6533b85efe1ef96bd12bfd63
RESEARCH PRODUCT
Inference of Spatiotemporal Processes over Graphs via Kernel Kriged Kalman Filtering
Daniel RomeroVassilis N. IoannidisGeorgios B. Giannakissubject
business.industryInference020206 networking & telecommunicationsNetwork science02 engineering and technologyKalman filterNetwork topologyMachine learningcomputer.software_genreGraphKriging0202 electrical engineering electronic engineering information engineeringArtificial intelligenceNumerical testsbusinessAlgorithmLaplace operatorcomputerMathematicsdescription
Inference of space-time signals evolving over graphs emerges naturally in a number of network science related applications. A frequently encountered challenge pertains to reconstructing such dynamic processes given their values over a subset of vertices and time instants. The present paper develops a graph-aware kernel-based kriged Kalman filtering approach that leverages the spatio-temporal dynamics to allow for efficient online reconstruction, while also coping with dynamically evolving network topologies. Laplacian kernels are employed to perform kriging over the graph when spatial second-order statistics are unknown, as is often the case. Numerical tests with synthetic and real data illustrate the superior reconstruction performance of the proposed approach.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-01-25 |