Tree-Based Models Versus Neural Network in Predicting Energy​ Commodities Futures DOI
Xibin Zhang,

Yihe Qian,

Yang Zhang

et al.

Published: Jan. 1, 2024

Language: Английский

A Novel Forecasting Framework Leveraging Large Language Model and Machine Learning for Methanol Price DOI
Wenyang Wang,

Yuping Luo,

Mingrui Ma

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135123 - 135123

Published: Feb. 1, 2025

Language: Английский

Citations

2
Receiver or transmitter? Unlocking the role of green technology innovation in sustainable development, energy, and carbon markets DOI
Kai‐Hua Wang,

Cui-Ping Wen,

Baochang Xu

et al.

Technology in Society, Journal Year: 2024, Volume and Issue: 79, P. 102703 - 102703

Published: Aug. 30, 2024

Language: Английский

Citations

13
Macroeconomic and sectoral effects of natural gas price: Policy insights from a macroeconometric model DOI Creative Commons
Fakhri Hasanov,

Muhammad Javid,

Jeyhun I. Mikayilov

et al.

Energy Economics, Journal Year: 2025, Volume and Issue: unknown, P. 108233 - 108233

Published: Jan. 1, 2025

Language: Английский

Citations

0
CTr2L: A Novel Carbon Trading Transfer Learning Framework for the Price Volatility Forecasting DOI

Jianshu Hao,

Xinyi Bao,

Shanjunqi Guan

et al.

Journal of Systems Science and Systems Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: May 9, 2025

Language: Английский

Citations

0
Examining the role of geopolitical risk on energy market tail risk forecasting using explainable machine learning DOI
Mohammad Ashraful Ferdous Chowdhury, Mohammad Abdullah, Emmanuel Joel Aikins Abakah

et al.

Journal of commodity markets, Journal Year: 2025, Volume and Issue: unknown, P. 100478 - 100478

Published: May 1, 2025

Language: Английский

Citations

0
A Variational-Mode-Decomposition-Cascaded Long Short-Term Memory with Attention Model for VIX Prediction DOI Creative Commons

Do-Hyeon Kim,

Dong-Jun Kim,

Sun‐Yong Choi

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(10), P. 5630 - 5630

Published: May 18, 2025

Financial time-series forecasting presents a significant challenge due to the inherent volatility and complex patterns in market data. This study introduces novel framework that integrates Variational Mode Decomposition (VMD) with Cascaded Long Short-Term Memory (LSTM) network enhanced by an Attention mechanism. The primary objective is enhance predictive accuracy of VIX, key measure uncertainty, through advanced signal processing deep learning techniques. VMD employed as preprocessing step decompose financial data into multiple Intrinsic Functions (IMFs), effectively isolating short-term fluctuations from long-term trends. These decomposed features serve inputs LSTM model mechanism, which enables capture critical temporal dependencies, thereby improving performance. Experimental evaluations using VIX S&P 500 January 2020 December 2024 demonstrate superior capability proposed compared seven benchmark models. results highlight effectiveness combining decomposition techniques Attention-based architectures for forecasting. research contributes field introducing hybrid improves accuracy, enhances robustness against fluctuations, underscores importance mechanisms capturing essential dynamics.

Language: Английский

Citations

0
Can the sentiment of the official media predict the return volatility of the Chinese crude oil futures? DOI
Zhiwei Xu,

Saixiong Gan,

Xia Hua

et al.

Energy Economics, Journal Year: 2024, Volume and Issue: 140, P. 107967 - 107967

Published: Oct. 18, 2024

Language: Английский

Citations

1
A seasonal grey model for forecasting energy imports demand from information differences perspective DOI
Weijie Zhou, Jiaxin Chang,

Weizhen Zuo

et al.

Applied Mathematical Modelling, Journal Year: 2024, Volume and Issue: unknown, P. 115907 - 115907

Published: Dec. 1, 2024

Language: Английский

Citations

1
Unveiling the dynamics: exploring the impact of oil-gas relationships on natural gas bubbles DOI
Hao Zhai, Kai‐Hua Wang

Energy Sources Part B Economics Planning and Policy, Journal Year: 2024, Volume and Issue: 19(1)

Published: June 28, 2024

Natural gas plays an increasingly important role in the energy landscape, and its price experiences significant volatility is easily affected by oil due to substitutability. Therefore, this study investigates emergence of natural bubbles terms relationship between prices (ROPGP) during sample period from January 1997 2023. Using a theoretical framework covering that examines structural dependencies, analysis combines subsample rolling window causality tests, Generalized Supremum Augmented Dickey-Fuller (GSADF) bubble detection methods, logistic regression models. The empirical results indicate Europe's diversified policies, ROPGP has adverse effect on Europe. In Japan, however, positively influences scarcity indigenous resources reliance contracts linked imported oil. Conversely, US, does not significantly impact bubbles, which attributed country's self-sufficiency production. These findings highlight differing impacts markets various regions, shaped their unique structures, geopolitical factors.

Language: Английский

Citations

0
Tree-Based Models Versus Neural Network in Predicting Energy​ Commodities Futures DOI
Xibin Zhang,

Yihe Qian,

Yang Zhang

et al.

Published: Jan. 1, 2024

Language: Английский

Citations

0