  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.
