Assessing the Effectiveness of Generative Adversarial Networks for Time Series Data Augmentation

This study explores the effectiveness of Generative Adversarial Networks (GANs) for augmenting time series data, a key task in domains like finance, healthcare, and environmental research. Time series data augmentation plays a vital role in enhancing the performance of various AI models by expanding the training dataset and improving model accuracy. In this research, GANs are employed to generate synthetic time series data that closely mimics the characteristics of real-world datasets. Traditional methods often rely on limited series datasets for augmentation, but GANs offer a promising alternative. We present and analyze the findings of a pilot study that compares two time series augmentation approaches: transformation-based methods and model blending techniques. Our results highlight the potential of GANs in this field and suggest future directions for research on time series data augmentation.

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