Financial Time Series Prediction Based on XGBoost and Generative Adversarial Networks

Authors: Jialing Xu, Jingxing He, Jinqiang Gu, Huayang Wu, Lei Wang, Yongzhen Zhu, Tiejun Wang, Xiaoling He, Zhangyuan Zhou

Abstract: Considering the problems of the model collapse and the low forecast precision in predicting the financial time series of the generative adversarial networks (GAN), we apply the WGAN-GP model to solve the gradient collapse. Extreme gradient boosting (XGBoost) is used for feature extraction to improve prediction accuracy. Alibaba stock is taken as the research object, using XGBoost to optimize its characteristic factors, and training the optimized characteristic variables with WGAN-GP. We compare the prediction results of WGAN-GP model and classical time series prediction models, long short term memory (LSTM) and gate recurrent unit (GRU). In the experimental stage, root mean square error (RMSE) is chosen as the evaluation index. The results of different models show that the RMSE of WGAN-GP model is the smallest, which are 61.94% and 47.42%, lower than that of LSTM model and GRU model respectively. At the same time, the stock price data of Google and Amazon confirm the stability of WGAN-GP model. WGAN-GP model can obtain higher prediction accuracy than the classical time series prediction model.

Pages: 637-645

DOI: 10.46300/9106.2022.16.79

International Journal of Circuits, Systems and Signal Processing, E-ISSN: 1998-4464, Volume 16, 2022, Art. #79