  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.
