Comparison between Artificial Neural Network and neuro-fuzzy for gold price prediction

This article presents a comparison of Artificial Neural Network (ANN) and Adaptive Neural Fuzzy Inference System (ANFIS) for predicting a real system, gold price. Also, we compared a new hybrid model which is a weighted average of the ANN and ANFIS model. The main objective is to predict the gold price in the Forex market. We used two prediction machine models in ANN, a model which feeds back the network output as input and another model that does not do it. Our results show that the performance error of the former model is more than the latter, and also the performance of ANFIS is better than both models of ANN. To evaluate the methods three performance measurements are used: Root Mean Squared Error (RMSE), percentage error and Mean Tendency Error (MTE) which is proposed in this study. The strength point of our method is the prediction machine model that is one of the most powerful prediction machine models of ANN. At last, a Wavelet denoising algorithm is applied to the data, but due to the chaotic structure of the gold price, it impairs data and causes to reduce the performance of prediction result.