Yazar "Aladag, Cagdas Hakan" için listeleme
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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 ... -
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 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 ... -
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 ... -
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 ... -
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 Architecture Selection Strategy in Solving Seasonal Autoregressive Time Series By Artificial Neural Networks
Aladag, Cagdas Hakan; Egrioglu, Erol; Gunay, Suleyman (Hacettepe Univ, Fac Sci, 2008)The only suggestions given in the literature for determining the architecture of neural networks are based on observations, and a simulation study to determine the architecture has not yet been reported. Based on the results ... -
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 model selection strategy in artificial neural networks
Egrioglu, Erol; Aladag, Cagdas Hakan; Gunay, Suleyman (Elsevier Science Inc, 2008)In recent years, artificial neural networks have been used for time series forecasting. Determining architecture of artificial neural networks is very important problem in the applications. In this study, the problem in ... -
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. ... -
A New Seasonal Fuzzy Time Series Method Based on the Multiplicative Neuron Model and Sarima
Aladag, Sibel; Aladag, Cagdas Hakan; Mentes, Turhan; Egrioglu, Erol (Hacettepe Univ, Fac Sci, 2012)When fuzzy time series include a seasonal component, conventional fuzzy time series models are not sufficient. For such fuzzy time series, lagged variables which are around the period of the time series should also be ... -
A new time invariant fuzzy time series forecasting method based on particle swarm optimization
Aladag, Cagdas Hakan; Yolcu, Ufuk; Egrioglu, Erol; Dalar, Ali Z. (Elsevier Science Bv, 2012)In the analysis of time invariant fuzzy time series, fuzzy logic group relationships tables have been generally preferred for determination of fuzzy logic relationships. The reason of this is that it is not need to perform ... -
Recurrent Multiplicative Neuron Model Artificial Neural Network for Non-linear Time Series Forecasting
Egrioglu, Erol; Yolcu, Ufuk; Aladag, Cagdas Hakan; Bas, Eren (Springer, 2015)Artificial neural networks (ANN) have been widely used in recent years to model non-linear time series since ANN approach is a responsive method and does not require some assumptions such as normality or linearity. An ...
