Gold price prediction using support vector regression and ANFIS models

Abstract

In recent times, gold has been one of the prioritized commodities in terms of long term as well as short term investments since the investors consider gold as a hedgerow against the unforeseen events leading to chaos in the market. Consequentially, the price of gold in the market plays an important role. In this research work, time-series gold price prediction models have been developed using the support vector regression and anfis models for the prediction of daily gold prices. The support vector model was designed using epsilon support vector regression method while the adaptive neural fuzzy inference systems have been developed using grid partition and subtractive clustering methods. The gold prices obtained for the training and testing were obtained from Perth Mint of Australia. The evaluation criteria for the comparison of the models are Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Nash-Sutcliffe model efficiency coefficient (E) and Mean absolute percentage error (MAPE). It was observed that the models obtained using support vector regression outperformed the ANFIS models. In the ANFIS models, it was observed that ANFIS-GP performed slightly better than the ANFIS-SC model.