Expanding the Potential of Financial Time Series Data: High-Performance Data Augmentation and Future Scenario Simulation Strategies Based on Diffusion Models
The scarcity of financial time series data, privacy concerns, and limitations in capturing complex multi-modal distributions have hindered modern financial AI. Diffusion models offer a powerful generative AI solution that addresses these challenges by synthesizing realistic and diverse data to maximize the robustness of model training, and by simulating complex future scenarios with unprecedented accuracy. This deep generative capability, going beyond simple statistical methods, is a game-changer that will shift the paradigm of financial AI.
1. Inherent Challenges of Financial Data and the Need for New Solutions
Financial data, by its nature, presents unique challenges distinct from general data. Non-stationarity, low signal-to-noise ratio, fat tails, and sudden market regime shifts are major factors that degrade the performance of traditional statistical models or simple machine learning models. Furthermore, the following issues hinder data utilization:
- Data Scarcity and Access Restrictions: High-quality financial data is proprietary, and it is difficult to secure sufficient quantities due to regulations (e.g., GDPR) and high acquisition costs.
- Bias of Historical Data: Existing datasets primarily reflect past market conditions, thus limiting their ability to predict new future patterns or black swan events.
- Difficulty in Learning Complex Distributions: Traditional data augmentation techniques like bootstrapping or simple noise addition fail to adequately capture the complex dependencies and multi
