Time series forecasting using artificial bee colony based neural networks

Artificial neural networks (ANN) are among the nonlinear prediction techniques popular in the last two decades. Recent studies show that ANN can be modeled with different training techniques. ANN is usually trained by the backpropagation method (BP). In this study, ANN structures were trained by using artificial bee colony algorithm (ABC) and, weight and bias values were tried to be determined. ABC training (ANN-ABC) was tested over three different datasets and compared with the BP training (ANN-BP) results. In addition to use ABC in modeling, different error types such as mean square error (MSE), mean absolute percent error (MAPE) and adjusted coefficient of determination (R) have been used in the training. The results on popular time series datasets have shown that ABC based ANN training yields successful results in forecasting.