Improving failures prediction by exploring weighted shape-based time-series clustering

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.