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Öğe A new approach based on artificial neural networks for high order multivariate fuzzy time series(PERGAMON-ELSEVIER SCIENCE LTD, 2009) Egrioglu, Erol; Aladag, Cagdas Hakan; Yolcu, Ufuk; Uslu, Vedide R.; Basaran, Murat A.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 be more reasonable in order to get more accurate forecasts. To obtain fuzzy forecasts when multivariate fuzzy time series approach is adopted, the most applied method is using tables of fuzzy relations. However, employing this method is a computationally though task. In this study, we introduce a new method that does not require using fuzzy logic relation tables in order to determine fuzzy relationships. Instead, a feed forward artificial neural network is employed to determine fuzzy relationships. The proposed method is applied to the time series data of the total number of annual car road accidents casualties in Belgium from 1974 to 2004 and a comparison is made between our proposed method and the methods proposed by Jilani and Burney [Jilani, T. A., & Burney, S. M. A. (2008). Multivariate stochastic fuzzy forecasting models. Expert Systems with Applications, 35, 691-700] and Lee et al. [Lee, L.-W., Wang, L.-H., Chen, S.-M., & Leu, Y.-H. (2006). Handling forecasting problems based on two factors high order fuzzy time series. IEEE Transactions on Fuzzy Systems, 14, 468-477]. (C) 2009 Elsevier Ltd. All rights reserved.Öğe A new approach based on the optimization of the length of intervals in fuzzy time series(IOS PRESS, 2011) Egrioglu, Erol; Aladag, Cagdas Hakan; Basaran, Murat A.; Yolcu, Ufuk; Uslu, Vedide R.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 this paper, we propose a new method based on the use of a single variable constrained optimization to determine the length of interval. In order to determine optimum length of interval for the best forecasting accuracy, we used aMATLAB function which is employing an algorithm based on golden section search and parabolic interpolation. Mean square error is used as a measure of forecasting accuracy so the objective function value is mean square error value for forecasted observations. The proposed method was employed to forecast the enrollments of the University of Alabama to show the considerable outperforming results.Öğe A new approximation method based on linear programming for fuzzy division(WORLD SCIENTIFIC AND ENGINEERING ACAD AND SOC, 2008) Basaran, Murat Alper; Aladag, Cagdas Hakan; Kadilar, Cem; Demiralp, M; Mikhael, WB; Caballero, AA; Abatzoglou, N; Tabrizi, MN; Leandre, RInstead of doing arithmetic operations using fuzzy membership functions for fuzzy numbers, parameterized representation of fuzzy numbers have been used in arithmetic operations in the literature. The most applied parameterized fuzzy numbers used in many of the research papers are symmetric and asymmetric triangular and trapezoidal fuzzy numbers. Although addition and subtraction of parameterized fuzzy numbers give closed form results, multiplication and division of these fuzzy numbers generate approximated results. Therefore, some approximation methods have been proposed for fuzzy multiplication and division. In this study, we propose a new approximation method based on linear programming for fuzzy division. In order to show the applicability of the proposed method, some examples are solved using this method and the: results are compared with those generated by other methods in the literature. The proposed method has produced better results than those generated by the others.Öğe A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model(PERGAMON-ELSEVIER SCIENCE LTD, 2009) Egrioglu, Erol; Aladag, Cagdas Hakan; Yolcu, Ufuk; Basaran, Murat A.; Uslu, Vedide R.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 is obtained by using the previous observation. In other words, only the first tagged variable is used when constructing the first order fuzzy time series model. Therefore, this model can not be sufficient for some time series such as seasonal time series which is an important class in time series models. Besides, the time series encountered in real life have not only autoregressive (AR) structure but also moving average (MA) structure. The fuzzy time series models available in the literature are AR structured and are not appropriate for MA structured time series. In this paper, a hybrid approach is proposed in order to analyze seasonal fuzzy time series. The proposed hybrid approach is based on partial high order bivariate fuzzy time series forecasting model which is first introduced in this paper. The order of this model is determined by utilizing Box-Jenkins method. In order to show the efficiency of the proposed hybrid method, real time series are analyzed with this method. The results obtained from the proposed method are compared with the other methods. As a result, it is observed that more accurate results are obtained from the proposed hybrid method. (C) 2008 Elsevier Ltd. All rights reserved.Öğe Finding an optimal interval length in high order fuzzy time series(PERGAMON-ELSEVIER SCIENCE LTD, 2010) Egrioglu, Erol; Aladag, Cagdas Hakan; Yolcu, Ufuk; Uslu, Vedide R.; Basaran, Murat A.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 in fuzzy time series is that the length of interval is very vital. As mentioned in the first-order models by Egrioglu, Aladag, Basaran, Uslu, and Yolcu (2009), the length of interval also plays very important role in high order models too. In this study, a new approach which uses an optimization technique with a single-variable constraint is proposed to determine an optimal interval length in high order fuzzy time series models. An optimization procedure is used in order to determine optimum length of interval for the best forecasting accuracy, we used optimization procedure. In the optimization process, we used a MATLAB function employing an algorithm based on golden section search and parabolic interpolation. The proposed method was employed to forecast the enrollments of the University of Alabama to show the considerable outperforming results. (C) 2009 Elsevier Ltd. All rights reserved.Öğe Improving weighted information criterion by using optimization(ELSEVIER SCIENCE BV, 2010) Aladag, Cagdas Hakan; Egrioglu, Erol; Gunay, Suleyman; Basaran, Murat A.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 a model selection strategy based on the weighted information criterion (WIC). WIC is calculated by summing weighted different selection criteria which measure the forecasting accuracy of an ANN model in different ways. In the calculation of WIC, the weights of different selection criteria are determined heuristically. In this study, these weights are calculated by using optimization in order to obtain a more consistent criterion. Four real time series are analyzed in order to show the efficiency of the improved WIC. When the weights are determined based on the optimization, it is obviously seen that the improved WIC produces better results. (C) 2009 Elsevier B.V. All rights reserved.Öğe The effect of neighborhood structures on tabu search algorithm in solving course timetabling problem(PERGAMON-ELSEVIER SCIENCE LTD, 2009) Aladag, Cagdas Hakan; Hocaoglu, Gulsum; Basaran, Murat AlperThe 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 departments to departments. Different heuristic approaches have been proposed in order to solve this kind of problem in the literature. One of the efficient solution methods for this problem is tabu search. Different neighborhood structures based on different types of move have been defined in studies using tabu search. In this paper, the effects of moves called simple and swap on the operation of tabu search are examined based on defined neighborhood structures. Also, two new neighborhood structures are proposed by using the moves called simple and swap. The fall semester of course timetabling problem of the Department of Statistics at Hacettepe University is solved utilizing four neighborhood structures and the comparison of the results obtained from these structures is given. (C) 2009 Elsevier Ltd. All rights reserved.
