuzzy sets, Information and Control, 8 (3), 338-353, 1965.
14. Yager R.R., A new approach to the summarization of data, Inf Sci (N Y), 28 (1), 69-86, 1982.
15. Kacprzyk, J., Yager, R. R., Linguistic summaries of data using fuzzy logic, Int J Gen Syst, 30 (2), 133-154, 2001.
16. Zadeh, L. A., A computational approach to fuzzy quantifiers in natural languages, Computers and Mathematics with Applications, 9 (1), 149-184, 1983.
17. Kacprzyk, J., Wilbik, A., Zadrozny, S., Linguistic summarization of trends: a fuzzy logic based approach, 11th International Conference Information Processing and Management of Uncertainty in Knowledge-based Systems, 2166–2172, 2006.
18. Sklansky, J., Gonzalez, V., Fast polygonal approximation of digitized curves, Pattern Recognit, 12 (5), 327-331, 1980.
19. Kacprzyk, J., Wilbik, A., Zadrozny, S., Capturing the essence of a dynamic behavior of sequences of numerical data using elements of a quasi-natural language, IEEE International Conference on Systems, Man and Cybernetics, 4, 3365-3370, 2007.
20. Kacprzyk, J., Wilbik, A., Zadrozny, S., A linguistic quantifier based aggregation for a human consistent summarization of time series, Advances in Soft Computing, 37, 183-190, 2006.
21. Kacprzyk, J., Wilbik, A., Zadrozny, S., On some types of linguistic summaries of time series, 3rd International IEEE Conference Intelligent Systems, 373-378, 2006.
22. Kacprzyk, J., Wilbik, A., Zadrozny, S., Linguistic summarization of time series under different granulation of describing features, Lecture Notes in Computer Science, 4585 LNAI, 230-240, 2007.
23. Kacprzyk, J., Wilbik, A., Zadrozny, S., Linguistic summarization of time series using a fuzzy quantifier driven aggregation, Fuzzy Sets Syst, 159 (12), 1485-1499, 2008.
24. Kacprzyk, J., Wilbik, A., Zadrozny, S., Linguistic summaries of time series via a quantifier based aggregation using the Sugeno integral, IEEE International Conference on Fuzzy Systems, 713-719, 2006.
25. Kacprzyk, J., Wilbik, A., Zadrozny, S., Analysis of Time Series via their Linguistic Summarization: The Use of the Sugeno Integral, Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007), 262-270, 2007.
26. Kacprzyk, J., Wilbik, A., Zadrozny, S., Mining time series data via linguistic summaries of trends by using a modified Sugeno integral based aggregation, Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007, 742-749, 2007.
27. Kacprzyk, J., Wilbik, A., Zadrożny, S., Linguistic Summarization of Time Series by Using the Choquet Integral, Lecture Notes in Computer Science (LNCS), 4529, 284-294, 2007.
28. Kacprzyk, J., Wilbik, A., Zadrozny, S., Linguistic summaries of time series via an OWA operator based aggregation of partial trends, IEEE International Conference on Fuzzy Systems, 1-6, 2007.
29. Kacprzyk, J., Wilbik, A., Using fuzzy linguistic summaries for the comparison of time series: an application to the analysis of investment fund quotations, IFSA/EUSFLAT, 2009, 1321-1326, 2009.
30. Castillo-Ortega, R., Marín, N., Martínez-Cruz, C., Sánchez, D., A proposal for the hierarchical segmentation of time series. Application to trend-based linguistic description, IEEE International Conference on Fuzzy Systems, 489-496, 2014.
31. Castillo-Ortega, R., Marin, N., Martinez-Cruz, C., Sanchez, D., Linguistic comparison of time series using the End-Point Fit algorithm, IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1-8, 2015.
32. Ramos-Soto, A., Bugarin, A., Barro, S., Computing with perceptions for the linguistic description of complex phenomena through the analysis of time series data, 7th ICAART Conference, Doctoral Consortium Session, 7, 2015.
33. Hatipoǧlu, H., Boran, F. E., Avci, M., Akay, D., Linguistic summarization of Europe Brent spot price time series along with the interpretations from the perspective of Turkey, International Journal of Intelligent Systems, John Wiley and Sons Ltd, 29 (10), 946-970, 2014.
34. Sanchez-Valdes, D., Alvarez-Alvarez, A., Trivino, G., Dynamic linguistic descriptions of time series applied to self-track the physical activity, Fuzzy Sets Syst, 285, 162-181, 2016.
35. Kacprzyk, J., Zadrozny, S., Fuzzy logic-based linguistic summaries of time series: A powerful tool for discovering knowledge on time varying processes and systems under imprecision, Wiley Interdiscip Rev Data Min Knowl Discov, 6 (1), 37-46, 2016.
36. Kaczmarek, K., Hryniewicz, O., Kruse, R., Human input about linguistic summaries in time series forecasting, ACHI 2015 - 8th International Conference on Advances in Computer-Human Interactions, 1, 9-13, 2015.
37. Moyse, G., Lesot, M. J., Linguistic summaries of locally periodic time series, Fuzzy Sets Syst, 285, 94-117, 2016.
38. Marín, N., Sánchez, D., On generating linguistic descriptions of time series, Fuzzy Sets Syst, 285, 6-30, 2016.
39. Kaczmarek, K., Hryniewicz, O., Time Series Classification with Linguistic Summaries, 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (IFSA-EUSFLAT-15), 2015.
40. Kaczmarek-Majer, K., Hryniewicz, O., Application of linguistic summarization methods in time series forecasting, Inf Sci (N Y), 478, 580-594, 2019.
41. Dündar, B., Akay, D., Boran, F. E., Özdemir, S., Fuzzy Quantification and Opinion Mining on Qualitative Data using Feature Reduction, International Journal of Intelligent Systems, 33 (9), 1840-1857, 2018.
42. Jain, A., Popescu, M., Keller, J., Rantz, M., Markway, B., Linguistic summarization of in-home sensor data, J Biomed Inform, 96, 103240, 2019.
43. Genç, S., Akay, D., Boran, F. E., Yager, R. R., Linguistic summarization of fuzzy social and economic networks: an application on the international trade network, Soft comput, 24 (2), 1511-1527, 2020.
44. Niewiadomski, A., A type-2 fuzzy approach to linguistic summarization of data, IEEE Transactions on Fuzzy Systems, 16 (1), 198-212, 2008.
45. Niewiadomski, A., On finity, countability, cardinalities, and cylindric extensions of type-2 fuzzy sets in linguistic summarization of databases, IEEE Transactions on Fuzzy Systems, 18 (3), 532-545, 2010.
46. Wu, D., Mendel, J. M., Linguistic summarization using IFTHEN rules and interval Type-2 fuzzy sets, IEEE Transactions on Fuzzy Systems, 19 (1), 136-151, 2011.
47. Boran, F. E., Akay, D., A generic method for the evaluation of interval type-2 fuzzy linguistic summaries, IEEE Trans Cybern, 44 (9), 1632-1645, 2014.
48. Delgado, M., Sánchez, D., Vila, M. A., Fuzzy cardinality based evaluation of quantified sentences, International Journal of Approximate Reasoning, 23 (1), 23-66, 2000.
49. Boran, F. E., Akay, D., Yager, R. R., A probabilistic framework for interval type-2 fuzzy linguistic summarization, IEEE Transactions on Fuzzy Systems, 22 (6), 1640-1653, 2014.
50. Özdoğan, İ., Boran, F. E., Akay, D., A possibilistic approach for interval type-2 fuzzy linguistic summarization of time series, Artificial Intelligence Review 2021 54:5, 54 (5), 3991-4018, 2021.
51. Klir, G. J., Yuan, Bo., Fuzzy sets and fuzzy logic : Theory and applications. Prentice Hall PTR, 1995. 52. Mendel, J. M., Uncertain Rule-Based Fuzzy Systems. Springer International Publishing, Cham, 2017. 53. Ross, T. J., Fuzzy logic with engineering applications. John Wiley, 2010.
54. Mendel, J. M., John, R. I., Liu, F., Interval type-2 fuzzy logic systems made simple, IEEE Transactions on Fuzzy Systems, 14, 808-821, 2006.
55. Hwang, C., Rhee, F. C. H., Uncertain fuzzy clustering: Interval type-2 fuzzy approach to C-means, IEEE Transactions on Fuzzy Systems, 15 (1), 107-120, 2007.
56. Wang, J., Kim, J., Predicting stock price trend using MACD optimized by historical volatility, Math Probl Eng, 2018, 2018.
57. Xie, X. L., Beni, G., A validity measure for fuzzy clustering, IEEE Trans Pattern Anal Mach Intell, 13 (8), 841-847, 1991.
58. Boran, F. E., Akay, D., Yager, R. R., An overview of methods for linguistic summarization with fuzzy sets, Expert Syst Appl, 61, 356-377, 2016.
59. Hu, C., Hu, Z. H., On Statistics, Probability, and Entropy of Interval-Valued Datasets, Communications in Computer and Information Science, 1239 CCIS, 407-421, 2020.

Aralıklı tip-2 bulanık c-ortalama ile zaman serilerinin bulanık dilsel özetlemesi: BIST100 örnek hisse uygulaması

Yıl 2025, Cilt: 40 Sayı: 3, 1659 - 1672

İlyas Özdoğan , Fatih Emre Boran , Oktay Yıldız

https://doi.org/10.17341/gazimmfd.1263678

Öz

Son yıllarda büyük ilgi gören dilsel özetleme, büyük verilerden insanların anlayabileceği çıkarımlar sağlamaktadır. Dilsel özetlemenin önemli bir aşaması, verilerin ne kadar doğru yansıtıldığını gösteren doğruluk derecesinin belirlenmesidir. Bu doğruluk derecelerinin hesaplanmasında literatürde eşit bölümleme yöntemi kullanılmaktadır fakat bu yöntem verilerin yoğunlaştığı aralıkları göz ardı etmektedir. Bu çalışmada, verilerin yoğun olarak yer aldığı aralıkları dikkate alarak bulanık kümeleri dağıtan Aralıklı Tip-2 Bulanık C Ortalama (AT2BCO) bölümleme yöntemi önerilmektedir. Önerilen yöntem ile aralıklı tip-2 bulanık kümeler oluşturulmuş, bulanık kardinalite tabanlı olasılık ve olabilirlik temelli yaklaşımlar kullanılarak dilsel özetlerin doğruluk dereceleri hesaplanmıştır. Önerilen yaklaşım adım adım açıklanmış ve literatürde kullanılan eşit bölümleme yöntemi ile sonuçları karşılaştırılmıştır. Önerilen AT2BCO bölümleme yöntemi, Borsa İstanbul'da (BIST) işlem gören üç hisse senedine ait son on yıllık dönemi kapsayan finansal zaman serilerine uygulanmıştır. Elde edilen sonuçlar, literatürde yer alan eşit bölümleme çalışmalarına göre daha kararlı sonuçlar ürettiğini göstermektedir.

Anahtar Kelimeler

Dilsel Özetleme, Aralıklı Tip-2 Bulanık C-Ortalama, Veri Madenciliği, Zaman Serileri

Kaynakça

1. Lecun, Y., Bengio, Y., Hinton, G., Deep learning, Nature. 2015.
2. Zhao, Z. Q., Zheng, P., Xu, S. T., Wu, X., Object Detection with Deep Learning: A Review, IEEE Trans Neural Netw Learn Syst. 2019.
3. Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., Platen, P. von, Ma, C., Jernite, Y., Plu, J., Xu, C., Scao, T. Le, Gugger, S., Drame, M., Lhoest, Q., Rush, A. M., Transformers: State-of-the-Art Natural Language Processing, 38-45, 2020.
4. Qiu, X. P., Sun, T. X., Xu, Y. G., Shao, Y. F., Dai, N., Huang, X. J., Pre-trained models for natural language processing: A survey, Sci China Technol Sci, 63 (10), 1872-1897, 2020.
5. Liu, P., Yuan, W., Jiang, Z., Hayashi, H., Neubig, G., Fu, J., Yuan, W., Jiang, Z., Hayashi, H., Neubig, G., Fu, J., Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing, ACM Comput Surv, 55 (9), 1-35, 2023.
6. Topol, E. J., High-performance medicine: the convergence of human and artificial intelligence, Nature Medicine, 25 (1), 44-56, 2019.
7. He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., Zhang, K., The practical implementation of artificial intelligence technologies in medicine, Nat Med, 25 (1), 30-36, 2019.
8. Fernandes, M., Vieira, S. M., Leite, F., Palos, C., Finkelstein, S., Sousa, J. M. C., Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: A Review, Artif Intell Med, 102, 101762, 2020.
9. Otter, D. W., Medina, J. R., Kalita, J. K., A Survey of the Usages of Deep Learning for Natural Language Processing, IEEE Trans Neural Netw Learn Syst, 2020.
10. Xi, L., Zhang, L., Liu, J., Li, Y., Chen, X., Yang, L., Wang, S., A virtual generation ecosystem control strategy for automatic generation control of interconnected microgrids, IEEE Access, 8, 94165-94175, 2020.
11. Xi, L., Zhou, L., Liu, L., Duan, D., Xu, Y., Yang, L., Wang, S., A deep reinforcement learning algorithm for the order optimization allocation of total power in the interconnected power grids, CSEE Journal of Power and Energy Systems, 6, 713-723, 2020.
12. Ribeiro, M. T., Wu, T., Guestrin, C., Singh, S., Beyond Accuracy: Behavioral Testing of NLP Models with CheckList, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 4902-4912, 2020.
13. Zadeh, L. A., Fuzzy sets, Information and Control, 8 (3), 338-353, 1965.
14. Yager R.R., A new approach to the summarization of data, Inf Sci (N Y), 28 (1), 69-86, 1982.
15. Kacprzyk, J., Yager, R. R., Linguistic summaries of data using fuzzy logic, Int J Gen Syst, 30 (2), 133-154, 2001.
16. Zadeh, L. A., A computational approach to fuzzy quantifiers in natural languages, Computers and Mathematics with Applications, 9 (1), 149-184, 1983.
17. Kacprzyk, J., Wilbik, A., Zadrozny, S., Linguistic summarization of trends: a fuzzy logic based approach, 11th International Conference Information Processing and Management of Uncertainty in Knowledge-based Systems, 2166–2172, 2006.
18. Sklansky, J., Gonzalez, V., Fast polygonal approximation of digitized curves, Pattern Recognit, 12 (5), 327-331, 1980.
19. Kacprzyk, J., Wilbik, A., Zadrozny, S., Capturing the essence of a dynamic behavior of sequences of numerical data using elements of a quasi-natural language, IEEE International Conference on Systems, Man and Cybernetics, 4, 3365-3370, 2007.
20. Kacprzyk, J., Wilbik, A., Zadrozny, S., A linguistic quantifier based aggregation for a human consistent summarization of time series, Advances in Soft Computing, 37, 183-190, 2006.
21. Kacprzyk, J., Wilbik, A., Zadrozny, S., On some types of linguistic summaries of time series, 3rd International IEEE Conference Intelligent Systems, 373-378, 2006.
22. Kacprzyk, J., Wilbik, A., Zadrozny, S., Linguistic summarization of time series under different granulation of describing features, Lecture Notes in Computer Science, 4585 LNAI, 230-240, 2007.
23. Kacprzyk, J., Wilbik, A., Zadrozny, S., Linguistic summarization of time series using a fuzzy quantifier driven aggregation, Fuzzy Sets Syst, 159 (12), 1485-1499, 2008.
24. Kacprzyk, J., Wilbik, A., Zadrozny, S., Linguistic summaries of time series via a quantifier based aggregation using the Sugeno integral, IEEE International Conference on Fuzzy Systems, 713-719, 2006.
25. Kacprzyk, J., Wilbik, A., Zadrozny, S., Analysis of Time Series via their Linguistic Summarization: The Use of the Sugeno Integral, Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007), 262-270, 2007.
26. Kacprzyk, J., Wilbik, A., Zadrozny, S., Mining time series data via linguistic summaries of trends by using a modified Sugeno integral based aggregation, Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007, 742-749, 2007.
27. Kacprzyk, J., Wilbik, A., Zadrożny, S., Linguistic Summarization of Time Series by Using the Choquet Integral, Lecture Notes in Computer Science (LNCS), 4529, 284-294, 2007.
28. Kacprzyk, J., Wilbik, A., Zadrozny, S., Linguistic summaries of time series via an OWA operator based aggregation of partial trends, IEEE International Conference on Fuzzy Systems, 1-6, 2007.
29. Kacprzyk, J., Wilbik, A., Using fuzzy linguistic summaries for the comparison of time series: an application to the analysis of investment fund quotations, IFSA/EUSFLAT, 2009, 1321-1326, 2009.
30. Castillo-Ortega, R., Marín, N., Martínez-Cruz, C., Sánchez, D., A proposal for the hierarchical segmentation of time series. Application to trend-based linguistic description, IEEE International Conference on Fuzzy Systems, 489-496, 2014.
31. Castillo-Ortega, R., Marin, N., Martinez-Cruz, C., Sanchez, D., Linguistic comparison of time series using the End-Point Fit algorithm, IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1-8, 2015.
32. Ramos-Soto, A., Bugarin, A., Barro, S., Computing with perceptions for the linguistic description of complex phenomena through the analysis of time series data, 7th ICAART Conference, Doctoral Consortium Session, 7, 2015.
33. Hatipoǧlu, H., Boran, F. E., Avci, M., Akay, D., Linguistic summarization of Europe Brent spot price time series along with the interpretations from the perspective of Turkey, International Journal of Intelligent Systems, John Wiley and Sons Ltd, 29 (10), 946-970, 2014.
34. Sanchez-Valdes, D., Alvarez-Alvarez, A., Trivino, G., Dynamic linguistic descriptions of time series applied to self-track the physical activity, Fuzzy Sets Syst, 285, 162-181, 2016.
35. Kacprzyk, J., Zadrozny, S., Fuzzy logic-based linguistic summaries of time series: A powerful tool for discovering knowledge on time varying processes and systems under imprecision, Wiley Interdiscip Rev Data Min Knowl Discov, 6 (1), 37-46, 2016.
36. Kaczmarek, K., Hryniewicz, O., Kruse, R., Human input about linguistic summaries in time series forecasting, ACHI 2015 - 8th International Conference on Advances in Computer-Human Interactions, 1, 9-13, 2015.
37. Moyse, G., Lesot, M. J., Linguistic summaries of locally periodic time series, Fuzzy Sets Syst, 285, 94-117, 2016.
38. Marín, N., Sánchez, D., On generating linguistic descriptions of time series, Fuzzy Sets Syst, 285, 6-30, 2016.
39. Kaczmarek, K., Hryniewicz, O., Time Series Classification with Linguistic Summaries, 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (IFSA-EUSFLAT-15), 2015.
40. Kaczmarek-Majer, K., Hryniewicz, O., Application of linguistic summarization methods in time series forecasting, Inf Sci (N Y), 478, 580-594, 2019.
41. Dündar, B., Akay, D., Boran, F. E., Özdemir, S., Fuzzy Quantification and Opinion Mining on Qualitative Data using Feature Reduction, International Journal of Intelligent Systems, 33 (9), 1840-1857, 2018.
42. Jain, A., Popescu, M., Keller, J., Rantz, M., Markway, B., Linguistic summarization of in-home sensor data, J Biomed Inform, 96, 103240, 2019.
43. Genç, S., Akay, D., Boran, F. E., Yager, R. R., Linguistic summarization of fuzzy social and economic networks: an application on the international trade network, Soft comput, 24 (2), 1511-1527, 2020.
44. Niewiadomski, A., A type-2 fuzzy approach to linguistic summarization of data, IEEE Transactions on Fuzzy Systems, 16 (1), 198-212, 2008.
45. Niewiadomski, A., On finity, countability, cardinalities, and cylindric extensions of type-2 fuzzy sets in linguistic summarization of databases, IEEE Transactions on Fuzzy Systems, 18 (3), 532-545, 2010.
46. Wu, D., Mendel, J. M., Linguistic summarization using IFTHEN rules and interval Type-2 fuzzy sets, IEEE Transactions on Fuzzy Systems, 19 (1), 136-151, 2011.
47. Boran, F. E., Akay, D., A generic method for the evaluation of interval type-2 fuzzy linguistic summaries, IEEE Trans Cybern, 44 (9), 1632-1645, 2014.
48. Delgado, M., Sánchez, D., Vila, M. A., Fuzzy cardinality based evaluation of quantified sentences, International Journal of Approximate Reasoning, 23 (1), 23-66, 2000.
49. Boran, F. E., Akay, D., Yager, R. R., A probabilistic framework for interval type-2 fuzzy linguistic summarization, IEEE Transactions on Fuzzy Systems, 22 (6), 1640-1653, 2014.
50. Özdoğan, İ., Boran, F. E., Akay, D., A possibilistic approach for interval type-2 fuzzy linguistic summarization of time series, Artificial Intelligence Review 2021 54:5, 54 (5), 3991-4018, 2021.
51. Klir, G. J., Yuan, Bo., Fuzzy sets and fuzzy logic : Theory and applications. Prentice Hall PTR, 1995. 52. Mendel, J. M., Uncertain Rule-Based Fuzzy Systems. Springer International Publishing, Cham, 2017. 53. Ross, T. J., Fuzzy logic with engineering applications. John Wiley, 2010.
54. Mendel, J. M., John, R. I., Liu, F., Interval type-2 fuzzy logic systems made simple, IEEE Transactions on Fuzzy Systems, 14, 808-821, 2006.
55. Hwang, C., Rhee, F. C. H., Uncertain fuzzy clustering: Interval type-2 fuzzy approach to C-means, IEEE Transactions on Fuzzy Systems, 15 (1), 107-120, 2007.
56. Wang, J., Kim, J., Predicting stock price trend using MACD optimized by historical volatility, Math Probl Eng, 2018, 2018.
57. Xie, X. L., Beni, G., A validity measure for fuzzy clustering, IEEE Trans Pattern Anal Mach Intell, 13 (8), 841-847, 1991.
58. Boran, F. E., Akay, D., Yager, R. R., An overview of methods for linguistic summarization with fuzzy sets, Expert Syst Appl, 61, 356-377, 2016.
59. Hu, C., Hu, Z. H., On Statistics, Probability, and Entropy of Interval-Valued Datasets, Communications in Computer and Information Science, 1239 CCIS, 407-421, 2020.

Toplam 57 adet kaynakça vardır.

Ayrıntılar

Birincil Dil	Türkçe
Konular	Mühendislik
Bölüm	Makaleler
Yazarlar	İlyas Özdoğan 0000-0003-0426-1833 Fatih Emre Boran 0000-0001-8404-3814 Oktay Yıldız 0000-0001-9155-7426
Erken Görünüm Tarihi	13 Mayıs 2025
Yayımlanma Tarihi
Gönderilme Tarihi	11 Mart 2023
Kabul Tarihi	16 Ocak 2025
Yayımlandığı Sayı	Yıl 2025 Cilt: 40 Sayı: 3

Kaynak Göster

APA	Özdoğan, İ., Boran, F. E., & Yıldız, O. (2025). Aralıklı tip-2 bulanık c-ortalama ile zaman serilerinin bulanık dilsel özetlemesi: BIST100 örnek hisse uygulaması. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 40(3), 1659-1672. https://doi.org/10.17341/gazimmfd.1263678
AMA	Özdoğan İ, Boran FE, Yıldız O. Aralıklı tip-2 bulanık c-ortalama ile zaman serilerinin bulanık dilsel özetlemesi: BIST100 örnek hisse uygulaması. GUMMFD. Mayıs 2025;40(3):1659-1672. doi:10.17341/gazimmfd.1263678
Chicago	Özdoğan, İlyas, Fatih Emre Boran, ve Oktay Yıldız. “Aralıklı Tip-2 bulanık C-Ortalama Ile Zaman Serilerinin bulanık Dilsel özetlemesi: BIST100 örnek Hisse Uygulaması”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 40, sy. 3 (Mayıs 2025): 1659-72. https://doi.org/10.17341/gazimmfd.1263678.
EndNote	Özdoğan İ, Boran FE, Yıldız O (01 Mayıs 2025) Aralıklı tip-2 bulanık c-ortalama ile zaman serilerinin bulanık dilsel özetlemesi: BIST100 örnek hisse uygulaması. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 40 3 1659–1672.
IEEE	İ. Özdoğan, F. E. Boran, ve O. Yıldız, “Aralıklı tip-2 bulanık c-ortalama ile zaman serilerinin bulanık dilsel özetlemesi: BIST100 örnek hisse uygulaması”, GUMMFD, c. 40, sy. 3, ss. 1659–1672, 2025, doi: 10.17341/gazimmfd.1263678.
ISNAD	Özdoğan, İlyas vd. “Aralıklı Tip-2 bulanık C-Ortalama Ile Zaman Serilerinin bulanık Dilsel özetlemesi: BIST100 örnek Hisse Uygulaması”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 40/3 (Mayıs 2025), 1659-1672. https://doi.org/10.17341/gazimmfd.1263678.
JAMA	Özdoğan İ, Boran FE, Yıldız O. Aralıklı tip-2 bulanık c-ortalama ile zaman serilerinin bulanık dilsel özetlemesi: BIST100 örnek hisse uygulaması. GUMMFD. 2025;40:1659–1672.
MLA	Özdoğan, İlyas vd. “Aralıklı Tip-2 bulanık C-Ortalama Ile Zaman Serilerinin bulanık Dilsel özetlemesi: BIST100 örnek Hisse Uygulaması”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, c. 40, sy. 3, 2025, ss. 1659-72, doi:10.17341/gazimmfd.1263678.
Vancouver	Özdoğan İ, Boran FE, Yıldız O. Aralıklı tip-2 bulanık c-ortalama ile zaman serilerinin bulanık dilsel özetlemesi: BIST100 örnek hisse uygulaması. GUMMFD. 2025;40(3):1659-72.

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