An architecture selection method based on tabu search

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 usage and providing accurate results. On the other hand, there are still some problems with using this method. Fining a good artificial neural network architecture which gives the most accurate forecasts is an important issue when the method is used for forecasting. Although, there are some systematical methods proposed to determine the best architecture, the most preferred method is trial and error method [1]. To solve the architecture selection problem, Aladag [2] also proposed an approach based on tabu search algorithm. In this study, the air pollution in Ankara time series is forecasted by utilizing artificial neural networks and the architecture selection algorithm proposed by Aladag [2] is used to determine the best architecture. The obtained results show that high accuracy level is reached when Aladag's [2] algorithm is employed.