Data Augmentation techniques in time series domain: A survey and taxonomy. (arXiv:2206.13508v1 [cs.LG])

With the latest advances in deep learning 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 depend heavily on
the breadth and consistency of the datasets used in training. These types of
characteristic are not usually abundant in the real world, where they are
usually limited and often with privacy constraints that must be guaranteed.
Therefore, an effective way is to increase the number of data using gls{da}
techniques, either by adding noise or permutations and by generating new
synthetic data. It is systematically review 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 researches. The efficiency of the different variants will
be evaluated; as a vital part of the process, the different metrics to evaluate
the performance and the main problems concerning each model will be analysed.
The ultimate goal 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.



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