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Reconstruction of missing data in multidimensional time series by fuzzy similarity

Abstract : The present work addresses the problem of missing data in multidimensional time series such as those collected during operational transients in industrial plants. We propose a novel method for missing data reconstruction based on three main steps: (1) computing a fuzzy similarity measure between a segment of the time series containing the missing data and segments of reference time series; (2) assigning a weight to each reference segment; (3) reconstructing the missing values as a weighted average of the reference segments. The performance of the proposed method is compared with that of an Auto Associative Kernel Regression (AAKR) method on an artificial case study and a real industrial application regarding shutdown transients of a Nuclear Power Plant (NPP) turbine.
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Submitted on : Thursday, July 16, 2015 - 1:58:00 PM
Last modification on : Wednesday, July 15, 2020 - 10:00:02 AM
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Piero Baraldi, F. Di Maio, D. Genini, Enrico Zio. Reconstruction of missing data in multidimensional time series by fuzzy similarity. Applied Soft Computing, Elsevier, 2015, 26, pp.1-9. ⟨10.1016/j.asoc.2014.09.038⟩. ⟨hal-01177010⟩



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