Robust training of median dendritic artificial neural networks for time series forecasting

Although artificial neural network models have produced very successful results in the time series forecasting problem in recent years, an outlier or outliers in the data set adversely affect the forecasting performance of the artificial neural network models. Dendritic neuron model artificial neural networks which are the most similar neural network model to an artificial neural network model are also adversely affected by outliers in the data set like many artificial neural network models in the literature. In this study, to prevent the dendritic neuron model artificial neural networks from being affected by the outliers in the data set; a robust learning algorithm based on Talwar's m estimator, median statistics to prevent the effect of outliers in the inputs, and a new data preprocessing method are used together in a network structure. In addition, the training of the proposed artificial neural network model is carried out with the symbiotic organism search algorithm. To evaluate the performance of the proposed method, analyses are carried out over the closing prices of the time series of Spain, Italy and German stock exchanges in certain years. According to the results of the analysis of the time series of the relevant stock exchanges, both in their original state and by injecting outliers into the time series, the proposed method has superior forecasting performance even when the time series contains outliers and does not contain outliers.