The application of Generative Adversarial Networks (GANs) has revolutionized time series analysis, enabling tasks such as data synthesis, imputation, forecasting, and anomaly detection. This paper analyzes modifications to GAN architectures specifically designed for time series data. A review of recent literature is conducted, including studies on General Purpose Time Series Synthesis with Generative Adversarial Networks (GT-GAN), Time-series Generative Adversarial Network (TimeGAN), Conditional Sig-Wasserstein GAN (SigCWGAN), and others. This analysis highlights the versatility and effectiveness of modified GANs in addressing time series challenges across diverse domains (e.g., energy, finance, healthcare). Key trends, challenges, and successes are discussed, with emphasis placed on advancements in anomaly detection, data imputation, and forecasting. Future research directions are outlined, including cross-domain adaptation, enhanced data synthesis, improved interpretability, and the development of standardized evaluation metrics. This work bridges existing gaps in time series GAN research, paving the way for practical applications across various industries.