Time series data augmentation method of small sample based on optimized generative adversarial network

In the time series classification task, for the problem that small samples have difficulty in capturing sequence features cause deep learning models have low classification accuracy. This article proposes a time series data augmentation method based on gated recurrent unit (GRU)-convolutional neural network (CNN) structure of generative adversarial network, denoted as GC-GAN. Specifically, the structure of GRU network in series with CNN is used as the generator of GAN model, while the discriminator part uses two-layer convolution network. In order to avoid the disappearance of gradient, Wasserstein distance is used to measure the distance between the generated distribution and the true distribution, and penalties are introduced in the loss function. Finally, the particle swarm algorithm is used to optimize the hidden variable input of the generator to approximate the real sequence to the greatest extent and obtain the best sample. The experimental results on the UCR time series dataset show that the long short-term memory network classification accuracy has been effectively improved after using the GC-GAN model, with a maximum improvement of 4.4%, indicating that the method proposed in this article has better data augmentation capabilities.