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 article proposes an end-to-end, deep hybrid network-based, short-term, multivariate time-series prediction framework for industrial processes. First, the maximal information coefficient is adopted to extract the nonlinear variate correlation features. Second, a convolutional neural network with a residual elimination module is designed to eliminate data uncertainty. Third, a bidirectional gated recurrent unit 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-based, time-series prediction methods in the case study illustrates the superiority of the proposed framework in noisy IIoT environments.

中文翻译：

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数据不确定性下通过深度混合网络进行工业过程中的多元时间序列预测


随着工业物联网（IIoT）的快速进步，降低数据不确定性已成为预测系统发展趋势和制定未来维护策略的关键问题。本文提出了一种用于工业过程的端到端、基于深度混合网络的短期多元时间序列预测框架。首先，采用最大信息系数提取非线性变量相关特征。其次，设计了带有残差消除模块的卷积神经网络来消除数据的不确定性。第三，以时间分布形式连接双向门控循环单元网络以实现超前预测。最后，采用优化的贝叶斯优化方法来优化模型的学习率。案例研究中与其他最先进的基于深度学习的时间序列预测方法的比较说明了所提出的框架在嘈杂的工业物联网环境中的优越性。