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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 approach for determining the length of intervals for fuzzy time series(ELSEVIER SCIENCE BV, 2009) Yolcu, Ufuk; Egrioglu, Erol; Uslu, Vedide R.; Basaran, Murat A.; Aladag, Cagdas H.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 papers. Huarng developed a new approach which is called ratio-based lengths of intervals in order to identify the length of intervals. In our paper, we propose a new approach which uses a single-variable constrained optimization to determine the ratio for the length of intervals. The proposed approach is applied to the two well-known time series, which are enrollment data at The University of Alabama and inventory demand data. The obtained results are compared to those of other methods. The proposed method produces more accurate predictions for the future values of used time series. (c) 2008 Elsevier B.V. All rights reserved.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 Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations(PERGAMON-ELSEVIER SCIENCE LTD, 2009) Aladag, Cagdas H.; Basaran, Murat A.; Egrioglu, Erol; Yolcu, Ufuk; Uslu, Vedide R.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 series approach. Defining fuzzy relation in high order fuzzy time series approach are more complicated than that in first order fuzzy time series approach. A new proposed approach, which uses feed forward neural networks to define fuzzy relation in high order fuzzy time series, is introduced in this paper. The new proposed approach is applied to well-known enrollment data for the University of Alabama and obtained results are compared with other methods proposed in the literature. It is found that the proposed method produces better forecasts than the other methods. (c) 2008 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 Monitoring corrosion and corrosion control of iron in HCl by non-ionic surfactants of the TRITON-X series - Part III. Immersion time effects and theoretical studies(PERGAMON-ELSEVIER SCIENCE LTD, 2011) Amin, Mohammed A.; Ahmed, M. A.; Arida, H. A.; Kandemirli, Fatma; Saracoglu, Murat; Arslan, Taner; Basaran, Murat A.The inhibition performance of three selected non-ionic surfactants of the TRITON-X series, namely TRITONX-100 (TX-100), TRITON-X-165 (TX-165) and TRITON-X-305 (TX-305), on the corrosion of iron was studied in 1.0 M HCl solutions as a function of inhibitor concentration (0.01-0.20 g L(-1)) and immersion time (0.0-8 h) at 298 K. Measurements were conducted based on Tafel polarization, LPR and impedance studies. At high frequencies, the impedance spectrum showed a depressed capacitive loop in the complex impedance plane, whose diameter is a function of the immersion time and the type and concentration of the introduced surfactant. In all cases, an inductive loop was observed in the low frequency and this could be attributed to the adsorption behavior. The inhibition efficiency increased with immersion time, reached a maximum and then decreased. This was attributed to the orientation change of adsorbed surfactant molecules. TX-305 inhibited iron corrosion more effectively than TX-100 and TX-165. The frontier orbital energies, the energy gap between frontier orbitals, dipole moments (mu), charges on the C and O atoms, the polarizabilities, and the quantum chemical descriptors were calculated. The quantum chemical calculation results inferred that for the HOMO representing the condensed Fukui function for an electrophilic attack (f(k)(+)), the contributions belong to the phenyl group and the oxygen atom attached to the phenyl group for each tested surfactant. Quantitative structure-activity relationship (QSAR) approach has also been used and a correlation of the composite index having some of the quantum chemical parameters with average inhibition efficiencies (I(av.)(%)) was conducted to determine the inhibition performance of the tested surfactant molecules. The results showed that the values of I(av.)(%) of the tested inhibitor molecules were closely related to some of the quantum chemical parameters. The calculated I(av.)(%) values were found to be close to the experimental ones. Based on the values of coefficients of correlations at 0.02 level concentration. B3LYP/3-21G* method outpaced the other two methods namely B3LYP/6-31G and RHF/6-31G*. The relationship between I(av.)(%) and composite index is accounted for by B3LYP/3-21G*. (C) 2011 Elsevier Ltd. All rights reserved.
