Forecasting total health expenditures with a hybrid heuristic method

Artificial neural networks (ANN) have been successfully applied to a multitude of problems in various fields. One of the most prominent application fields is time series forecasting. Although ANN produces accurate forecasts in many time series implementations, there are still some problems with using ANN. ANN approach is composed of some components which are architecture structure, learning algorithm and activation function. These components have important effect on the forecasting performance of ANN. An important decision is the selection of optimum architecture of neural network that consists of determining the numbers of neurons in the layers of the network. Therefore, various approaches have been proposed to find the best ANN architecture in the literature. However, the most preferred method is still trial and error method for finding a good architecture. In this study, the total health expenditures made by social security institution in Turkey is forecasted by using a hybrid heuristic method proposed by Aladag [10] which is based on feed forward neural networks and Tabu search algorithm. It is seen that the hybrid forecasting approach produces very accurate results.