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.
IEEE Access