Online Joint Topology Identification and Signal Estimation From Streams With Missing Data

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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on signal and information processing over networks 2023, Vol.9, p.691-704
Main Authors: Zaman, Bakht, Lopez-Ramos, Luis Miguel, Beferull-Lozano, Baltasar
Format: Article
Language:English
Subjects:
Algorithms
Autoregressive models
Big Data
dynamic regret analysis
Estimation
Heuristic algorithms
Iterative methods
Missing data
Network topology
network topology estimation
noisy and missing data
online covex optimization
Optimization
optimization methods
Reactive power
Signal reconstruction
Spatiotemporal data
Time series
Time series analysis
time-varying systems
Topology
Vector autoregressive processes
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2373-776X
2373-7778
DOI:10.1109/TSIPN.2023.3324569