AuthorsR. Money, J. Krishnan and B. Beferull-Lozano
TitleOnline Joint Nonlinear Topology Identification and Missing Data Imputation over Dynamic Graphs
AfilliationMachine Learning
Project(s)Signal and Information Processing for Intelligent Systems
Publication TypeProceedings, refereed
Year of Publication2022
Conference Name30th European Signal Processing Conference (EUSIPCO)
Publisher IEEE

Extracting causal graph structures from multivariate time series, termed topology identification, is a fundamental problem in network science with several important applications. Topology identification is a challenging problem in real-world sensor networks, especially when the available time series are partially observed due to faulty communication links or sensor failures. The problem becomes even more challenging when the sensor dependencies are nonlinear and nonstationary. This paper proposes a kernel-based online framework using random feature approximation to jointly estimate nonlinear causal dependencies and missing data from partial observations of streaming graph-connected time series. Exploiting the fact that real-world networks often exhibit sparse topologies, we propose a group lasso-based optimization framework for topology identification, which is solved online using alternating minimization techniques. The ability of the algorithm is illustrated using several numerical experiments conducted using both synthetic and real data.


This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS INDURB grant 270730/O70 and the SFI Offshore Mechatronics grant 237896/O30 from the Research Council of Norway.

Citation Key42572