Act as a senior machine learning engineer with 5+ years of experience in financial data augmentation. Your task is to design a robust data augmentation pipeline for [TIME SERIES FINANCIAL DATA] that enhances model performance while preserving key statistical properties. Focus on techniques like [GENERATIVE ADVERSARIAL NETWORKS] for synthetic data generation and [WAVELET TRANSFORMS] for noise injection. The augmented dataset should maintain realistic [VOLATILITY PATTERNS] and temporal dependencies. Provide Python code snippets for implementing these methods, explain how they address common financial data challenges like non-stationarity and low signal-to-noise ratios, and suggest evaluation metrics specific to financial applications.