2019/04/03 by Bakht Zaman, Luis Miguel Lopez Ramos, Zaman, Bakht +5 · 2 citations
Engineering · Mathematics · #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (stat.ML) #Signal Processing (eess.SP) #eess.SP #electronic engineering #information engineering #stat.ML
paper · pdf · doi:10.48550/arxiv.1904.01864
23 pages including supplementary material, submitted to IEEE Transactions on Signal Processing
arxiv created 2020/11/13 · arxiv updated 2020/11/16
Causality graphs are routinely estimated in social sciences, natural sciences, and engineering due to their capacity to efficiently represent the spatiotemporal structure of multivariate data sets in a format amenable for human interpretation, forecasting, and anomaly detection. A popular approach to mathematically formalize causality is based on vector autoregressive (VAR) models and constitutes an alternative to the well-known, yet usually intractable, Granger causality. Relying on such a VAR causality notion, this paper develops two algorithms with complementary benefits to track time-varying causality graphs in an online fashion. Their constant complexity per update also renders these algorithms appealing for big-data scenarios. Despite using data sequentially, both algorithms are shown to asymptotically attain the same average performance as a batch estimator which uses the entire data set at once. To this end, sublinear (static) regret bounds are established. Performance is also characterized in time-varying setups by means of dynamic regret analysis. Numerical results with real and synthetic data further support the merits of the proposed algorithms in static and dynamic scenarios.