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

CASIA OpenIR > 学术期刊 > IEEE/CAA Journal of Automatica Sinica

	Variational Inference Based Kernel Dynamic Bayesian Networks for Construction of Prediction Intervals for Industrial Time Series With Incomplete Input
	Long Chen; Linqing Wang; Zhongyang Han; Jun Zhao; Wei Wang
发表期刊	IEEE/CAA Journal of Automatica Sinica
ISSN	2329-9266
	2020
卷号	7 期号:5 页码:1429-1437
摘要	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.
关键词	Industrial time series kernel dynamic Bayesian networks (KDBN) prediction intervals (PIs) variational inference
DOI	10.1109/JAS.2019.1911645
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/43044
专题	学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	Long Chen,Linqing Wang,Zhongyang Han,et al. Variational Inference Based Kernel Dynamic Bayesian Networks for Construction of Prediction Intervals for Industrial Time Series With Incomplete Input[J]. IEEE/CAA Journal of Automatica Sinica,2020,7(5):1429-1437.
APA	Long Chen,Linqing Wang,Zhongyang Han,Jun Zhao,&Wei Wang.(2020).Variational Inference Based Kernel Dynamic Bayesian Networks for Construction of Prediction Intervals for Industrial Time Series With Incomplete Input.IEEE/CAA Journal of Automatica Sinica,7(5),1429-1437.
MLA	Long Chen,et al."Variational Inference Based Kernel Dynamic Bayesian Networks for Construction of Prediction Intervals for Industrial Time Series With Incomplete Input".IEEE/CAA Journal of Automatica Sinica 7.5(2020):1429-1437.

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JAS-2018-0375.pdf（1543KB）	期刊论文	出版稿	开放获取	CC BY-NC-SA	浏览