2020/12/10 by Bakht Zaman, Zaman, Bakht, Luis M. López-Ramos +3
Computer Science · Mathematics · #Algorithm #Artificial intelligence #Autoregressive model #Blind Source Separation Techniques #Computer science #Data mining #Data set #Identification (biology) #Machine learning #Mathematics #Missing data #Network topology #Noise (video) #Statistics #Target Tracking and Data Fusion in Sensor Networks #Time Series Analysis and Forecasting #Time series #Topology (electrical circuits)
paper · pdf · doi:10.48550/arxiv.2012.05957
openalex publication_date 2020/12/10 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
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.