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Reconstruction of missing data in multidimensional time series by fuzzy similarity |
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| Affiliation: | 1. Energy Department, Politecnico di Milano, Via Ponzio 34/3, 20133 Milano, Italy;2. Chair on System Science and the Energetic Challenge, European Foundation for New Energy – Paris and Supelec, Paris, France;1. Jewish General Hospital, 3755 Côte-Sainte-Catherine, Montreal, QC H3T1E2, Canada;2. McGill University, Montreal, QC, Canada;1. Department of Computer Science, Palacky University, 17. listopadu 12, Olomouc, Czech Republic;2. Department of Electrical Engineering, RIMT Institute, Mandi Gobindgarh 147301, India;3. School of Mathematics and Computer Applications, Thapar University, Patiala 147004, India |
| 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 shut-down transients of a Nuclear Power Plant (NPP) turbine. |
| Keywords: | Time series Missing data Fuzzy similarity Auto-Associative Kernel Regression (AAKR) Operational transients in industrial plants Nuclear power plant (NPP) |
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