Best paper Award – IEEE Data Science & Learning Conference
It is our pleasure to announce that Rohan Thekkemarickal Money’s conference paper, co-authored with Dr. Joshin Parakkulangarayil Krishnan and Prof. Baltasar Beferull-Lozano, has received one of the Best paper Awards in the IEEE Data Science & Learning Conference:
Rohan Thekkemarickal Money, Joshin Parakkulangarayil Krishnan, Baltasar Beferull Lozano «Online non-linear Topology Identification from Graph-connected time-series», IEEE DSLW 2021.
See UiA news about this Award.
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 nonlinear vector auto-regressive 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.