Multivariate Financial Time-Series Prediction With Certified Robustness

The futures market's forecasts are significant to investors and policymakers, where the application of deep learning approaches to finance has received a great deal of attention. In this study, we propose a multivariate financial time-series forecasting method. Our model addresses the long- and short-term features, multimodal and non-stationarity nature of multivariate time-series by incorporating the improved deep neural networks and certified noise injection. Specifically, multimodal variational autoencoder is used to extract deep high-level features of multivariate time-series, Long- and Short- Term recurrent neural network is applied for multivariate time-series forecasting, and certified noise injection mechanism, inspired by differential privacy, is proposed to improve the robustness and accuracy of prediction. Extensive empirical results on real-world agricultural commodity futures price time series and relevant external data demonstrate that our model achieves better performance over that of several state-of-the-art baseline methods.