Natural Gas Futures Price Prediction Based on Variational Mode Decomposition–Gated Recurrent Unit/Autoencoder/Multilayer Perceptron–Random Forest Hybrid Model

Sustainability, Год журнала: 2025, Номер 17(6), С. 2492 - 2492

Опубликована: Март 12, 2025

Forecasting natural gas futures prices can help to promote sustainable global energy development, as the efficient use of a clean source has become key growing demand for development. This study proposes new hybrid model prediction prices. Firstly, original price series is decomposed, and subsequences, along with influencing factors, are used input variables. Secondly, variables grouped based on their correlations output variable, different models employed forecast each group. A gated recurrent unit (GRU) captures long-term dependence, an autoencoder (AE) downscales extracts features, multilayer perceptron (MLP) maps complex relationships. Subsequently, random forest (RF) integrates results obtain final prediction. The experimental show that mean absolute error (MAE) 0.32427, percentage (MAPE) 10.17428%, squared (MSE) 0.46626, root (RMSE) 0.68283, R-squared (R²) 93.10734%, accuracy rate (AR) 89.82572%. demonstrate proposed decomposition–selection–prediction–integration framework reduces errors, enhances stability through multiple experiments, improves efficiency accuracy, provides insights forecasting.

Язык: Английский

Analysis of Self-Similarity in Short and Long Movements of Crude Oil Prices by Combination of Stationary Wavelet Transform and Range-Scale Analysis: Effects of the COVID-19 Pandemic and Russia-Ukraine War

Fractal and Fractional, Год журнала: 2025, Номер 9(3), С. 176 - 176

Опубликована: Март 14, 2025

This paper examines the self-similarity (long memory) in prices of crude oil markets, namely Brent and West Texas Instruments (WTI), by means fractals. Specifically, price series are decomposed stationary wavelet transform (SWT) to obtain their short long oscillations. Then, Hurst exponent is estimated from each resulting oscillation rescaled analysis (R/S) represent hidden fractals original series. The performed during three periods: calm period (before COVID-19 pandemic), pandemic, Russia-Ukraine war. In summary, WTI exhibited significant increases persistence movements pandemic addition, they showed a increase anti-persistence decrease It concluded that both war significantly affected memory prices.

Язык: Английский

Application of Dynamic Weight Mixture Model Based on Dual Sliding Windows in Carbon Price Forecasting

Energies, Год журнала: 2024, Номер 17(15), С. 3662 - 3662

Опубликована: Июль 25, 2024

As global climate change intensifies, nations around the world are implementing policies aimed at reducing emissions, with carbon-trading mechanisms emerging as a key market-based tool. China has launched markets in several cities, achieving significant trading volumes. Carbon-trading encompass cap-and-trade and voluntary markets, influenced by various factors, including policy changes, economic conditions, energy prices, fluctuations. The complexity of these coupled nonlinear non-stationary nature carbon makes forecasting substantial challenge. This paper proposes dynamic weight hybrid model based on dual sliding window approach, effectively integrating multiple models such LSTM, Random Forests, LASSO. facilitates thorough analysis influences policy, market dynamics, technological advancements, climatic conditions pricing. It serves potent tool for predicting price fluctuations offers valuable decision support to stakeholders market, ultimately aiding efforts towards emission reduction sustainable development goals.

Язык: Английский