Gold Price Prediction Using the ARIMA and LSTM Models


  • Yudha Randa Madhika Ferdinandus @yudharanda
  • Kusrini Universitas Amikom Yogyakarta, Indonesia
  • Tonny Hidayat Universitas Amikom Yogyakarta, Indonesia




Gold Price, Prediction, Economic Indicators, ARIMA, LSTM


For some investors who are interested in investing for the long term, gold is one of the promising options because the price of gold has recently continued to increase. In the current condition, gold investors generally use instinct and guesswork in investing in gold because there is a benchmark gold price based on world market prices. Many empirical studies identify factors that affect gold prices to forecast them. Factual and econometric analysis recommend different informative factors. This study investigates the influence of gold prices and five supporting variables in the form of economic indicators, namely crude oil price, federal funds effective rate, consumer price index, effective exchange rate and S&P 500 stock market index between 2002 and 2022. Models were built using ARIMA and LSTM methods, evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percent Error (MAPE). With a dataset allocation of 80% for training data and 20% for testing data, the comparison of actual gold prices with the predicted values of each model shows that LSTM has the best performance compared to the ARIMA (0,1,1) model where the LSTM model has an RMSE value of 8.124 and a MAPE value of 0.023. The models also show that economic indicators affect the ounce price of gold.

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How to Cite

Ferdinandus, Y. R. M., Kusrini, K., & Hidayat, T. . (2023). Gold Price Prediction Using the ARIMA and LSTM Models. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(3), 1255-1264.