Forecasting of Daily Prices of Gold in India using ARIMA and FFNN Models
K Murali Krishna1, N Konda Reddy2, M Raghavendra Sharma3
1K Murali Krishna, Department of Mathematics, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur (Andhra Pradesh), India.
2Dr N Konda Reddy, Department of Mathematics, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur (Andhra Pradesh), India.
3Dr M Raghavendra Sharma, Department of Statistics, Osmania University, Hyderabad (Telangana), India.
Manuscript received on 18 February 2019 | Revised Manuscript received on 27 February 2019 | Manuscript published on 28 February 2019 | PP: 516-521 | Volume-8 Issue-3, February 2019 | Retrieval Number: C5970028319/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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 1st January, 2014 to 24th 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
Keywords: Gold Prices, ARIMA, FFNN, MAE, MAPE and RMSE.
Scope of the Article: Applied Mathematics and Mechanics