A new method based on generative adversarial networks for multivariate time series prediction

Abstract Multivariate time series have more complex and high‐dimensional characteristics, which makes it difficult to analyze and predict the data accurately. In this paper, a new multivariate time series prediction method is proposed. This method is a generative adversarial networks (GAN) method based on Fourier transform and bi‐directional gated recurrent unit (Bi‐GRU). First, the Fourier transform is utilized to extend the data features, which helps the GAN to better learn the distributional features of the original data. Second, in order to guide the model to fully learn the distribution of the original time series data, Bi‐GRU is introduced as the generator of GAN. To solve the problems of mode collapse and gradient vanishing that exist in GAN, Wasserstein distance is used as the loss function of GAN. Finally, the proposed method is used for the prediction of air quality, stock price and RMB exchange rate. The experimental results show that the model can effectively predict the trend of the time series compared with the other nine baseline models. It significantly improves the accuracy and flexibility of multivariate time series forecasting and provides new ideas and methods for accurate time series forecasting in industrial, financial and environmental fields.