# Cagdas Hakan AladagHacettepe University · Department of Statistics

Cagdas Hakan Aladag

Professor of Statistics

## About

99

Publications

18,672

Reads

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2,550

Citations

Citations since 2017

Introduction

Additional affiliations

September 2013 - present

July 2009 - September 2013

## Publications

Publications (99)

Determination of the parameters of metaheuristic methods, such as particle swarm optimization (PSO) is a vital issue. Determining the optimal values of these parameters is a very difficult task and sometimes impossible so we focus on identifying the most important PSO parameters. For this purpose, statistical analysis of the relationship between th...

The effect of overburden stress on the rock mass deformation modulus is a known issue. However, the effect of overburden stress has been studied less with empirical methods due to the lack of appropriate data. In this study, it is aimed to investigate the effect of overburden stress on rock mass deformation modulus using artificial neural network (...

One of the biggest problems in using artificial neural networks is to determine the best architecture. This is a crucial problem since there are no general rules to select the best architecture structure. Selection of the best architecture is to determine how many neurons should be used in the layers of a network. It is a well-known fact that using...

Purpose
Double auctions are widely used market mechanisms on the world. Communication technologies such as internet increased importance of this market institution. The purpose of this study is to develop novel bidding strategies for dynamic double auction markets, explain price formation through interactions of buyers and sellers in decentralized...

Three-parameter Weibull is one of the most popular and most widely-used distribution in many fields of science. Therefore, many studies have been conducted concerning the statistical inferences of the parameters of Weibull distribution. In general, the maximum likelihood (ML) methodology is used in the estimation process of unknown parameters. In t...

This volume is a valuable source of recent knowledge about advanced time series forecasting techniques such as artificial neural networks, fuzzy time series, or hybrid approaches. New forecasting frameworks are discussed and their application is demonstrated. The second volume of the series includes applications of some powerful forecasting approac...

Fuzzy time series approach has been widely used to analyze real-world time series in recent years since using this approach has some important advantages. Various fuzzy time series models have been proposed in the literature in order to reach better forecasting results. A few of these models have been suggested to forecast seasonal time series and...

Feed Forward Neural Networks (FFNN) are widely used in time series forecasting and produces satisfactory results for various real world applications. Many researches have showed that FFNN models also provide their success on forecasting exchange rate time series. In this study, various FFNN models are utilized to forecast EURO/USD exchange rate tim...

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 assumptions, whereas conventional stochastic models need to satisfy some assumptions. In addition, fuzzy time series methods can be...

Non-probabilistic forecasting methods are commonly used in various scientific fields. Fuzzy-time-series methods are well-known non-probabilistic and nonlinear forecasting methods. Although these methods can produce accurate forecasts, linear autoregressive models can produce forecasts that are more accurate than those produced by fuzzy-time-series...

Multiplicative neuron model-based artificial neural networks are one of the artificial neural network types which have been proposed recently and have produced successful forecasting results. Sigmoid activation function was used in multiplicative neuron model-based artificial neural networks in the previous studies. Although artificial neural netwo...

Various models which are based on fuzzy systems have been successfully used in many application areas including but not limited to forecasting, optimization, clustering, and modeling. An important issue is to evaluate the performance of these models. In general, a performance measure is calculated based on the difference between the defuzzified out...

The multilayer perceptron model has been suggested as an alternative to conventional approaches, and can accurately forecast time series. Additionally, several novel artificial neural network models have been proposed as alternatives to the multilayer perceptron model, which have used (for example) the generalized mean, geometric mean, and multipli...

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 can exist in the model although these lagged variables are not significant in explaining fuzzy relationships. If such lagged var...

In recent years, fuzzy time series have been drawn great attention due to their potential for use in time series forecasting. In many studies available in the literature, fuzzy time series have been successfully used to forecast time series contain some uncertainty. Studies on this method still continue to reach better forecasting results. Determin...

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 important problem with using ANN for time series forecasting is to determine the number of neurons in hidden layer. There have been...

Multilayer perceptron has been widely used in time series forecasting for last two decades. However, it is a well-known fact that the forecasting performance of multilayer perceptron is negatively affected when data have outliers and this is an important problem. In recent years, some alternative neuron models such as generalized-mean neuron, geome...

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, can be considered as the two of main elements affecting the forecasting performance of these forecasting methods. In the liter...

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, Seasonal forecasting in fuzzy time series, Fuzzy Sets and Systems 107 1999, 235--236. In his paper, the model was a first order...

Fuzzy inference systems have been used for prediction problems in the literature. Classical fuzzy inference systems are rule-based systems. The determination of the rules is important and difficult problem. Fuzzy functions were proposed as a good alternative for fuzzy inference systems. Fuzzy functions are not rule-based. This is a big advantage fo...

Many fuzzy t ime series forecasting methods have been suggested to forecast time series in the literature. Most of these methods use various artificial intelligence methods. The determining of the fu zzy relation is one of the most important stage in fuzzy t ime series methods. Some methods determine fuzzy relations by using index number of the fu...

Traditional forecasting methods need strict assumptions such as normality and linearity. It is very difficult to satisfy these assumptions for real-world time series. Many real-world time series can be easily analyzed by using fuzzy time series methods since fuzzy time series methods do not require any strict assumptions. Therefore, fuzzy time seri...

The aim of this study is to expose the conceptual frameworks of undergraduates who studied environmental information before about ecology and environemnt and determine concept misunderstandings by using word association test. The research was appealed on 81 students studying in Necmettin Erbakan University A. K. Educational Faculty Department of Ge...

The concept of reliability statistically pertains to the data obtained. Therefore, reliability of the attitude scales and satisfaction questionnaires should be expressed as the accuracy degree of the data obtained. Accuracy is depending on whether the answers of the individuals who participate in the questionnaire reflect their real thoughts. Howev...

Yapay sinir ağları literatürde zaman serisi öngörü problemi için sıklıkla kullanılmaktadır. Yapay sinir ağlarının, zaman serisi öngörüsü için kullanılan birçok türü vardır. Literatürde ilk kez Yadav vd. (2007) tarafından tek çarpımsal sinir hücresi model önerilmiştir. Tek çarpımsal sinir hücresi model, diğer yapay sinir hücresi modellerinden farklı...

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. This type has been used to forecast various types of time series in many implementations. In this study, a novel multiplicative...

Artificial intelligence procedures such as artificial neural networks (ANNs), genetic algorithms and particle swarm optimization and other procedures such as fuzzy clustering have been successfully used in the various stages of different fuzzy time-series forecasting approaches. Fuzzy clustering, genetic algorithm and particle swarm optimization ar...

Fu zzy time series forecasting methods have been widely studied in recent years. This is because fuzzy time series forecasting methods are co mpatib le with flexib le calculat ion techniques and they do not require constraints that exist in conventional time series approaches. Most of the real life time series exh ibit periodical changes arising fr...

Determination of fuzzy logic relationships between observations is quite effective on the forecasting performance of fuzzy time series approaches. In various studies available in the literature, it has been seen that utilizing artificial neural networks for establishing fuzzy relations increase the forecasting accuracy. In this study, a novel high...

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 algorithms have been used in fuzzy time series method to improve the method. While fuzzy clustering and genetic algorithms are b...

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 can model both these two components have also been proposed in the literature. The hybrid approaches suggested in the literature...

Multilayer perceptron has been widely used in
time series forecasting for last two decades. However, it is
a well-known fact that the forecasting performance of
multilayer perceptron is negatively affected when data
have outliers and this is an important problem. In recent
years, some alternative neuron models such as generalizedmean
neuron, geomet...

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 modeling error and bad forecasting performance like in conventional time series analysis. To overcome these problems, a new first-ord...

The main purpose of the present study is to develop some artificial neural network (ANN) models for the prediction of limit pressure (P
L) and pressuremeter modulus (E
M) for clayey soils. Moisture content, plasticity index, and SPT values are used as inputs in the ANN models. To get plausible results, the number of hidden layer neurons in all mode...

Gözlemleri gün içinde değişen borsa endeksi, altın fiyatları veya döviz kuru gibi zaman serilerinin yapay sinir ağları ile çözümlenmesi bu çalışmanın temel aldığı problemdir.Altın fiyatları gibi zaman serileri gün içinde değişen aralık değerlere sahip olduğundan klasik zaman serileri analizi ile bu tür verilerin çözümlenmesinde en düşük ve en yükse...

Fuzzy time series forecasting methods, which have been widely studied in recent years, do not require constraints as found in conventional approaches. On the other hand, most of the time series encountered in real life should be considered as fuzzy time series due to the vagueness that they contain. Although numerous methods have been proposed for...

Fuzzy set theory has been widely used in various fields of statistics in recent years. The correlation between fuzzy random variables can be measured by a fuzzy correlation coefficient. When the correlation of the fuzzy random variables has being calculated, mathematical programming and fuzzy arithmetic operations have been used in the literature....

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 included in the model. Determining the lagged variables which will be in the forecasting model is a vital issue. Also, defining fuzz...

In recent years, artificial neural networks have being successfully used in time series analysis. Using linear methods such as ARIMA and exponential smoothing for non linear time series cannot produce satisfactory results. Although there are various non linear methods, these methods have an important drawback that all of them require a specific mod...

In recent years, artificial neural networks have being successfully used in time series analysis. Using linear methods such as ARIMA and exponential smoothing for non linear time series cannot produce satisfactory results. Although there are various non linear methods, these methods have an important drawback that all of them require a specific mod...

In recent years, autoregressive fractionally integrated moving average (ARFIMA) models have been used for forecasting of long memory time series in the literature. Major limitation of ARFIMA models is the pre-assumed linear form of the model. Since many time series in real-world have non-linear structure, ARFIMA models are not always satisfactory....

In recent years, the most preferred forecasting method in time series forecasting has been artificial neural networks. In many applications, artificial neural networks have been successfully employed to obtain accurate forecasts in the literature. This approach has been preferred to conventional time series forecasting models because of its easy us...

In the literature, different selection criteria are used for determining the best architecture when time series is analyzed by artificial neural networks. Criteria available in the literature measure different properties of forecasts. To obtain better forecasts, Eǧrioǧlu et al. [1] proposed a criterion which can measure all properties of forecasts....

The accurate forecast of public expenditure is crucial for the success of the new public financial management approach developed in Turkey since the financial crisis of 2001. The public institutions are now obliged to align their expenditure with the framework shaped by the Public Financial Management and Control Law (No: 5018), the Middle-Term Pro...

In recent years, artificial neural networks (ANN) have been widely used in real life time series forecasting. Artificial neural networks can model both linear and curvilinear structure in time series. Most of the conventional methods used in the analysis of time series are linear structure and fail to analyze non-linear time series. In conventional...

Forecasting the number of outpatient visits plays important role in strategic decisions for the expert of healthcare administration. In order to manage hospitals effectively, it is needed to forecast the number of outpatient visits accurately. In the literature, there have been some methods proposed to forecast these time series. One of these metho...

Most of the time series faced in real life are fuzzy time series and these time series have to be forecasted by fuzzy time series forecasting methods. Therefore, there have been many studies in the literature in which various fuzzy time series approaches are proposed. The fuzzy time series methods introduced in the literature have been generally pr...

Forecast combination is a method used for obtaining more accurate forecasts. Forecast combination consists of the combination of forecasts obtained from different models with various methods. There are several types of forecast combination in the forecasting literature. In this study, various fuzzy time series approaches are applied to Turkey's dai...

In recent years, studies including long memory time series are existed in the literature. Such time series in real life may have both linear and nonlinear structures. Linear models are inadequate for this kind of time series. An alternative method to forecast these time series is artificial neural networks which is data based and can model both lin...

Various theoretical assumptions in conventional time series methods do not need to be checked in fuzzy time series approach. Therefore fuzzy time series are preferred in many applications. The identification of the length of intervals is an important issue and affects the forecasting performance. But in many studies in the literature, the length of...

The researchers from various fields have been studying on time series forecasting for approximately one century in order to get better forecasts for the future.To achieve high forecast accuracy level, various time series forecasting approaches have been improved in the literature. During 1980s, some crucial developments happened and time series res...

Fuzzy time series approaches have been recently used for forecasting in many studies [1]. These approaches can be categorized into two subclasses that are univariate and multivariate approaches. It is a fact that many factors can actually affect real time series data. Therefore, using a multivariate fuzzy time series forecasting model can be more r...

Fuzzy time series, subjected to many scientific studies, have been used in forecasting in recent years. Due to their uncertainty, time series encountered in daily life should be perceived as fuzzy time series and analyzed by fuzzy time series methods. Instead of representing time series, which may have different values during the time they measured...

Time series analysis has got attention of many researches from different fields, such as business administration, economics, public finances, engineering, statistics, econometrics, mathematics and actuarial sciences. When many organizations are planning their future, they have to forecast the future. Time series analysis has been employed by many o...

This paper presents an alternative approach to the dual response systems problem by utilizing a tabu search algorithm that yields a string of solutions and examine the trade-offs graphically and systematically how the controllable variables simultaneously impact the mean and the standard deviation of a characteristic of interest relevant to an indu...

Artificial neural networks (ANN) have been successfully applied to a multitude of problems in various fields. One of the most prominent application fields is time series forecasting. Although ANN produces accurate forecasts in many time series implementations, there are still some problems with using ANN. ANN approach is composed of some components...

Son yıllarda zaman serisi öngörüsü için yapay sinir ağlarına olan ilgi gittikçe artmaktadır.
Yapay sinir ağlarının normallik, doğrusallık gibi zor kısıtlara sahip olmaması uygulamada
daha çok tercih edilmesine sebep olmaktadır. Yapay sinir ağları öngörü probleminin
çözümünde birçok avantajına rağmen göz ardı edilemeyecek problemlere de sahiptir. Ya...

Artificial neural networks (ANN) have been used in various applications in recent years. One of these applications is time series forecasting. Although ANN produces accurate forecasts in many time series implementations, there are still some problems with using ANN. ANN consist of some components such as architecture structure, learning algorithm a...

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 sensible and sound results in clustering analysis. In many clustering algorithm, it is firstly need to know number of cluster. Howe...

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 the length of intervals, two approaches which are based on the average and the distribution have been proposed by Huarng [4]. In t...

Seasonal Autoregressive Fractionally Integrated Moving Average (SARFIMA) models are used in the analysis of seasonal long memory-dependent time series. Two methods, which are conditional sum of squares (CSS) and two-staged methods introduced by Hosking (1984), are proposed to estimate the parameters of SARFIMA models. However, no simulation study h...

In the past decade, there have been many implementations in which artificial neural networks (ANN) successfully applied to many areas of science and engineering. One of these areas is time series forecasting. ANN method has been preferred to conventional time series forecasting models because of its easy usage and providing accurate results. In spi...

Artificial neural networks (ANN) have been efficiently used in many fields. One of these fields is forecasting. Forecasting problem is an important issue on which many researchers from different disciplines have still working. ANN method has proved its success in time series forecasting. While traditional time series forecasting methods are not suf...

Time series forecasting is a vital issue for many institutions. In the literature, many researchers from various disciplines have tried to improve forecasting models to reach more accurate forecasts. It is known that real life time series has a nonlinear structure in general. Therefore, conventional linear methods are insufficient for real life tim...

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 accuracy by utilizing feed forward neural networks to determine fuzzy relationships in high order fuzzy time series. Anothe...

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 neural networks and fuzzy
time series approaches have proved their success in the field of forecasting. In this study, a new for...

The observations of some real time series such as temperature and stock market can take different values in a day. Instead of representing the observations of these time series by real numbers, employing linguistic values or fuzzy sets can be more appropriate. In recent years, many approaches have been introduced to analyze time series consisting o...

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 fuzzy time series approaches improve the forecasting accuracy. One of the important parts of obtaining high accuracy forecasts i...

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 have been proposed in order to select the best model for forecasting in ANN in recent years. One of these approaches is to use...

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 brain wave data which has been recorded during the Wisconsin Card Sorting Test. The forecasting performances of different artif...

Obtaining the inflation prediction is an important problem. Having this prediction accurately will lead to more accurate decisions. Various time series techniques have been used in the literature for inflation prediction. Recently, Artificial Neural Network (ANN) is being preferred in the time series prediction problem due to its flexible modeling...

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 have been proposed in order to select the best model for forecasting in ANN in recent years. One of these approaches is to use...

1. Giriş Son yıllarda zaman serisi öngörüsü için yapay sinir ağlarına olan ilgi gittikçe artmaktadır. Yapay sinir ağlarının normallik, doğrusallık gibi zor kısıtlara sahip olmaması uygulamada daha çok tercih edilmesine sebep olmaktadır. Yapay sinir ağları öngörü probleminin çözümünde birçok avantajına rağmen göz ardı edilemeyecek problemlere de sah...

The course timetabling problem must be solved by the departments of Universities at the beginning of every semester. It is a though problem which requires department to use humans and computers in order to find a proper course timetable. One of the most mentioned difficult nature of the problem is context dependent which changes even from departmen...

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 violations. Therefore, forecasts
are not reliable. [8], [9] gave a definition of fuzzy time series which have fuzzy observations and...

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 series data can actually be affected by many factors. In this case, the using multivariate fuzzy time series forecasting model can...