Comparison of Autoregressive Integrated Moving Average (ARIMA) model and Adaptive Neuro-Fuzzy Inference System (ANFIS) model (Case study: forecasting the gold price)

Proper models for prediction of time series data can be an advantage in making important decisions. in this study, we try to compare one of the most useful classic models of economic evaluation, auto regressive integrated moving average model with one of the most useful artificial intelligence models, adaptive neuro-fuzzy inference system (anfis). furthermore, we analyze the performance of these methods to predict the global gold price. our sample data is 200 gold prices from february 2015 to october 2015. we use both methods for determination of model parameters’ and to apply them on our test data. with respect to reliable evaluation methods, as root mean square of errors, it can be seen that in our test data, prediction of adaptive neuro-fuzzy inference system model is more accurate than auto-regressive integrated moving average. so we can conclude that at least in some cases where time series have nonlinear trend, it is better to use adaptive neuro-fuzzy inference system for prediction.