Abstract
Developing a precise 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 artificial 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 coefficient of determination (R 2), 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.
Highlights
Gold price plays a significant role in economical and monetary systems
The results indicate that the best performance can be obtained by adaptive neuro-fuzzy inference system (ANFIS), genetic programming (GP) and support vector machine (SVM), in terms of different evaluation criteria during the training and validation phases
Because accurate forecasting of gold price changes helps to foresee the circumstances of trends in the future; so that, this provides the useful information for stakeholder to fulfill the appropriate strategies in order to prevent or mitigate risks
Summary
Gold price plays a significant role in economical and monetary systems. The price of gold and other assets are often closely correlated (Corti, Holliday 2010). Artificial intelligence models such as artificial neural networks (ANN) have been developed as a non-linear tool for gold price forecasting (Achireko, Ansong 2000; Parisi et al 2008; Lineesh et al 2010). Wang et al (2009) employed autoregressive moving-average (ARMA) models, artificial neural networks (ANNs) approaches, ANFIS techniques, genetic programming (GP) models and support vector machine (SVM) method to forecast monthly discharge time series. ANFIS uses the hybrid-learning algorithm, consists of the combination of gradient descent, and least-squares methods The former is employed to determine the nonlinear input parameters and the latter is used to identify the linear output parameters. The process of adjusting the weights is continued until performance measures are satisfied
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