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：随着基于深度学习生成模型的最新进展，其卓越性能在时间序列领域的应用很快得到发展。用于处理时间序列的深度神经网络高度依赖于训练数据集的规模与一致性。然而在现实世界中，这些特征通常不足，数据集往往规模有限且需满足特定约束条件。因此，增加数据量的有效途径是采用数据增强技术，包括添加噪声、进行排列变换或生成合成新数据。本文系统梳理了该领域现有研究前沿，全面概述现有算法，并提出最具相关性的研究分类体系。作为核心环节，本文评估了不同变体方案的效率，同时分析了评估性能的各类指标及每种模型面临的主要问题。本研究最终旨在总结效果更优领域的发展历程与性能表现，为该领域的未来研究者提供指引。