Forecasting of daily prices of gold in India using ARIMA and FFNN models

Abstract

The present paper is aimed to develop a forecasting model to predict the daily gold prices in India with high accuracy. The historical prices of gold were collected from 1 st January, 2014 to 24 th July, 2018 and the same is divided into training sample and out-of-sample. The forecasting models were developed using auto regressive integrated moving average (ARIMA) and artificial neural networks (ANN) for the daily gold prices in India. The performance of the forecasting model was evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE). The results show that, the feed forward neural networks (FFNN) model outperforming the traditional ARIMA model.