Joint Signal Estimation and Nonlinear Topology Identification from Noisy Data with Missing Entries

Topology identification from multiple time series has been proved to be useful for system identification, anomaly detection, denoising, and data completion. Vector autoregressive (VAR) methods have proved well in identifying directed topology from complex networks. The task of inferring topology in...

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Bibliographic Details
Main Authors: Roy, Kevin, Lopez-Ramos, Luis Miguel, Beferull-Lozano, Baltasar
Format: Conference Proceeding
Language:English
Subjects:
Estimation
invertible neural network
missing data
Network topology
nonlinear topology identification
Reactive power
Sensors
System identification
Time series analysis
Topology
Vector autoregression
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Summary:Topology identification from multiple time series has been proved to be useful for system identification, anomaly detection, denoising, and data completion. Vector autoregressive (VAR) methods have proved well in identifying directed topology from complex networks. The task of inferring topology in the presence of noise and missing observations has been studied for linear models. As a first approach to joint signal estimation and topology identification with a nonlinear model, this paper proposes a method to do so under the modelling assumption that signals are generated by a sparse VAR model in a latent space and then transformed by a set of invertible, component-wise nonlinearities. A non convex optimization problem is formed with lasso regularisation and solved via block coordinate descent (BCD). Initial experiments conducted on synthetic data sets show the identifying capability of the proposed method.
ISSN:2576-2303
DOI:10.1109/IEEECONF56349.2022.10051968