Feb 27, 2020 � In this paper, we systematically review different data augmentation methods for time series. We propose a taxonomy for the reviewed methods, and�...

Time-frequency analysis is a widely applied technique for time series analysis, which can be utilized as an appropriate input features in deep neural networks.

This paper systematically review different data augmentation methods for time series, and proposes a taxonomy for the reviewed methods, and provides a�...

Mar 24, 2023 � Deep neural networks used to work with time series heavily depend on the size and consistency of the datasets used in training. These features�...

Jan 24, 2024 � Time Series Data Augmentation for Deep Learning: A Survey. Deep learning performs remarkably well on many time series analysis tasks recently.

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�...

We propose a taxonomy and outline the four families in time series data augmentation, including transformation-based methods, pattern mixing, generative models,�...

This propose a taxonomy for the reviewed methods, and then provide a structured review for these methods by highlighting their strengths and limitations.

May 13, 2024 � Data augmentation approaches are used to generate data and improve models' performance. This study investigates the efficacy of machine learning�...

In machine learning, data augmentation is the process of generating synthetic data samples that will be used to train the model to improve the performance�...