Reconstruction of missing data in multidimensional time series by fuzzy similarity

• We address the problem of missing data in multidimensional time series.
• We propose a novel method based on a fuzzy similarity measure.
• The performance is compared with that of an Auto Associative Kernel Regression.
• The method is applied to shut-down transients of a Nuclear Power Plant (NPP) turbine.

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 shut-down transients of a Nuclear Power Plant (NPP) turbine.

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