Online Non-linear Topology Identification from Graph-connected Time Series
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 finance engineering. Inference of such causal dependencies, often know 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.
Cite
@article{arxiv.2104.00030,
title = {Online Non-linear Topology Identification from Graph-connected Time Series},
author = {Rohan Money and Joshin Krishnan and Baltasar Beferull-Lozano},
journal= {arXiv preprint arXiv:2104.00030},
year = {2021}
}