Empirical mode decomposition using deep learning model for financial market forecasting.

Area of Science:

• * Computational Finance
• * Data Science

Background:

• * Financial market forecasting is crucial but challenging due to noisy, non-stationary data.
• * Deep learning excels at extracting features from large datasets without prior knowledge.
• * Existing methods struggle with the inherent complexity of financial time series.

Purpose of the Study:

• * To propose a deep learning model for autonomous statistical rule mining in financial data.
• * To guide financial market transactions using empirical mode decomposition (EMD) and back-propagation neural networks (BPNN).
• * To enhance prediction accuracy for nonlinear and non-stationary financial time series.

Main Methods:

• * Financial data was decomposed using Empirical Mode Decomposition (EMD) to capture intrinsic wave patterns.
• * A deep learning model, incorporating Back-Propagation Neural Networks (BPNN), was developed.
• * Particle Swarm Optimization (PSO) was used to analyze and optimize the financial market data.

Main Results:

• * The EMD-based deep learning model demonstrated excellent predictive performance.
• * The model successfully forecasted future financial market price trends.
• * Trading signals were generated based on predefined confidence levels.

Conclusions:

• * The proposed EMD-based deep learning model effectively handles noisy and non-stationary financial data.
• * This approach offers a robust method for improving financial market prediction accuracy.
• * The model provides a reliable tool for guiding financial trading decisions.