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Artificial Intelligence Review

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01-10-2016

Prediction intervals for industrial data with incomplete input using kernel-based dynamic Bayesian networks

Authors: Long Chen, Ying Liu, Jun Zhao, Wei Wang, Quanli Liu

Published in: Artificial Intelligence Review | Issue 3/2016

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Abstract

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.

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Title: Prediction intervals for industrial data with incomplete input using kernel-based dynamic Bayesian networks
Authors: Long Chen
Ying Liu
Jun Zhao
Wei Wang
Quanli Liu
Publication date: 01-10-2016
Publisher: Springer Netherlands
Published in: Artificial Intelligence Review / Issue 3/2016
Print ISSN: 0269-2821
Electronic ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-016-9465-y

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