Financial Series Forecasting: A New Fuzzy Inference System for Crisp Values and Interval-Valued Predictions
Author:
Funder
CNPq
CAPES
FAPEMIG
BIOS
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10614-024-10670-w.pdf
Reference106 articles.
1. Alves, K. S. T. R., & de Aguiar, E. P. (2021). A novel rule-based evolving fuzzy system applied to the thermal modeling of power transformers. Applied Soft Computing, 112, 107764. https://doi.org/10.1016/j.asoc.2021.107764
2. Angelov, P., & Filev, D. (2005). Simpl_ets: A simplified method for learning evolving takagi-sugeno fuzzy models. In: The 14th IEEE international conference on fuzzy systems, 2005. FUZZ’05., IEEE, (pp. 1068–1073), https://doi.org/10.1109/FUZZY.2005.1452543.
3. Angelov, P., & Zhou, X. (2006). Evolving fuzzy systems from data streams in real-time. In: 2006 International symposium on evolving fuzzy systems, IEEE, (pp. 29–35), https://doi.org/10.1109/ISEFS.2006.251157.
4. Angelov, P. P., & Filev, D. P. (2004). An approach to online identification of takagi-sugeno fuzzy models. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34(1), 484–498. https://doi.org/10.1109/TSMCB.2003.817053
5. Anggoro, D. A., & Kurnia, N. D. (2020). Comparison of accuracy level of support vector machine (svm) and k-nearest neighbors (knn) algorithms in predicting heart disease. International Journal, 8(5), 1689–1694. https://doi.org/10.30534/ijeter/2020/32852020
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