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Erol Egrioglu

Erol Egrioglu

D-Index & Metrics

Computer Science

D-Index
38
Citations
4879
World Ranking
10388
National Ranking
16

Overview

Erol Egrioglu is affiliated with Giresun University in Turkey and has contributed extensively to research in the fields of decision sciences, computer science, and engineering. Their work spans numerous subfields, including management science and operations research, artificial intelligence, electrical and electronic engineering, statistics and probability, and economics and econometrics.

Their research has addressed a range of topics, primarily focusing on forecasting methods and neural network applications. Key areas of interest include stock market forecasting methods, energy load and power forecasting, neural networks and applications, forecasting techniques and applications, fuzzy logic and control systems, fuzzy systems and optimization, and metaheuristic optimization algorithms research.

Egrioglu's publication record includes papers in various academic venues. Frequent outlets for their research include:

  • Granular Computing
  • SSRN Electronic Journal
  • Computational Economics
  • Information Sciences
  • Library Hi Tech

Notable recent publications include:

  • Recurrent dendritic neuron model artificial neural network for time series forecasting (2022), published in Information Sciences
  • Recurrent fuzzy time series functions approaches for forecasting (2021), published in Granular Computing
  • Robust training of median dendritic artificial neural networks for time series forecasting (2023), published in Expert Systems with Applications
  • Training simple recurrent deep artificial neural network for forecasting using particle swarm optimization (2021), published in Granular Computing
  • Intuitionistic fuzzy time series functions approach for time series forecasting (2020), published in Granular Computing

Egrioglu frequently collaborates with other researchers in the field, including Eren Baş, Mu-Yen Chen, Ufuk Yolcu, Edwin Lughofer, and Turan Cansu. These collaborations likely reflect interdisciplinary approaches and joint investigation into forecasting techniques and artificial intelligence.

Best Publications

  • Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations

    Cagdas H. Aladag;Murat A. Basaran;Erol Egrioglu;Ufuk Yolcu

  • Forecasting nonlinear time series with a hybrid methodology

    Cagdas Hakan Aladag;Erol Egrioglu;Cem Kadilar

  • Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks

    Erol Egrioglu;Cagdas Hakan Aladag;Ufuk Yolcu

  • A new linear & nonlinear artificial neural network model for time series forecasting

    Ufuk Yolcu;Erol Egrioglu;Cagdas H. Aladag

  • A new approach for determining the length of intervals for fuzzy time series

    Ufuk Yolcu;Erol Egrioglu;Vedide R. Uslu;Murat A. Basaran

  • A new time invariant fuzzy time series forecasting method based on particle swarm optimization

    Cagdas Hakan Aladag;Ufuk Yolcu;Erol Egrioglu;Ali Z. Dalar

  • A new approach based on artificial neural networks for high order multivariate fuzzy time series

    Erol Egrioglu;Cagdas Hakan Aladag;Ufuk Yolcu;Vedide R. Uslu

  • Finding an optimal interval length in high order fuzzy time series

    Erol Egrioglu;Cagdas Hakan Aladag;Ufuk Yolcu;Vedide R. Uslu

  • Recurrent Multiplicative Neuron Model Artificial Neural Network for Non-linear Time Series Forecasting

    Erol Egrioglu;Ufuk Yolcu;Cagdas Hakan Aladag;Eren Bas

  • Original articles: A high order fuzzy time series forecasting model based on adaptive expectation and artificial neural networks

    Cagdas Hakan Aladag;Ufuk Yolcu;Erol Egrioglu

  • A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model

    Erol Egrioglu;Cagdas Hakan Aladag;Ufuk Yolcu;Murat A. Basaran

  • A new approach based on the optimization of the length of intervals in fuzzy time series

    Erol Egrioglu;Cagdas Hakan Aladag;Murat A. Basaran;Ufuk Yolcu

  • A new model selection strategy in artificial neural networks

    Erol Eğrioğlu;Çağdaş Hakan Aladağ;Süleyman Günay

  • Fuzzy time series forecasting method based on Gustafson-Kessel fuzzy clustering

    E. Egrioglu;C. H. Aladag;U. Yolcu;V. R. Uslu

  • A new hybrid method for time series forecasting: AR–ANFIS

    Unknown

  • A modified genetic algorithm for forecasting fuzzy time series

    Eren Bas;Vedide Rezan Uslu;Ufuk Yolcu;Erol Egrioglu

  • Training simple recurrent deep artificial neural network for forecasting using particle swarm optimization

    Unknown

  • Determining the most proper number of cluster in fuzzy clustering by using artificial neural networks

    N. Alp Erilli;Ufuk Yolcu;Erol Eğrioğlu;Ç. Hakan Aladağ

  • High order fuzzy time series method based on pi-sigma neural network

    Eren Bas;Eren Bas;Eren Bas;Crina Grosan;Crina Grosan;Erol Egrioglu;Ufuk Yolcu

  • Comparison of intraoral radiography and cone-beam computed tomography for the detection of horizontal root fractures: an in vitro study

    Hakan Avsever;Kaan Gunduz;Kaan Orhan;Ismail Uzun

  • Time-series forecasting with a novel fuzzy time-series approach: an example for Istanbul stock market

    Ufuk Yolcu;Cagdas Hakan Aladag;Erol Egrioglu;Vedide R. Uslu

  • High order fuzzy time series forecasting method based on an intersection operation

    Ozge Cagcag Yolcu;Ufuk Yolcu;Erol Egrioglu;C. Hakan Aladag

Frequent Co-Authors

Crina Grosan
Crina Grosan King's College London
Edwin Lughofer
Edwin Lughofer Johannes Kepler University of Linz
Yaochu Jin
Yaochu Jin Westlake University

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