<![CDATA[Precious metals, particularly gold, represent one of the most sought-after value-preserving investment instruments, yet their dynamic price fluctuations present significant challenges, making gold a difficult-to-predict yet crucial asset for investment decision-making. This study aims to forecast gold prices by comparing the performance of Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) forecasting models through three testing scenarios: single-step, multi-step forecasting, and rolling forecasting. This study utilizes 40 years of historical gold price data obtained from the public source Kaggle. The ARIMA model was implemented on stationary data, while LSTM was optimized with additional lag, volatility, and momentum features. Experimental results indicate that in the single-step scenario, both models produced equivalent accuracy with a MAPE below 1%. In the multi-step scenario, LSTM significantly outperformed ARIMA with a MAPE of 1.89% compared to 3.54%. In the rolling scenario, LSTM again performed better with a MAPE of 1.83% versus 3.52% for ARIMA. Conversely, ARIMA consistently recorded higher Mean Directional Accuracy (MDA) values across all scenarios, reaching 57.30% in the rolling forecast compared to LSTM's 46.07%, indicating ARIMA's advantage in identifying trend direction. This study concludes that the LSTM approach is more optimal for achieving numerical prediction precision over medium-term horizons, while the statistical ARIMA method is more reliable for accurately projecting market movement direction.]]>
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