Prediction Of Gold Prices Using Hybrid Model Arima-Lstm - Mun, Woon Kah; Sufahani, Suliadi Firaus; Kamil, Anton Abdulbasah; Mohd Nawawi, Mohd Kamal (2025)
Time series forecasting has been gaining attention since the COVID19 pandemic to predict sales, economics, and weather outcomes. In this research, an empirical study on a time series model was done to predict daily gold prices using historical everyday gold prices from 1st January 2020 to 31st December 2021 as the training and testing dataset. The performance of autoregressive integrated moving average (ARIMA), long short-term memory (LSTM) and hybrid-model ARIMA-LSTM was compared through their mean absolute percentage error (MAPE) and root mean-squared error (RMSE) values. The results showed that the model with the smallest RMSE was the hybrid ARIMA-LSTM, but the model with the smallest MAPE was LSTM.