6533b7dbfe1ef96bd126fe6a
RESEARCH PRODUCT
Online topology estimation for vector autoregressive processes in data networks
Luis M. Lopez-ramosBaltasar Beferull-lozanoBakht ZamanDaniel Romerosubject
Recursive least squares filter021103 operations researchComputer science0211 other engineering and technologiesEstimatorApproximation algorithm020206 networking & telecommunications02 engineering and technologyNetwork topologyCausality (physics)Autoregressive model0202 electrical engineering electronic engineering information engineeringOnline algorithmTime seriesAlgorithmdescription
An important problem in data sciences pertains to inferring causal interactions among a collection of time series. Upon modeling these as a vector autoregressive (VAR) process, this paper deals with estimating the model parameters to identify the underlying causality graph. To exploit the sparse connectivity of causality graphs, the proposed estimators minimize a group-Lasso regularized functional. To cope with real-time applications, big data setups, and possibly time-varying topologies, two online algorithms are presented to recover the sparse coefficients when observations are received sequentially. The proposed algorithms are inspired by the classic recursive least squares (RLS) algorithm and offer complementary benefits in terms of computational efficiency. Numerical results showcase the merits of the proposed schemes in both estimation and prediction tasks.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-12-01 | 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) |