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Ufuk Yolcu
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2020 – today
- 2024
- [j43]Ozge Cagcag Yolcu
, Ufuk Yolcu:
A new ensemble intuitionistic fuzzy-deep forecasting model: Consolidation of the IFRFs-bENR with LSTM. Inf. Sci. 679: 121007 (2024) - [j42]Ozge Cagcag Yolcu
Short-term load forecasting: cascade intuitionistic fuzzy time series - univariate and bivariate models. Neural Comput. Appl. 36(32): 20167-20192 (2024) - 2023
- [j41]Eren Bas, Erol Egrioglu, Ufuk Yolcu, Mu-Yen Chen
A Robust Learning Algorithm Based on Particle Swarm Optimization for Pi-Sigma Artificial Neural Networks. Big Data 11(2): 105-116 (2023) - [j40]Ozge Cagcag Yolcu
A novel intuitionistic fuzzy time series prediction model with cascaded structure for financial time series. Expert Syst. Appl. 215: 119336 (2023) - [j39]Ayse Yilmaz
A robust training of dendritic neuron model neural network for time series prediction. Neural Comput. Appl. 35(14): 10387-10406 (2023) - 2022
- [j38]Ozge Cagcag Yolcu
Multivariate intuitionistic fuzzy inference system for stock market prediction: The cases of Istanbul and Taiwan. Appl. Soft Comput. 116: 108363 (2022) - 2021
- [j37]Abdullah Yalçinkaya
Maximum likelihood and maximum product of spacings estimations for the parameters of skew-normal distribution under doubly type II censoring using genetic algorithm. Expert Syst. Appl. 168: 114407 (2021) - [j36]Ufuk Yolcu
Probabilistic forecasting, linearity and nonlinearity hypothesis tests with bootstrapped linear and nonlinear artificial neural network. J. Exp. Theor. Artif. Intell. 33(3): 383-404 (2021) - [j35]Nihat Tak
An adaptive forecast combination approach based on meta intuitionistic fuzzy functions. J. Intell. Fuzzy Syst. 40(5): 9567-9581 (2021) - 2020
- [j34]Erol Egrioglu
Picture fuzzy time series: Defining, modeling and creating a new forecasting method. Eng. Appl. Artif. Intell. 88 (2020) - [j33]Eren Bas
Picture fuzzy regression functions approach for financial time series based on ridge regression and genetic algorithm. J. Comput. Appl. Math. 370 (2020) - [j32]Ozge Cagcag Yolcu
A new intuitionistic fuzzy functions approach based on hesitation margin for time-series prediction. Soft Comput. 24(11): 8211-8222 (2020)
2010 – 2019
- 2019
- [j31]Erol Egrioglu
Intuitionistic time series fuzzy inference system. Eng. Appl. Artif. Intell. 82: 175-183 (2019) - [j30]Erol Egrioglu
Median-Pi artificial neural network for forecasting. Neural Comput. Appl. 31(1): 307-316 (2019) - 2018
- [j29]Eren Bas
High order fuzzy time series method based on pi-sigma neural network. Eng. Appl. Artif. Intell. 72: 350-356 (2018) - [j28]Esra Akdeniz
An ARMA Type Pi-Sigma Artificial Neural Network for Nonlinear Time Series Forecasting. J. Artif. Intell. Soft Comput. Res. 8(2): 121-132 (2018) - [j27]Ufuk Yolcu
A new fuzzy inference system for time series forecasting and obtaining the probabilistic forecasts via subsampling block bootstrap. J. Intell. Fuzzy Syst. 35(2): 2349-2358 (2018) - [j26]Ozge Cagcag Yolcu
Single Multiplicative Neuron Model Artificial Neural Network with Autoregressive Coefficient for Time Series Modelling. Neural Process. Lett. 47(3): 1133-1147 (2018) - [j25]Abdullah Yalçinkaya
Maximum likelihood estimation for the parameters of skew normal distribution using genetic algorithm. Swarm Evol. Comput. 38: 127-138 (2018) - 2016
- [j24]Çagdas Hakan Aladag
Type-1 fuzzy time series function method based on binary particle swarm optimisation. Int. J. Data Anal. Tech. Strateg. 8(1): 2-13 (2016) - [j23]Ozge Gundogdu, Erol Egrioglu
Multiplicative neuron model artificial neural network based on Gaussian activation function. Neural Comput. Appl. 27(4): 927-935 (2016) - [c1]Ufuk Yolcu
An ensemble of single multiplicative neuron models for probabilistic prediction. SSCI 2016: 1-8 - 2015
- [j22]Eren Bas
Fuzzy-time-series network used to forecast linear and nonlinear time series. Appl. Intell. 43(2): 343-355 (2015) - [j21]Erol Egrioglu
Recurrent Multiplicative Neuron Model Artificial Neural Network for Non-linear Time Series Forecasting. Neural Process. Lett. 41(2): 249-258 (2015) - 2014
- [j20]Eren Bas
A modified genetic algorithm for forecasting fuzzy time series. Appl. Intell. 41(2): 453-463 (2014) - [j19]Çagdas Hakan Aladag
Fuzzy lagged variable selection in fuzzy time series with genetic algorithms. Appl. Soft Comput. 22: 465-473 (2014) - [j18]Çagdas Hakan Aladag
A high order seasonal fuzzy time series model and application to international tourism demand of Turkey. J. Intell. Fuzzy Syst. 26(1): 295-302 (2014) - [j17]Ufuk Yolcu
An enhanced fuzzy time series forecasting method based on artificial bee colony. J. Intell. Fuzzy Syst. 26(6): 2627-2637 (2014) - [j16]Çagdas Hakan Aladag
Robust multilayer neural network based on median neuron model. Neural Comput. Appl. 24(3-4): 945-956 (2014) - [j15]Vedide Rezan Uslu, Eren Bas
A fuzzy time series approach based on weights determined by the number of recurrences of fuzzy relations. Swarm Evol. Comput. 15: 19-26 (2014) - 2013
- [j14]Ufuk Yolcu
A new linear & nonlinear artificial neural network model for time series forecasting. Decis. Support Syst. 54(3): 1340-1347 (2013) - [j13]Erol Egrioglu
Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks. Expert Syst. Appl. 40(3): 854-857 (2013) - [j12]Çagdas Hakan Aladag
A New Multiplicative Seasonal Neural Network Model Based on Particle Swarm Optimization. Neural Process. Lett. 37(3): 251-262 (2013) - 2012
- [j11]Çagdas Hakan Aladag
A new time invariant fuzzy time series forecasting method based on particle swarm optimization. Appl. Soft Comput. 12(10): 3291-3299 (2012) - 2011
- [j10]N. Alp Erilli, Ufuk Yolcu
Determining the most proper number of cluster in fuzzy clustering by using artificial neural networks. Expert Syst. Appl. 38(3): 2248-2252 (2011) - [j9]Erol Egrioglu
Fuzzy time series forecasting method based on Gustafson-Kessel fuzzy clustering. Expert Syst. Appl. 38(8): 10355-10357 (2011) - [j8]Erol Egrioglu
A new approach based on the optimization of the length of intervals in fuzzy time series. J. Intell. Fuzzy Syst. 22(1): 15-19 (2011) - 2010
- [j7]Erol Egrioglu
Finding an optimal interval length in high order fuzzy time series. Expert Syst. Appl. 37(7): 5052-5055 (2010) - [j6]Çagdas Hakan Aladag
A high order fuzzy time series forecasting model based on adaptive expectation and artificial neural networks. Math. Comput. Simul. 81(4): 875-882 (2010) - [j5]Çagdas Hakan Aladag
Forecast Combination by Using Artificial Neural Networks. Neural Process. Lett. 32(3): 269-276 (2010)
2000 – 2009
- 2009
- [j4]Ufuk Yolcu
A new approach for determining the length of intervals for fuzzy time series. Appl. Soft Comput. 9(2): 647-651 (2009) - [j3]Çagdas Hakan Aladag
Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations. Expert Syst. Appl. 36(3): 4228-4231 (2009) - [j2]Erol Egrioglu
A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model. Expert Syst. Appl. 36(4): 7424-7434 (2009) - [j1]Erol Egrioglu
A new approach based on artificial neural networks for high order multivariate fuzzy time series. Expert Syst. Appl. 36(7): 10589-10594 (2009)
Coauthor Index
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last updated on 2024-12-10 20:38 CET by the dblp team
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