Prediction of Daily Gold Prices Using an Autoregressive Neural Network

Mohamad As'ad, Sujito Sujito, Sigit Setyowibowo


Gold is a precious metal that functions as a gem and also an investment. Gold investment is the reason for many people because it is practical, not easily damaged, easy cashed, not taxable, and other purposes. Based on this, many people choose gold as an investment. The problem for people who will invest in gold is related to uncertain gold price predictions so that the accuracy of forecasting methods are needed. The purpose of this paper is to forecast accurately daily gold prices using the Neural Network Autoregressive (NNAR) method. Training Data to find out the value of accuracy in the NNAR method uses secondary data obtained from Yahoo Finance in the form of daily gold prices. Test results on the NNAR method produce a better and more accurate level using the NNAR (25,13) model with a MAPE value of 0.370707, a MASE of 0.5851083, and an RMSE of 6.939331. The conclusion of the results of this paper is the daily price of gold is influenced by the daily price of gold a day ago to 24 periods ago with the NNAR (25,13) model.


prediction; forecasting; daily gold prices; yahoo finance dataset; artificial neural network; neural network autoregressive

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