Multivariate Financial Time-Series Prediction With Certified Robustness

文献类型： 外文期刊

第一作者： Li, Hui

作者： Li, Hui;Cui, Yunpeng;Liu, Juan;Wang, Shuo;Wang, Shuo;Qin, Jinyuan;Yang, Yilin;Cui, Yunpeng;Liu, Juan

作者机构：

关键词： Time series analysis; Feature extraction; Differential privacy; Logic gates; Sensitivity; Robustness; Predictive models; Futures prices; deep neural networks; prediction; multivariate; Gaussian noise

期刊名称：IEEE ACCESS （ 影响因子：3.367； 五年影响因子：3.671 ）

ISSN： 2169-3536

年卷期： 2020 年 8 卷

页码：

收录情况： SCI

摘要： 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.

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