Abstract
The prediction of gold prices is of paramount importance in the financial sector due to gold's role as a safe-haven asset during periods of global economic uncertainty and market volatility. Accurately forecasting gold prices can help investors, policymakers, and businesses make informed decisions regarding investments, risk management, and hedging strategies. This paper utilizes ARIMA (Auto Regressive Integrated Moving Average) and ETS (Error-Trend-Seasonality) time series models to forecast future gold prices. Both models have been applied extensively in financial forecasting, each with unique strengths in handling different time series characteristics. This study aims to compare the prediction accuracy of these two models using Root Mean Square Error (RMSE) as the evaluation metric. The results show that while both models are effective in capturing the underlying trends in gold prices, the ARIMA model outperforms the ETS model with a lower RMSE of 56.80 compared to 61.51 for the ETS model. This comparative analysis contributes to the field of gold price forecasting by identifying the strengths and limitations of each model and providing insights into their applications in the context of financial time series forecasting.
Published Version
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