The Comparison between ARIMA and ARFIMA Model to Forecast Kijang Emas (Gold) Prices in Malaysia using MAE, RMSE and MAPE


  • Atiqa Nur Azza Mahmad Azan Faculty of Computer and Mathematical Sciences, University Teknologi MARA Shah Alam, Selangor, Malaysia
  • Nur Faizatul Auni Mohd Zulkifly Mototo Faculty of Computer and Mathematical Sciences, University Teknologi MARA Shah Alam, Selangor, Malaysia
  • Pauline Jin Wee Mah Faculty of Computer and Mathematical Sciences, University Teknologi MARA Shah Alam, Selangor, Malaysia



Kijang Emas Price, Time Series Modelling, ARIMA model, ARFIMA model


Gold is known as the most valuable commodity in the world because it is a universal currency recognized by every single bank across the globe. Thus, many people were interested in investing gold since gold market was always steadier compared to other investment (Khamis and Awang, 2020). However, the credibility of gold was questionable due to the changes in gold prices caused by a variety of circumstances (Henriksen, 2018). Hence, information on the inflation of gold prices were needed to understand the trend in order to plan for the future in accordance with international gold price standards. The aim of this study was to identify the trend of Kijang Emas monthly average prices in Malaysia from the year 2010 to 2021, to determine the best fit time series model for Kijang Emas prices in Malaysia and using univariate time series models to forecast Kijang Emas prices in Malaysia. The ARIMA and ARFIMA models were used in this study to model and forecast the prices of gold (Kijang Emas) in Malaysia. Each of the actual monthly Kijang Emas prices for 2021 were found to be within the 95% predicted intervals for both the ARIMA and ARFIMA models. The performances for each model were checked by considering the values of MAE, RMSE and MAPE. From the findings, all the MAE, RMSE and MAPE values showed that the ARFIMA model emerged as the better model in forecasting the Kijang Emas prices in Malaysia compared to the ARIMA model.


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Azzutti, A. (2016). Forecasting Gold Price: A Comparative Study. DOI:10.13140/RG.2.1. 4206.5686

Bank Negara Malaysia (2021). Kijang Emas Prices. Retrieved on 1 April 2021 from

Bernama. (2020). Covid-19 Vaccine Takes Shine Off Gold as Investment Item. Free Malaysia Today (FMT). Retrieved on 25 April 2021 from

Bhardwaj, G., & Swanson, N. R. (2006). An Empirical Investigation of The Usefulness of ARFIMA Models for Predicting Macroeconomic and Financial Time Series. Journal of Econometrics, 131(1-2), 539–578.

Brockwell, P.J. & Davis, R.A. (2002). Introduction to Time Series and Forecasting, Second Edition. Springer, New York.

Eryigit, M. (2017). Short-Term and Long-Term Relationships Between Gold Prices and Precious Metal (Palladium, Silver, and Platinum) and Energy (Crude Oil and Gasoline) Prices. Economic Research-Ekonomska Istraživanja, 30(1), 499–510.

Gorn, S. (2021). Gold Price Forecast and Prediction for 2021, 2022, 2023, 2025-2030: PrimeXBT. Retrieved on 18 May 2021 from

Granger, C. W., & Joyeux, R. (1980). An Introduction to Long-Memory Time Series Models and Fractional Differencing. Journal of Time Series Analysis, 1(1), 15–29.

Guha, B., & Bandyopadhyay, G. (2016). Gold Price Forecasting using ARIMA Model. Journal of Advanced Management Science, 4(2).

Gunaseelan, S & Kesavan, N. (2016). Gold Price Volatility Differences Among Major Countries - An Analysis for Two Decades (1996-2015).

Hashim, S. L., Ramlan, H., Razali, N. H., & Nordin, N. Z. (2017). Macroeconomic Variables Affecting the Volatility of Gold Price. Journal of Global Business and Social Entrepreneurship (GBSE), 3(5), 97-106.

Hayes, A. (2021). Autoregressive Integrated Moving Average (ARIMA). Investopedia. Retrieve on 31 July 2021 from

Henriksen, T. E. S. (2018). Properties of Long/Short Commodity Indices in Stock and Bond Portfolios. The Journal of Alternative Investments, 20(4), 51–68.

Hoong, T. B. (2021). Does Gold Still Matter Post-Pandemic? New Straits Times. NST Online. Retrieved on 18 May 2021 from

Johan, Z. J. (2020). Investors with The Golden-I: Preference in Gold-I Investment. Journal of Emerging Economies & Islamic Research, 8(2), 1-11.

Khamis, A., & Awang, N. S. (2020). Forecasting Kijang Emas Price Using Holt-Tend Exponential Smoothing and ARIMA Model. International Journal for Research in Applied Science and Engineering Technology, 8(8), 1531–1539.

Lazim. (2018). Introductory Business Forecasting: A Practical Approach. Kuala Lumpur: UiTM Press.

Pistilli, M. (2021). What Was the Highest Price of Gold? Investing News Network (INN). Retrieved on 24 August 2021 from

Razimi, A., Shahril, M., Romle, A.R. & Azizan, K.A. (2017). An Understanding of Shariah Issues on Gold Investment: A Review. Asian Journal of Business Management Studies (AJBMS), 8(1), 9-12.

Sato, R. C. (2013). Disease Management with ARIMA Model in Time Series. Einstein (Sao Paulo), 11, 128-131.

World Gold Council (2019). The Relevance of Gold as a Strategic Asset 2019 Edition. Retrieved on 29 April 2021 from

Yang, X. (2018). The Prediction of Gold Price Using ARIMA Model. Advances in Social Science, Education and Humanities Research, 196(2), 273-276.18.2019.66.

Yousef, I., & Shehadeh, E. (2020). The Impact of the COVID-19 On Gold Price Volatility. International Journal of Economics & Business Administration (IJEBA), 8(4), 353-364.

Zainab, M., & Neha, S. (2019). Gold and Investor's Perspective in Different Market Conditions. Advances in Management, 12(1), 68-70.




How to Cite

Mahmad Azan, A. N. A., Mohd Zulkifly Mototo, N. F. A., & Mah, P. J. W. (2021). The Comparison between ARIMA and ARFIMA Model to Forecast Kijang Emas (Gold) Prices in Malaysia using MAE, RMSE and MAPE. Journal of Computing Research and Innovation, 6(3), 22–33.