An xLSTM–XGBoost Ensemble Model for Forecasting Non-Stationary and Highly Volatile Gasoline Price
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...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| 格式: | Article |
| 語言: | 英语 |
| 出版: |
MDPI AG
2025-06-01
|
| 叢編: | Computers |
| 主題: | |
| 在線閱讀: | https://www.mdpi.com/2073-431X/14/7/256 |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| _version_ | 1839616216309694464 |
|---|---|
| author | Fujiang Yuan Xia Huang Hong Jiang Yang Jiang Zihao Zuo Lusheng Wang Yuxin Wang Shaojie Gu Yanhong Peng |
| author_facet | Fujiang Yuan Xia Huang Hong Jiang Yang Jiang Zihao Zuo Lusheng Wang Yuxin Wang Shaojie Gu Yanhong Peng |
| author_sort | Fujiang Yuan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-83aa90cdf79c4197b3dcf35e3fbdcc88 |
| institution | Matheson Library |
| issn | 2073-431X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-83aa90cdf79c4197b3dcf35e3fbdcc882025-07-25T13:18:59ZengMDPI AGComputers2073-431X2025-06-0114725610.3390/computers14070256An xLSTM–XGBoost Ensemble Model for Forecasting Non-Stationary and Highly Volatile Gasoline PriceFujiang Yuan0Xia Huang1Hong Jiang2Yang Jiang3Zihao Zuo4Lusheng Wang5Yuxin Wang6Shaojie Gu7Yanhong Peng8School of Computer Science and Technology, Taiyuan Normal University, Jinzhong 030619, ChinaCollege of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, ChinaCollege of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, ChinaCollege of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, ChinaCollege of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, ChinaCollege of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, ChinaSchool of Energy and Power, Jiangsu University of Science and Technology, Zhenjiang 212100, ChinaMagnesium Research Center, Kumamoto University, Kumamoto 860-8555, JapanCollege of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, ChinaHigh-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.https://www.mdpi.com/2073-431X/14/7/256xLSTMXGBoostLSTMgasoline price |
| spellingShingle | Fujiang Yuan Xia Huang Hong Jiang Yang Jiang Zihao Zuo Lusheng Wang Yuxin Wang Shaojie Gu Yanhong Peng An xLSTM–XGBoost Ensemble Model for Forecasting Non-Stationary and Highly Volatile Gasoline Price Computers xLSTM XGBoost LSTM gasoline price |
| title | An xLSTM–XGBoost Ensemble Model for Forecasting Non-Stationary and Highly Volatile Gasoline Price |
| title_full | An xLSTM–XGBoost Ensemble Model for Forecasting Non-Stationary and Highly Volatile Gasoline Price |
| title_fullStr | An xLSTM–XGBoost Ensemble Model for Forecasting Non-Stationary and Highly Volatile Gasoline Price |
| title_full_unstemmed | An xLSTM–XGBoost Ensemble Model for Forecasting Non-Stationary and Highly Volatile Gasoline Price |
| title_short | An xLSTM–XGBoost Ensemble Model for Forecasting Non-Stationary and Highly Volatile Gasoline Price |
| title_sort | xlstm xgboost ensemble model for forecasting non stationary and highly volatile gasoline price |
| topic | xLSTM XGBoost LSTM gasoline price |
| url | https://www.mdpi.com/2073-431X/14/7/256 |
| work_keys_str_mv | AT fujiangyuan anxlstmxgboostensemblemodelforforecastingnonstationaryandhighlyvolatilegasolineprice AT xiahuang anxlstmxgboostensemblemodelforforecastingnonstationaryandhighlyvolatilegasolineprice AT hongjiang anxlstmxgboostensemblemodelforforecastingnonstationaryandhighlyvolatilegasolineprice AT yangjiang anxlstmxgboostensemblemodelforforecastingnonstationaryandhighlyvolatilegasolineprice AT zihaozuo anxlstmxgboostensemblemodelforforecastingnonstationaryandhighlyvolatilegasolineprice AT lushengwang anxlstmxgboostensemblemodelforforecastingnonstationaryandhighlyvolatilegasolineprice AT yuxinwang anxlstmxgboostensemblemodelforforecastingnonstationaryandhighlyvolatilegasolineprice AT shaojiegu anxlstmxgboostensemblemodelforforecastingnonstationaryandhighlyvolatilegasolineprice AT yanhongpeng anxlstmxgboostensemblemodelforforecastingnonstationaryandhighlyvolatilegasolineprice AT fujiangyuan xlstmxgboostensemblemodelforforecastingnonstationaryandhighlyvolatilegasolineprice AT xiahuang xlstmxgboostensemblemodelforforecastingnonstationaryandhighlyvolatilegasolineprice AT hongjiang xlstmxgboostensemblemodelforforecastingnonstationaryandhighlyvolatilegasolineprice AT yangjiang xlstmxgboostensemblemodelforforecastingnonstationaryandhighlyvolatilegasolineprice AT zihaozuo xlstmxgboostensemblemodelforforecastingnonstationaryandhighlyvolatilegasolineprice AT lushengwang xlstmxgboostensemblemodelforforecastingnonstationaryandhighlyvolatilegasolineprice AT yuxinwang xlstmxgboostensemblemodelforforecastingnonstationaryandhighlyvolatilegasolineprice AT shaojiegu xlstmxgboostensemblemodelforforecastingnonstationaryandhighlyvolatilegasolineprice AT yanhongpeng xlstmxgboostensemblemodelforforecastingnonstationaryandhighlyvolatilegasolineprice |