Multivariate Time Series Prediction in Industrial Processes via a Deep Hybrid Network under Data Uncertainty

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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  • Yuantao Yao
  • Minghan Yang
  • Jianye Wang
  • Min Xie


Original languageEnglish
Number of pages11
Journal / PublicationIEEE Transactions on Industrial Informatics
Online published16 Aug 2022
Publication statusOnline published - 16 Aug 2022


With the rapid progress of the industrial Internet of Things (IIoT), reducing data uncertainty has become a critical issue in predicting the development trends of systems and formulating future maintenance strategies. This paper proposes an end-to-end, deep hybrid network-based, short-term, multivariate time series prediction framework for industrial processes. First, the maximal information coefficient (MIC) is adopted to extract the nonlinear variate correlation features. Second, a convolutional neural network (CNN) with a residual elimination module is designed to eliminate data uncertainty. Third, a bidirectional gated recurrent unit (Bi-GRU) network is connected in a time-distributed form to achieve step-ahead prediction. Last, an optimized Bayesian optimization method is adopted to optimize the model's learning rate. A comparison with other state-of-the-art, deep learning (DL)-based, time series prediction methods in the case study illustrates the superiority of the proposed framework in noisy IIoT environments.

Research Area(s)

  • data uncertainty, deep hybrid networks, hyperparameter optimization, IIoT, Industrial Internet of Things, Logic gates, Monitoring, multivariate time-series prediction, Predictive models, Task analysis, Time series analysis, Uncertainty