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Forecasting Monthly Prices of Gold Using Artificial Neural Network

Author

Listed:
  • Nwokike Chukwudike C.
  • Ugoala
  • Chukwuma B.
  • Obubu Maxwell
  • Uche-Ikonne Okezie O.
  • Offorha Bright C.
  • Ukomah Henry I.

Abstract

This study successfully ï¬ tted an artiï¬ cial neural network to a series on gold prices. The data used was monthly gold prices in US dollars and cents per troy ounce from October 2004 to February 2020. Of the 17 suggested Artiï¬ cial Neural Network structures, the one with 2, 6 and 1 neurons in the input, hidden and output layers (ANN (2-6-1)) was adjudged the best because it had the least error, Mean Square Error (MSE) and Mean Absolute Error (MAE). The adequacy of the selected model was further conï¬ rmed by graphical examination of the actual values of gold prices and the ones predicted by the model as well as graphical residual analysis. Consequently, forecasts were made using the chosen network. The forecasts suggest a decline in gold prices in the coming months.  Keywords: Gold, Forecasting, ANN.

Suggested Citation

  • Nwokike Chukwudike C. & Ugoala & Chukwuma B. & Obubu Maxwell & Uche-Ikonne Okezie O. & Offorha Bright C. & Ukomah Henry I., 2020. "Forecasting Monthly Prices of Gold Using Artificial Neural Network," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 9(3), pages 1-2.
    • Handle: RePEc:spt:stecon:v:9:y:2020:i:3:f:9_3_2
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    References listed on IDEAS

    as
    1. Maravall, Agustin, 1983. "An Application of Nonlinear Time Series Forecasting," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(1), pages 66-74, January.
    2. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    3. Ayodele Ariyo Adebiyi & Aderemi Oluyinka Adewumi & Charles Korede Ayo, 2014. "Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-7, March.
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    Keywords

    gold; forecasting; ann.;
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