The Comparison Between Random Forest and LSTM Models Based on the Gold Price Prediction

The gold price forecast is crucial for financial institutions, investors, mining firms, and associated enterprises. Precise price prediction is especially important in the gold market because of its volatility and unpredictability; this has important consequences for strategy formulation and decision making. In order to give practitioners a better prediction technique to increase the accuracy of market trading, the aim of this study is to assess the predictive effectiveness of the two models in predicting the gold market. The study forecasts gold prices using the Random Forest and Long Short-Term Memory (LSTM) models, comparing their performances using five assessment metrics. The end findings prove that the Random Forest method outperforms the LSTM model in comparison. In particular, the Random Forest model predicts more accurately and with less inaccuracy. The study finds that, while the Random Forest model is more accurate, both the LSTM model and the Random Forest model are appropriate techniques for gold market projections. To increase the precision and consistency of the predictions, future studies will keep refining the LSTM model and look for causes for its subpar performance.