Data Augmentation techniques in time series domain: A survey and taxonomy
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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 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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