A Novel Wavelet Based Generative Model for Time Series Prediction
Chaofan Dai, Xiaoguang Yuan, Zongkai Tian, Xinyue Hu, Zhen Luan, Youchen Wang · 2024
Generative models have become an exciting area of research in recent years. Generative Adversarial Networks (GANs) and Denoising Diffusion Probabilistic Models (DDPMs) have been utilized in various data augmentation applications. However, generative models can learn high-dimensional features of data through adversarial learning, making them suitable for nonlinear and nonstationary time series analysis, such as stock market prediction, high-frequency trading, and ocean current forecasting. In this paper, the researchers focus on using a wavelet-based GAN to predict stock market prices by generating synthetic stock market price trends. Historical stock price data from 2014 to 2024 is used for our experiments, and the results show that the wavelet-based GAN outperforms deep learning baseline models..