Modeling world gold price by deep learning methods

Abstract

The gold price trend in the market is the most important factor for gold investors and is the basis for earning profits, which motivates researchers to explore gold price forecasting methods. Accurate gold price prediction is essential for economic and currency markets, which can effectively grasp price change trends and reduce the impact of gold market fluctuations. Therefore, intelligent prediction models must be applied to predict prices, and forecast accuracy is one of the most critical factors when choosing a forecasting method.The article presents the application of Long Short Term Memory (LSTM) deep learning models, Gated Recurrent Unit (GRU) models, and Generative AI models for forecasting world gold prices.We have performed Data collection and processing, Model building and training, Forecasting and Error Evaluation, and Comparison of results with the experimental model on the gold price dataset from July 2010 to March 2024. We conducted a series of experiments and model evaluations and analyzed time series volatility factors to find the best results to improve forecasting performance. The results show that the proposed method can cope with fluctuations in gold price series over time and provides good prediction accuracy, so it can be considered a suitable tool for financial forecasting problems