| Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations CH Aladag, MA Basaran, E Egrioglu, U Yolcu, VR Uslu Expert Systems with Applications 36 (3), 4228-4231, 2009 | 232 | 2009 |
| Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks E Egrioglu, CH Aladag, U Yolcu Expert Systems with Applications 40 (3), 854-857, 2013 | 186 | 2013 |
| A new linear & nonlinear artificial neural network model for time series forecasting U Yolcu, E Egrioglu, CH Aladag Decision support systems 54 (3), 1340-1347, 2013 | 165 | 2013 |
| A new approach for determining the length of intervals for fuzzy time series U Yolcu, E Egrioglu, VR Uslu, MA Basaran, CH Aladag Applied Soft Computing 9 (2), 647-651, 2009 | 162 | 2009 |
| A new time invariant fuzzy time series forecasting method based on particle swarm optimization CH Aladag, U Yolcu, E Egrioglu, AZ Dalar Applied Soft Computing 12 (10), 3291-3299, 2012 | 152 | 2012 |
| A new approach based on artificial neural networks for high order multivariate fuzzy time series E Egrioglu, CH Aladag, U Yolcu, VR Uslu, MA Basaran Expert Systems with Applications 36 (7), 10589-10594, 2009 | 145 | 2009 |
| Finding an optimal interval length in high order fuzzy time series E Egrioglu, CH Aladag, U Yolcu, VR Uslu, MA Basaran Expert Systems with Applications 37 (7), 5052-5055, 2010 | 142 | 2010 |
| A new approach based on the optimization of the length of intervals in fuzzy time series E Egrioglu, CH Aladag, MA Basaran, U Yolcu, VR Uslu Journal of Intelligent & Fuzzy Systems 22 (1), 15-19, 2011 | 123 | 2011 |
| A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model E Egrioglu, CH Aladag, U Yolcu, MA Basaran, VR Uslu Expert Systems with Applications 36 (4), 7424-7434, 2009 | 120 | 2009 |
| Fuzzy time series forecasting method based on Gustafson–Kessel fuzzy clustering E Egrioglu, CH Aladag, U Yolcu, VR Uslu, NA Erilli Expert Systems with Applications 38 (8), 10355-10357, 2011 | 114 | 2011 |
| A high order fuzzy time series forecasting model based on adaptive expectation and artificial neural networks CH Aladag, U Yolcu, E Egrioglu Mathematics and Computers in Simulation 81 (4), 875-882, 2010 | 114 | 2010 |
| Recurrent multiplicative neuron model artificial neural network for non-linear time series forecasting E Egrioglu, U Yolcu, CH Aladag, E Bas Neural Processing Letters 41, 249-258, 2015 | 104 | 2015 |
| A modified genetic algorithm for forecasting fuzzy time series E Bas, VR Uslu, U Yolcu, E Egrioglu Applied intelligence 41, 453-463, 2014 | 85 | 2014 |
| A fuzzy time series approach based on weights determined by the number of recurrences of fuzzy relations VR Uslu, E Bas, U Yolcu, E Egrioglu Swarm and Evolutionary Computation 15, 19-26, 2014 | 69 | 2014 |
| Determining the most proper number of cluster in fuzzy clustering by using artificial neural networks NA Erilli, U Yolcu, E Eğrioğlu, ÇH Aladağ, Y Öner Expert Systems with Applications 38 (3), 2248-2252, 2011 | 68 | 2011 |
| High order fuzzy time series method based on pi-sigma neural network E Bas, C Grosan, E Egrioglu, U Yolcu Engineering Applications of Artificial Intelligence 72, 350-356, 2018 | 67 | 2018 |
| Intuitionistic fuzzy time series functions approach for time series forecasting E Bas, U Yolcu, E Egrioglu Granular Computing 6 (3), 619-629, 2021 | 65 | 2021 |
| Time-series forecasting with a novel fuzzy time-series approach: an example for Istanbul stock market U Yolcu, CH Aladag, E Egrioglu, VR Uslu Journal of Statistical Computation and Simulation 83 (4), 599-612, 2013 | 65 | 2013 |
| Fuzzy-time-series network used to forecast linear and nonlinear time series E Bas, E Egrioglu, CH Aladag, U Yolcu Applied Intelligence 43, 343-355, 2015 | 61 | 2015 |
| High order fuzzy time series forecasting method based on an intersection operation OC Yolcu, U Yolcu, E Egrioglu, CH Aladag Applied Mathematical Modelling 40 (19-20), 8750-8765, 2016 | 57 | 2016 |
