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In this study, a new hybrid forecasting method is proposed. The proposed method is called autoregressive adaptive network fuzzy inference system (AR–ANFIS). AR–ANFIS can be shown in a network structure. The architecture of the network has two parts. The first part is an ANFIS structure and the second part is a linear AR model structure. In the literature, AR models and ANFIS are widely used in time series forecasting. Linear AR models are used according to model-based strategy. A nonlinear model is employed by using ANFIS. Moreover, ANFIS is a kind of data-based modeling system like artificial neural network. In this study, a linear and nonlinear forecasting model is proposed by creating a hybrid method of AR and ANFIS. The new method has advantages of data-based and model-based approaches. AR–ANFIS is trained by using particle swarm optimization, and fuzzification is done by using fuzzy C-Means method. AR–ANFIS method is examined on some real-life time series data, and it is compared with the other time series forecasting methods. As a consequence of applications, it is shown that the proposed method can produce accurate forecasts. [ABSTRACT FROM AUTHOR]

It had been asserted that the more kind of instruments meant the less risk of portfolio until 1950. Conventional portfolio theory that proposed to invest for instruments was given up after mean- variance model was proposed and modern portfolio theory was established. The mathematical programming techniques are used to solve mean-variance model. In recent years, artificial intelligence methods have been employed for portfolio optimization. In this study, standard and guaranteed convergence particle swarm optimization methods have been applied to optimize the portfolio that contains IMKB 30 stock shares. The results are compared to the other results that are obtained through mathematical programming. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on topics including ideas to gather case studies and data analysis from the papers presented at the eleventh International Statistics Days Conference (ISDC).