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

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.