Gold Price Prediction with LSTM, MLP, and GWO
Analysis
This paper addresses the challenging task of gold price forecasting using a hybrid AI approach. The combination of LSTM for time series analysis, MLP for integration, and GWO for optimization is a common and potentially effective strategy. The reported 171% return in three months based on a trading strategy is a significant claim, but needs to be viewed with caution without further details on the strategy and backtesting methodology. The use of macroeconomic, energy market, stock, and currency data is appropriate for gold price prediction. The reported MAE values provide a quantitative measure of the model's performance.
Key Takeaways
- •Proposes a hybrid AI model (LSTM-MLP) for gold price prediction.
- •Employs Gray Wolf Optimization (GWO) for hyperparameter tuning.
- •Claims a 171% return in three months based on a trading strategy (details needed).
- •Uses a comprehensive dataset including macroeconomic and market data.
- •Provides MAE values for daily and monthly price predictions.
“The proposed LSTM-MLP model predicted the daily closing price of gold with the Mean absolute error (MAE) of $ 0.21 and the next month's price with $ 22.23.”