SIGAN-CNN: Convolutional Neural Network Based Stepwise Improving Generative Adversarial Network for Time Series Classification of Small Sample Size

With the development of artificial intelligence technology, time series classification has attracted greater attention. Various methods have been considered for using deep learning models to perform the task. Training such models, however, requires a large amount of high-quality labeled samples, which may not be available due to the expensive cost of collection. Therefore, we proposed a novel framework named Convolutional Neural Network based Stepwise Improving Generative Adversarial Network (SIGAN-CNN) to solve this problem by generating more samples from the original distribution. We designed the structure of the generator and discriminator of SIGAN to be suitable for time series data. We also explored a method for extracting trend information from time series data to obtain trend samples for stepwise training. Therefore, the generator can fit the original time series data with subtle variations. More importantly, SIGAN can improve the diversity of the generated samples and the stability of the training process to achieve a higher quality of the generated samples. The generated samples are then combined with CNN based time series data classification methods to improve the classification performance of time series data. Especially, the combination of SIGAN and Multi-scale Attention Convolutional Neural Network (MACNN) is suitable for this task. We conducted a comprehensive evaluation of 8 standard datasets from various domains. The results demonstrate that SIGAN-MACNN achieves the best performance and outperforms the other state-of-the-art methods by a large margin. Therefore, SIGAN-MACNN offers an effective solution for addressing the time series classification task of small sample size.