New paper accepted in IEEE Transactions on Signal and Information Processing over Networks

Congratulations to B. Zaman, L. M. López-Ramos, B. Beferull-Lozano for the acceptance of a journal paper in IEEE Transactions on Signal and Information Processing over Networks, 2023.

B. Zaman, L. M. Lopez-Ramos, B. Beferull-Lozano, “Online Joint Topology Identification and Signal Estimation from Streams with Missing Data”, Accepted, To appear in IEEE Transactions on Signal and Information Processing over Networks, 2023.

Short description of the paper: Identifying the topology underlying a set of time series is useful for tasks such as prediction, denoising, and data completion. Vector autoregressive (VAR) model-based topologies capture dependencies among time series and are often inferred from observed spatio-temporal data. When data are affected by noise and/or missing samples, topology identification and signal recovery (reconstruction) tasks must be performed jointly. Additional challenges arise when i) the underlying topology is time-varying, ii) data become available sequentially, and iii) no delay is tolerated. This study proposes an online algorithm to overcome these challenges in estimating VAR model-based topologies, having constant complexity per iteration, which makes it interesting for big-data scenarios. The inexact proximal online gradient descent framework is used to derive a performance guarantee for the proposed algorithm, in the form of a dynamic regret bound. Numerical tests are also presented, showing the ability of the proposed algorithm to track time-varying topologies with missing data in an online fashion.