With the latest advances in Deep Learning-based} generative models, it has not taken long to take advantage of their remarkable performance in the area of time series. Deep neural networks used to work with time series heavily depend on the size and consistency of the datasets used in training. These features are not usually abundant in the real world, where they are usually limited and often have constraints that must be guaranteed. Therefore, an effective way to increase the amount of data is by using Data Augmentation techniques, either by adding noise or permutations and by generating new synthetic data. This work systematically reviews the current state-of-the-art in the area to provide an overview of all available algorithms and proposes a taxonomy of the most relevant research. The efficiency of the different variants will be evaluated as a central part of the process, as well as the different metrics to evaluate the performance and the main problems concerning each model will be analysed. The ultimate aim of this study is to provide a summary of the evolution and performance of areas that produce better results to guide future researchers in this field.

翻译：随着基于深度学习的生成模型最新进展，其在时间序列领域的卓越性能很快得到应用。用于处理时间序列的深度神经网络高度依赖训练数据集的大小与一致性。然而现实世界中这些特征通常并不充足，数据集往往规模有限且必须满足特定约束条件。因此，通过数据增强技术——包括添加噪声、序列置换以及合成新数据——来有效扩充数据量至关重要。本文系统梳理了该领域的最新研究进展，提供所有可用算法概览，并提出最具相关性研究的分类体系。作为核心分析过程，我们将评估不同变体的效率，分析各类性能评价指标，并探讨各模型面临的主要问题。本研究最终目标是对该领域中表现优异的演进方向与性能表现进行总结，为后续研究者提供指导。