Time series classification is widely used in many fields, but it often suffers from a lack of labeled data. To address this, researchers commonly apply data augmentation techniques that generate synthetic samples through transformations such as jittering, warping, or resampling. However, with an increasing number of available augmentation methods, it becomes difficult to choose the most suitable one for a given task. In many cases, this choice is based on intuition or visual inspection. Assessing the impact of this choice on classification accuracy requires training models, which is time-consuming and depends on the dataset. In this work, we adopt a generative model perspective and evaluate augmentation methods prior to training any classifier, using metrics that quantify both fidelity and diversity of the generated samples. We benchmark 22 augmentation techniques on 131 public datasets using eight metrics. Our results provide a practical and efficient way to compare augmentation methods without relying solely on classifier performance. The source code is publicly available: https://github.com/MSD-IRIMAS/Data-Augmentation-4-TSC