Yazar "Egrioglu, Erol" için listeleme
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AR-ARCH Type Artificial Neural Network for Forecasting
Corba, Burcin Seyda; Egrioglu, Erol; Dalar, Ali Zafer (Springer, 2020)Real-world time series such as econometric time series are rarely linear and they have characteristics of volatility. Although autoregressive conditional heteroscedasticity models have used for forecasting financial time ... -
An ARMA Type Fuzzy Time Series Forecasting Method Based on Particle Swarm Optimization
Egrioglu, Erol; Yolcu, Ufuk; Aladag, Cagdas Hakan; Kocak, Cem (Hindawi Ltd, 2013)In the literature, fuzzy time series forecasting models generally include fuzzy lagged variables. Thus, these fuzzy time series models have only autoregressive structure. Using such fuzzy time series models can cause ... -
Bayesian model selection in ARFIMA models
Egrioglu, Erol; Guenay, Sueleyman (Pergamon-Elsevier Science Ltd, 2010)Various model selection criteria such as Akaike information criterion (AIC; Akaike, 1973), Bayesian information criterion (BIC; Akaike, 1979) and Hannan-Quinn criterion (HQC; Hannan, 1980) are used for model specification ... -
Comparing the Effect of Different Voxel Resolutions for Assessment of Vertical Root Fracture of Permanent Teeth
Uzun, Ismail; Gunduz, Kaan; Celenk, Peruze; Avsever, Hakan; Orhan, Kaan; Canitezer, Gozde; Egrioglu, Erol (Kowsar Publ, 2015)Background: The teeth with undiagnosed VRFs are likely to receive endodontic treatment or retreatment, leading to frustration and inappropriate endodontic therapies. Moreover, many cases of VRFs cannot be diagnosed ... -
Comparison of intraoral radiography and cone-beam computed tomography for the detection of horizontal root fractures: an in vitro study
Avsever, Hakan; Gunduz, Kaan; Orhan, Kaan; Uzun, Ismail; Ozmen, Bilal; Egrioglu, Erol; Midilli, Muhammed (Springer Heidelberg, 2014)This study aimed to compare the diagnostic accuracy of two different cone-beam computed tomography (CBCT) units with several intraoral radiography techniques for detecting horizontal root fractures. The study material ... -
Determining the most proper number of cluster in fuzzy clustering by using artificial neural networks
Erilli, N. Alp; Yolcu, Ufuk; Egrioglu, Erol; Aladag, C. Hakan; Oner, Yuksel (Pergamon-Elsevier Science Ltd, 2011)In a clustering problem, it would be better to use fuzzy clustering if there was an uncertainty in determining clusters or memberships of some units. Determining the number of cluster has an important role on obtaining ... -
An enhanced fuzzy time series forecasting method based on artificial bee colony
Yolcu, Ufuk; Cagcag, Ozge; Aladag, Cagdas Hakan; Egrioglu, Erol (Ios Press, 2014)In recent years, several forecasting methods have been proposed for the analysis of fuzzy time series. Determination of fuzzy relations and establishing interval lengths, which is used in partition of universe of discourse, ... -
Evaluation of the Prevalence of Bifid Mandibular Condyle Detected on Cone Beam Computed Tomography Images in a Turkish Population
Gunduz, Kaan; Buyuk, Cansu; Egrioglu, Erol (Soc Chilena Anatomia, 2015)The aim of this study was to assess the frequency of the BMC phenomenon in a Turkish patient population. Cone beam computed tomography (CBCT) images of 2634 consecutive patients were retrospectively reviewed. The Chi-squared ... -
Finding an optimal interval length in high order fuzzy time series
Egrioglu, Erol; Aladag, Cagdas Hakan; Yolcu, Ufuk; Uslu, Vedide R.; Basaran, Murat A. (Pergamon-Elsevier Science Ltd, 2010)Univariate fuzzy time series approaches which have been widely used in recent years can be divided into two classes, which are called first order and high order models. In the literature, it has been shown that high order ... -
Forecast Combination by Using Artificial Neural Networks
Aladag, Cagdas Hakan; Egrioglu, Erol; Yolcu, Ufuk (Springer, 2010)One of the efficient ways for obtaining accurate forecasts is usage of forecast combination method. This approach consists of combining different forecast values obtained from different forecasting models. Also artificial ... -
Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations
Aladag, Cagdas H.; Basaran, Murat A.; Egrioglu, Erol; Yolcu, Ufuk; Uslu, Vedide R. (Pergamon-Elsevier Science Ltd, 2009)A given observation in time series does not only depend on preceding one but also previous ones in general. Therefore, high order fuzzy time series approach might obtain better forecasts than does first order fuzzy time ... -
Forecasting nonlinear time series with a hybrid methodology
Aladag, Cagdas Hakan; Egrioglu, Erol; Kadilar, Cem (Pergamon-Elsevier Science Ltd, 2009)In recent years, artificial neural networks (ANNs) have been used for forecasting in time series in the literature. Although it is possible to model both linear and nonlinear structures in time series by using ANNs, they ... -
Fuzzy lagged variable selection in fuzzy time series with genetic algorithms
Aladag, Cagdas Hakan; Yolcu, Ufuk; Egrioglu, Erol; Bas, Eren (Elsevier, 2014)Fuzzy time series forecasting models can be divided into two subclasses which are first order and high order. In high order models, all lagged variables exist in the model according to the model order. Thus, some of these ... -
A fuzzy time series approach based on weights determined by the number of recurrences of fuzzy relations
Uslu, Vedide Rezan; Bas, Eren; Yolcu, Ufuk; Egrioglu, Erol (Elsevier, 2014)Fuzzy time series approaches, which do not require the strict assumptions of traditional time series approaches, generally consist of three stages. These are called as the fuzzification of crisp time series observations, ... -
Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks
Egrioglu, Erol; Aladag, Cagdas Hakan; Yolcu, Ufuk (Pergamon-Elsevier Science Ltd, 2013)In recent years, time series forecasting studies in which fuzzy time series approach is utilized have got more attentions. Various soft computing techniques such as fuzzy clustering, artificial neural networks and genetic ... -
High order fuzzy time series forecasting method based on an intersection operation
Yolcu, Ozge Cagcag; Yolcu, Ufuk; Egrioglu, Erol; Aladag, C. Hakan (Elsevier Science Inc, 2016)The use of non-stochastic models such as fuzzy time series forecasting models for time series analysis has attracted the attention of researchers in recent years. Fuzzy time series forecasting models do not need strict ... -
A high order fuzzy time series forecasting model based on adaptive expectation and artificial neural networks
Aladag, Cagdas Hakan; Yolcu, Ufuk; Egrioglu, Erol (Elsevier, 2010)Many fuzzy time series approaches have been proposed in recent years. These methods include three main phases such as fuzzification. defining fuzzy relationships and, defuzzification. Aladag et al. [2] improved the forecasting ... -
A high order seasonal fuzzy time series model and application to international tourism demand of Turkey
Aladag, Cagdas Hakan; Egrioglu, Erol; Yolcu, Ufufk; Uslu, Vedide R. (Ios Press, 2014)There have been many recently proposed methods for forecasting fuzzy time series. Most of them are, however, for non-seasonal fuzzy time series. A definition of seasonal fuzzy time series was firstly given by Song (Q. Song, ... -
Improving weighted information criterion by using optimization
Aladag, Cagdas Hakan; Egrioglu, Erol; Gunay, Suleyman; Basaran, Murat A. (Elsevier Science Bv, 2010)Although artificial neural networks (ANN) have been widely used in forecasting time series, the determination of the best model is still a problem that has been studied a lot. Various approaches available in the literature ... -
Modeling Brain Wave Data By Using Artificial Neural Networks
Aladag, Cagdas Hakan; Egrioglu, Erol; Kadilar, Cem (Hacettepe Univ, Fac Sci, 2010)Artificial neural networks can successfully model time series in real life. Because of their success, they have been widely used in various fields of application. In this paper, artificial neural networks are used to model ...
