2021/03/31 by Rohan Money, Joshin Krishnan, Money, Rohan +4
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #FOS: Electrical engineering #Signal Processing (eess.SP) #Time Series Analysis and Forecasting #eess.SP #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2104.00030
arxiv created 2021/03/31 · openalex publication_date 2021/03/31 · arxiv updated 2021/04/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.