Online Edge Flow Imputation on Networks
Money, Rohan Thekkemarickal; Krishnan, Joshin Parakkulangarayil; Beferull-Lozano, Baltasar; Isufi, Elvin
Peer reviewed, Journal article
Accepted version
Permanent lenke
https://hdl.handle.net/11250/3058116Utgivelsesdato
2022Metadata
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Originalversjon
Money, R. T., Krishnan, J. P., Beferull-Lozano, B. & Isufi, E. (2022). Online Edge Flow Imputation on Networks. IEEE Signal Processing Letters, 30, 115-119. https://doi.org/10.1109/LSP.2022.3221846Sammendrag
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.