Entropy-Driven Adaptive Decomposition and Linear-Complexity Score Attention: An AI-Powered Framework for Crude Oil...

Area of Science:

• Energy Markets
• Financial Econometrics
• Artificial Intelligence

Background:

• Crude oil markets exhibit financial entropy, characterized by uncertainty, multi-scale fluctuations, and nonlinear transitions, challenging traditional prediction models.
• Accurate energy price forecasting is crucial for market stability and economic planning.

Purpose of the Study:

• To develop an advanced artificial intelligence-driven hybrid prediction framework for improving energy financial market forecasting accuracy.
• To address the complexities of financial entropy in crude oil markets.

Main Methods:

• Adaptive Lagrange Annealing (ALA) to optimize Variational Mode Decomposition (VMD) hyperparameters for reduced entropy sub-modes.
• Convolutional Neural Network (CNN) autoencoder with a score attention mechanism for parallel sub-mode prediction, capturing volatility.
• An aggregation component to generate the final price prediction.

Main Results:

• The ALA-VMD-CASA framework demonstrated superior performance over benchmark models (ARIMA, LSTM, Transformer, etc.) in predicting Brent crude oil spot prices.
• Achieved over 63% reduction in mean square error compared to the best standalone model.
• Exhibited a perfect win rate in expanding-window evaluations, confirming robustness.

Conclusions:

• The ALA-VMD-CASA framework effectively models financial entropy and enhances energy price forecasting accuracy and robustness.
• The hybrid approach offers a significant advancement over existing prediction methodologies for volatile energy markets.