Prediction and Analysis of Soybean Oil Futures Prices Based on the LSTM-Transformer Model

本文运用长短期记忆网络(LSTM)和变换器(Transformer )混合模型预测豆油期货价格。通过数据处理、模型构建、训练评估与结果分析，发现该模型能有效地捕捉价格序列特征，在测试集上展现出良好预测性能，在平均绝对误差(MAE)，均方根误差(RMSE)，平均绝对百分比误差(MAPE)，相对均方根误差(RRMSE)等指标上表现优异，与单独的Transformer模型和LSTM模型预测相比，精确度有明显的提高。这表示该模型在期货价格预测领域具有一定的应用潜力。This paper uses a hybrid model of Long Short-Term Memory (LSTM) and Transformer to predict the futures price of soybean oil. Through data processing, model construction, training evaluation, and result analysis, it is found that this model can effectively capture the characteristics of the price sequence. It demonstrates excellent prediction performance on the test set and shows outstanding performance in indicators such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Relative Root Mean Square Error (RRMSE). Compared with the predictions of the individual LSTM model and Transformer model, the accuracy has been significantly improved. This indicates that this model has certain application potential in the field of futures price prediction.