<![CDATA[Gold (XAU/USD) is one of the most significant global commodities, often viewed as a safe-haven asset amid economic and political uncertainty. Accurate forecasting of gold prices is crucial for investors and policymakers in formulating strategic financial decisions. This study aims to compare the performance of the Single Exponential Smoothing (SES) and Autoregressive Integrated Moving Average (ARIMA) methods in forecasting gold prices using historical datasets from Kaggle, Investing.com, and ForexSB covering the period from January 2020 to September 2024. The analysis was conducted using Python on Google Colaboratory with evaluation metrics including Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results show that both SES and ARIMA effectively captured the upward trend of gold prices, with SES achieving slightly better accuracy across all datasets. The lowest MAPE value of 0.62% was obtained using SES on the ForexSB dataset, indicating an excellent forecasting performance. Therefore, SES is considered more efficient and reliable for non-seasonal time series with stable trends]]>
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