THE COMPARISON OF ARIMA AND RNN FOR FORECASTING GOLD FUTURES CLOSING PRICES

Abstract

In the financial markets, accurately forecasting the closing prices of gold futures is crucial for investors and analysts. Traditional methods like ARIMA (Autoregressive Integrated Moving Average) have been widely used for this purpose, particularly for their effectiveness in short-term stable data forecasting. However, the inherent complexity and dynamic nature of financial data, coupled with trends and seasonal patterns, present significant challenges for long-term forecasting with ARIMA. Conversely, advanced methods such as Recurrent Neural Networks (RNN) have shown promise in handling these complexities and providing reliable long-term forecasts. This research seeks to evaluate and compare the performance of ARIMA and RNN in forecasting daily gold futures closing prices using forecast accuracy tests namely RMSE and MAPE, aiming to identify the optimal method that balances accuracy, stability, and adaptability to trends and seasonal variations in the financial market. The daily data for this analysis is sourced from Investing.com (https://www.investing.com).