An xLSTM–XGBoost Ensemble Model for Forecasting Non-Stationary and Highly Volatile Gasoline Price - Fujiang Yuan; Xia Huang; Hong Jiang; Yang Jiang; Zihao Zuo; Lusheng Wang; Yuxin Wang; Shaojie Gu; Yanhong Peng (Computers, 2025)
High-frequency fluctuations in the international crude oil market have led to multilevel characteristics in China’s domestic refined oil pricing mechanism. To address the poor fitting performance of single deep learning models on oil price data, which hampers accurate gasoline price prediction, this paper proposes a gasoline price prediction method based on a combined xLSTM–XGBoost model. Using gasoline price data from June 2000 to November 2024 in Sichuan Province as a sample, the data are decomposed via STL decomposition to extract trend, residual, and seasonal components. The xLSTM model is then employed to predict the trend and seasonal components, while XGBoost predicts the residual component. Finally, the predictions from both models are combined to produce the final forecast. The experimental results demonstrate that the proposed xLSTM–XGBoost model reduces the MAE by 14.8% compared to the second-best sLSTM–XGBoost model and by 83% compared to the traditional LSTM model, significantly enhancing prediction accuracy.