2021

  • Çakmak, B., Doğru, F. Z. 2021. Optimal B-robust estimators for the parameters of the power Lindley distribution. Journal of Applied Statistics, 48(13-15), 2369-2388.
  • Doğru, F. Z., Arslan, O. 2021. Robust mixture regression modeling based on the Generalized M (GM)-estimation method. Communications in Statistics-Simulation and Computation, 50(9), 2643-2665.
  • Doğru, F. Z., Arslan, O. 2021. Finite mixtures of skew Laplace normal distributions with random skewness. Computational Statistics, 36, 423-447.
  • Bulut, Y.M., Doğru, F.Z.,  Arslan, O. 2021. Alpha power Lomax distribution: properties and application, Journal of Reliability and Statistical Studies, 14(1), 17-32.
  • Doğru, F.Z., Bulut, Y.M., Arslan, O. 2021. Finite mixtures of multivariate skew Laplace distributions, REVSTAT-Statistical Journal, 19(1), 35-46.
  • Yaman, I., Dalkılıç, T. E. (2021). A hybrid approach to cardinality constraint portfolio selection problem based on nonlinear neural network and genetic algorithm. Expert Systems with Applications, 169, 114517.
  • Bas E., Egrioglu E., U Yolcu, Bootstrapped Holt Method with Autoregressive Coefficients Based on Harmony Search Algorithm, 2021, Forecasting 3 (4), 839-850. (ESCI).
  • Bas E., Yolcu U., Egrioglu E., Intuitionistic fuzzy time series functions approach for time series forecasting, Granular Computing, 2021, 6(3), pp. 619–629. (ESCI).
  • Kocak, C; Egrioglu, EBas, E, A new deep intuitionistic fuzzy time series forecasting method based on long short-term memory, Journal of Supercomputing, 2021, 77(6), pp. 6178–6196, (SCI)
  • Chen, M.-Y., Lughofer, E.D., Egrioglu, E., Cloud & fog computing: intelligent applications, Enterprise Information Systems, 2021, 15(9), pp. 1197–1199. (SCI)
  • Tak N., Egrioglu E.Bas E., Yolcu U., An adaptive forecast combination approach based on meta intuitionistic fuzzy functions, Journal of Intelligent & Fuzzy Systems, 2021, 40(5), pp. 9567–9581. (SCIE)
  • Yildirim, Asiye Nur; Bas, ErenEgrioglu, Erol, Threshold single multiplicative neuron artificial neural networks for non-linear time series forecasting, Journal of Applied Statistics, 2021, 48(13-15), pp. 2809–2825. (SCIE).
  • Yolcu, U; Egrioglu, EBas, E; Yolcu, OC; Dalar, AZ, Probabilistic forecasting, linearity and nonlinearity hypothesis tests with bootstrapped linear and nonlinear artificial neural network, Journal of Experimental & Theoretical Artificial Intelligence, 2021, 33(3), pp. 383–404.(SCIE)

Basım Aşamasında / Erken Görünüm
  • Yilmaz O., Bas E.Egrioglu E., The Training of Pi-Sigma Artificial Neural Networks with Differential Evolution Algorithm for Forecasting, Computational Economics, Early Access, https://doi.org/10.1007/s10614-020-10086-2. (SSCI).
  • Bas, E.Egrioglu, E., Karahasan, O., A Pi-Sigma artificial neural network based on sine cosine optimization algorithm, Granular Computing, 2021, (Article in Press), 10.1007/s41066-021-00297-9. (ESCI)
  • Bas, E.Egrioglu, E., Tunc, T., Multivariate Picture Fuzzy Time Series: New Definitions and a New Forecasting Method Based on Pi-Sigma Artificial Neural Network, Computational Economics, 2021, (Article in Press), 10.1007/s10614-021-10202-w.(SSCI)
  • Kocak, C., Egrioglu, E.Bas, E., A new explainable robust high-order intuitionistic fuzzy time-series method, Soft Computing, 2021, (Article in Press), 10.1007/s00500-021-06079-4. (SCI)
  • Bas, E.Egrioglu, E., Kolemen, E., Training simple recurrent deep artificial neural network for forecasting using particle swarm optimization, Granular Computing, 2021, (Article in Press), 10.1007/s41066-021-00274-2. (ESCI)
  • Egrioglu, E; Fildes, R, A New Bootstrapped Hybrid Artificial Neural Network Approach for Time Series Forecasting, Computational Economics, Early Access, 10.1007/s10614-020-10073-7. (SCI).
  • Bas, EEgrioglu, E; Yolcu, U, A hybrid algorithm based on artificial bat and backpropagation algorithms for multiplicative neuron model artificial neural networks, Journal Of Ambient Intelligence And Humanized Computing, Early Access, 10.1007/s12652-020-01950-y. (SCI).
  • Egrioglu, E; Fildes, R, Bas E., Recurrent fuzzy time series functions approaches for forecasting, Granular Computing, Early Access, https://doi.org/10.1007/s41066-021-00257-3. (ESCI).