Yazar "Yolcu, Ufuk" için listeleme
-
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 ... -
Bulanık zaman serilerinde çok değişkenli çözümleme / Ufuk Yolcu ; danışman Vedide Rezan Uslu
Yolcu, Ufuk (Ondokuz Mayıs Üniversitesi, Fen Bilimleri Enstitüsü, 2011)… -
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, ... -
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 ... -
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 modified genetic algorithm for forecasting fuzzy time series
Bas, Eren; Uslu, Vedide Rezan; Yolcu, Ufuk; Egrioglu, Erol (Springer, 2014)Fuzzy time series approaches are used when observations of time series contain uncertainty. Moreover, these approaches do not require the assumptions needed for traditional time series approaches. Generally, fuzzy time ... -
A New Approach Based on Artificial Neural Networks for High Order Bivariate Fuzzy Time Series
Egrioglu, Erol; Uslu, V. Rezan; Yolcu, Ufuk; Basaran, M. A.; Hakan, Aladag C. (Springer, 2009)When observations of time series are defined linguistically or do not follow the assumptions required for time series theory, the classical methods of time series analysis do not cope with fuzzy numbers and assumption ... -
A new approach based on artificial neural networks for high order multivariate fuzzy time series
Egrioglu, Erol; Aladag, Cagdas Hakan; Yolcu, Ufuk; Uslu, Vedide R.; Basaran, Murat A. (Pergamon-Elsevier Science Ltd, 2009)Fuzzy time series methods have been recently becoming very popular in forecasting. These methods can be categorized into two subclasses that are univariate and multivariate approaches. It is a known fact that real time ... -
A new approach based on the optimization of the length of intervals in fuzzy time series
Egrioglu, Erol; Aladag, Cagdas Hakan; Basaran, Murat A.; Yolcu, Ufuk; Uslu, Vedide R. (Ios Press, 2011)In fuzzy time series analysis, the determination of the interval length is an important issue. In many researches recently done, the length of intervals has been intuitively determined. In order to efficiently determine ... -
A new approach for determining the length of intervals for fuzzy time series
Yolcu, Ufuk; Egrioglu, Erol; Uslu, Vedide R.; Basaran, Murat A.; Aladag, Cagdas H. (Elsevier, 2009)In the implementations of fuzzy time series forecasting, the identification of interval lengths has an important impact on the performance of the procedure. However, the interval length has been chosen arbitrarily in many ... -
A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model
Egrioglu, Erol; Aladag, Cagdas Hakan; Yolcu, Ufuk; Basaran, Murat A.; Uslu, Vedide R. (Pergamon-Elsevier Science Ltd, 2009)In the literature, there have been many studies using fuzzy time series for the purpose of forecasting. The most studied model is the first order fuzzy time series model. In this model, an observation of fuzzy time series ... -
A new linear & nonlinear artificial neural network model for time series forecasting
Yolcu, Ufuk; Egrioglu, Erol; Aladag, Cagdas H. (Elsevier Science Bv, 2013)Artificial neural network approach is a well-known method that is a useful tool for time series forecasting. Since real life time series can generally contain both linear and nonlinear components, hybrid approaches which ... -
A New Multiplicative Seasonal Neural Network Model Based on Particle Swarm Optimization
Aladag, Cagdas Hakan; Yolcu, Ufuk; Egrioglu, Erol (Springer, 2013)In recent years, artificial neural networks (ANNs) have been commonly used for time series forecasting by researchers from various fields. There are some types of ANNs and feed forward neural networks model is one of them. ...
