Financial time series forecasting with a hybrid VMD-CSA-BiT framework.

Area of Science:

• Quantitative Finance
• Computational Economics
• Machine Learning

Background:

• Financial time series forecasting is challenged by nonlinearity, non-stationarity, and noise.
• Existing models often struggle with complex market dynamics and data volatility.

Purpose of the Study:

• To propose an integrated framework, VMD-CSA-BiT, for robust financial time series forecasting.
• To enhance prediction accuracy and stability in financial markets.

Main Methods:

• Variational Mode Decomposition (VMD) to decompose time series into intrinsic mode functions.
• Convolutional Self-Attention (CSA) to refine time-step representations.
• Bidirectional Transformers (BiT) to model long-term temporal dependencies.

Main Results:

• VMD-CSA-BiT demonstrated consistent performance improvements over benchmark models.
• Achieved significant reductions in key error metrics for financial forecasting.
• Visual analyses confirmed predictions align well with actual market movements.

Conclusions:

• VMD-CSA-BiT is a promising and effective approach for financial time series forecasting.
• The framework offers superior accuracy and stability compared to existing methods.
• Future work includes architectural optimizations and broader financial applications.