@inproceedings {28388,
	title = {Online Non-linear Topology Identification from Graph-connected Time Series},
	journal = {2021 IEEE Data Science and Learning Workshop (DSLW)2021 IEEE Data Science and Learning Workshop (DSLW)},
	year = {2021},
	note = {This work was carried out at University of Agder with the funding from the IKTPLUSS INDURB grant 270730/O70 and the SFI Offshore Mechatronics grant 237896/O30 from the Research Council of Norway.},
	month = {June/2021},
	publisher = {IEEE},
	address = {Toronto, ON, Canada},
	abstract = {Estimating the unknown causal dependencies among graph-connected time series plays an important role in many applications, such as sensor network analysis, signal processing over cyber-physical systems, and financial engineering. Inference of such causal dependencies, often known as topology identification, is not well studied for non-linear non-stationary systems, and most of the existing methods are batch-based which are not capable of handling streaming sensor signals. In this paper, we propose an online kernel-based algorithm for topology estimation of non-linear vector autoregressive time series by solving a sparse online optimization framework using the composite objective mirror descent method. Experiments conducted on real and synthetic data sets show that the proposed algorithm outperforms the state-of-the-art methods for topology estimation.},
	doi = {10.1109/DSLW51110.2021.9523399},
	url = {https://ieeexplore.ieee.org/document/9523399},
	author = {Money, Rohan and Krishnan, Joshin and Beferull-Lozano, Baltasar}
}
