Research in IEEE Big Data Conference 2024

Research Overview

This research paper addresses the significant challenges in generating high-quality synthetic financial time series data. Financial markets exhibit unique characteristics—non-stationarity, regime changes, and complex correlations—that make traditional generative models insufficient for creating realistic synthetic data.

Key Innovations

Our approach introduces several key innovations:

1. Regime-Aware Generation: A novel architecture that explicitly models different market regimes (bull, bear, high volatility) and transitions between them
2. Preservation of Statistical Properties: Techniques to ensure generated data maintains critical statistical properties of financial time series
3. Multi-Resolution Temporal Dependencies: Capturing dependencies across multiple time scales (short-term, medium-term, long-term)
4. Reality Metrics: New evaluation metrics specifically designed for financial time series data quality assessment

Applications

The improved generative models enable:

• More robust backtesting of trading strategies
• Enhanced risk management through more realistic stress testing scenarios
• Better training data for machine learning models in finance
• Privacy-preserving data sharing for collaborative research

Future Directions

Our ongoing work focuses on incorporating additional market factors and expanding the model to handle multi-asset correlations and alternative data sources.