Developing a preci se and accurate model of gold price is critical to assets
management because of its unique features. In this paper, adaptive neuro-fuzzy inference
system (ANFIS) and artifi cial neural network (ANN) model have been used for modeling
the gold price, and compared with the traditional statistical model of ARIMA (autoregressive
integrated moving average). The three performance measures, the coeffi cient of
determination (R2), root mean squared error (RMSE), mean absolute error (MAE), are
utilized to evaluate the performances of different models developed. The results show
that the ANFIS model outperforms other models (i.e. ANN and ARIMA model), in terms
of different performance criteria during the training and validation phases. Sensitivity
analysis showed that the gold price changes are highly dependent upon the values of
silver price and oil price.
کلید واژگان :
forecasting, gold price changes, adaptive network fuzzy inference system
