Abstract: Developing a precise model for forecasting gold price is critical because of its unique features. Hence, continued research is directed to improve forecasting models employing a variety of techniques. In previous researches, ARIMA (Autoregressive Integrated Moving Average) and MLR (Multiple Linear Regression) model we have incorporated both heteroskedastic concept and learning past prediction errors. In this paper an enhanced ANFIS-GARCH model is proposed to overcome the above issues and compared with the Existing models. ANFIS-GARCH is comparatively good and robust than Existing models. ANFIS-GARCH has the capability to integrate and simulate knowledge from quantitative and qualitative source to model the behavior changes in the prediction. The coefficient of determination (R2), Root Mean Squared Error (RMSE) are utilized to evaluate the performance of developed model.
Keywords: Generalized Autoregressive Conditional Heteroskedasticity (GARCH), Gold price, Forecasting, US dollar, Inflation, ARIMA model, MLR Model.
Title: An Adaptive Network Based Fuzzy Inference System for the Prediction of Gold Price Using Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Method
Author: Dhanalakshmi. P.M, P. R. S. Reddy
International Journal of Management and Commerce Innovations
ISSN 2348-7585 (Online)
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