Metal Futures Prices Prediction Based on LSTM-GRU Hybrid Model

Abstract

Accurately predicting the prices of metal futures such as aluminum, copper, lead, nickel, tin, and zinc is crucial for investors, manufacturers, and policymakers due to their significant impact on economic activities and industrial processes. This paper presents a comprehensive study on the prediction of futures prices using a hybrid model combining Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks. The research utilizes historical data from 2014 to 2024, including daily open, high, low, close prices, trading volumes, and return. Data preprocessing involved interpolation for missing values and normalization using MinMaxScaler. The models performance was evaluated with various accuracy metrics, such as Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Error (MAE) ect, showing high accuracy for lead, tin, and zinc futures in both train and test datasets. However, the model performed well only on the train data for aluminum, copper, and nickel, indicating the need for more iterations, additional factors, or the consideration of unexpected events. The results highlight the efficacy of the LSTM-GRU hybrid model in futures price prediction, with significant implications for informed investment decisions. Future research should consider cross-market analysis and additional factors to enhance model accuracy further.