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论文类型：期刊论文

发表时间：2016-10-01

发表刊物：ARTIFICIAL INTELLIGENCE REVIEW

收录刊物：SCIE、EI、Scopus

卷号：46

期号：3

页面范围：307-326

ISSN号：0269-2821

关键字：Prediction intervals; Dynamic Bayesian network; Kernel; Sparse Bayesian learning; Incomplete input

摘要：Reliable prediction intervals (PIs) construction for industrial time series is substantially significant for decision-making in production practice. Given the industrial data feature of high level noises and incomplete input, a high order dynamic Bayesian network (DBN)-based PIs construction method for industrial time series is proposed in this study. For avoiding to designate the amount and type of the basis functions in advance, a linear combination of kernel functions is designed to describe the relationships between the nodes in the network, and a learning method based on the scoring criterion-the sparse Bayesian score, is then reported to acquire suitable model parameters such as the weights and the variances. To verify the performance of the proposed method, two types of time series which are the classical Mackey-Glass data mixed by additive noises and a real-world industrial data are employed. The results indicate the effectiveness of our proposed method for the PIs construction of the industrial data with incomplete input.