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• W2769891213 abstract "Abstract Because of the significant industrial demands towards quality and safety of system, reliability prediction with historical failures data has generated broad interest. Particularly, for system‐oriented failures time‐series data, although the hybridization strategy has been exploited to separately predict the feature components extracted from the original data and achieved noteworthy performance, a convictive method for effectively extracting these feature components has not been explored. In this paper, we introduce weighted shape‐based time‐series clustering to improve the hybrid modeling and prediction, in which a novel distance metric named as w_SBD (ie, weighted shape‐based distance) is devised by fully considering the shapes of time series and the characteristics of failures prediction. Moreover, we further develop a flexible framework to extract and validate the feature components (named as FF_EVFC). In the framework, besides w_SBD, 3 kinds of validations for the extracted feature components are also involved. To demonstrate the robustness of w_SBD and FF_EVFC, we perform extensive experimental evaluations with different clustering and prediction methods. The results show a competitive performance of w_SBD against other common distance metrics and verify the effectiveness of FF_EVFC on the improvement of failures prediction." @default.
• W2769891213 created "2017-12-04" @default.
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• W2769891213 date "2017-11-21" @default.
• W2769891213 modified "2023-09-23" @default.
• W2769891213 title "Improving failures prediction by exploring weighted shape-based time-series clustering" @default.
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• W2769891213 doi "https://doi.org/10.1002/qre.2242" @default.
• W2769891213 hasPublicationYear "2017" @default.
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