Online Edge Flow Imputation on Networks

Journal article (2022)
Copyright: © 2022 Rohan Money, Joshin Krishnan, Baltasar Beferull-Lozano, E. Isufi
DOI: https://doi.org/10.1109/LSP.2022.3221846
Optimization Line Graph Time series analysis Reactive power Kalman filters Laplace equations Signal processing algorithms Missing Flow Imputation Signal reconstruction Simplicial Complex Topological Signal Processing
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Published Date

2022

Language

English

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Abstract

An online algorithm for missing data imputation for networks with signals defined on the edges is presented. Leveraging the prior knowledge intrinsic to real-world networks, we propose a bi-level optimization scheme that exploits the causal dependencies and the flow conservation, respectively via (i) a sparse line graph identification strategy based on a group-Lasso and (ii) a Kalman filtering-based signal reconstruction strategy developed using simplicial complex (SC) formulation. The advantages of this first SC-based attempt for time-varying signal imputation have been demonstrated through numerical experiments using EPANET models of both synthetic and real water distribution networks.