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Volume 7 Issue 5
Sep.  2020

IEEE/CAA Journal of Automatica Sinica

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Long Chen, Linqing Wang, Zhongyang Han, Jun Zhao and Wei Wang, "Variational Inference Based Kernel Dynamic Bayesian Networks for Construction of Prediction Intervals for Industrial Time Series With Incomplete Input," IEEE/CAA J. Autom. Sinica, vol. 7, no. 5, pp. 1437-1445, Sept. 2020. doi: 10.1109/JAS.2019.1911645
Citation: Long Chen, Linqing Wang, Zhongyang Han, Jun Zhao and Wei Wang, "Variational Inference Based Kernel Dynamic Bayesian Networks for Construction of Prediction Intervals for Industrial Time Series With Incomplete Input," IEEE/CAA J. Autom. Sinica, vol. 7, no. 5, pp. 1437-1445, Sept. 2020. doi: 10.1109/JAS.2019.1911645

Variational Inference Based Kernel Dynamic Bayesian Networks for Construction of Prediction Intervals for Industrial Time Series With Incomplete Input

doi: 10.1109/JAS.2019.1911645
Funds:  This work was supported by the National Key Research and Development Program of China (2017YFA0700300) and the National Natural Sciences Foundation of China (61533005, 61703071, 61603069)
More Information
  • Prediction intervals (PIs) for industrial time series can provide useful guidance for workers. Given that the failure of industrial sensors may cause the missing point in inputs, the existing kernel dynamic Bayesian networks (KDBN), serving as an effective method for PIs construction, suffer from high computational load using the stochastic algorithm for inference. This study proposes a variational inference method for the KDBN for the purpose of fast inference, which avoids the time-consuming stochastic sampling. The proposed algorithm contains two stages. The first stage involves the inference of the missing inputs by using a local linearization based variational inference, and based on the computed posterior distributions over the missing inputs the second stage sees a Gaussian approximation for probability over the nodes in future time slices. To verify the effectiveness of the proposed method, a synthetic dataset and a practical dataset of generation flow of blast furnace gas (BFG) are employed with different ratios of missing inputs. The experimental results indicate that the proposed method can provide reliable PIs for the generation flow of BFG and it exhibits shorter computing time than the stochastic based one.

     

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    Highlights

    • This study proposes a variational inference method for the KDBN for time series prediction with missing points.
    • There are two inference stages, including the inference of missing inputs and the prediction stage.
    • The proposed method exhibits faster computing time with good performance in experiments.

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