An xLSTM–XGBoost Ensemble Model for Forecasting Non-Stationary and Highly Volatile Gasoline Price

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...

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Bibliographic Details
Main Authors: Fujiang Yuan, Xia Huang, Hong Jiang, Yang Jiang, Zihao Zuo, Lusheng Wang, Yuxin Wang, Shaojie Gu, Yanhong Peng
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Computers
Subjects:
xLSTM
XGBoost
LSTM
gasoline price
Online Access:https://www.mdpi.com/2073-431X/14/7/256
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