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2nd World Conference on Business, Economics and Management (BEM) -- APR 25-28, 2013 -- Antalya, TURKEY WOS: 000335570200182 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 some approaches in the literature to deal with the problem of determining the number of neurons in hidden layer. A new ANN model was suggested which is called multiplicative neuron model (MNM) in the literature. MNM has only one neuron in hidden layer. Therefore, the problem of determining the number of neurons in hidden layer is automatically solved when MNM is employed. Also, MNM can produce accurate forecasts for non-linear time series. ANN models utilized for non-linear time series have generally autoregressive structures since lagged variables of time series are generally inputs of these models. On the other hand, it is a well-known fact that better forecasts for real life time series can be obtained from models whose inputs are lagged variables of error. In this study, a new recurrent multiplicative neuron neural network model is firstly proposed. In the proposed method, lagged variables of error are included in the model. Also, the problem of determining the number of neurons in hidden layer is avoided when the proposed method is used. To train the proposed neural network model, particle swarm optimization algorithm was used. To evaluate the performance of the proposed model, it was applied to a real life time series. Then, results produced by the proposed method were compared to those obtained from other methods. It was observed that the proposed method has superior performance to existing methods. (C) 2014 The Authors. Published by Elsevier Ltd. Selection and peer review under responsibility of Organizing Committee of BEM 2013. SciencePark Res, Org & Counseling Ltd, Acad World Educ & Res Ctr, Hacettepe Univ, Eastern Mediterranean Univ, Near E Univ, Zirve Univ

Applied Computing Conference 2008 -- MAY 27-30, 2008 -- Istanbul, TURKEY, WOS: 000257984200069, Instead 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.

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 has been conducted in the literature. Therefore, it is not known how these methods behave under different parameter settings and sample sizes in SARFIMA models. The aim of this study is to show the behavior of these methods by a simulation study. According to results of the simulation, advantages and disadvantages of both methods under different parameter settings and sample sizes are discussed by comparing the root mean square error (RMSE) obtained by the CSS and two-staged methods. As a result of the comparison, it is seen that CSS method produces better results than those obtained from the two-staged method.