0000000000371139
AUTHOR
Bakht Zaman
showing 6 related works from this author
Online topology estimation for vector autoregressive processes in data networks
2017
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) algorit…
Online Topology Identification from Vector Autoregressive Time Series
2019
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 a…
Spectrum Occupancy and Residual Service Analysis in CRNs Using a Multi-Server Queueing Model
2015
Cognitive radio technology enables secondary users (SUs) to opportunistically access the unused or sparsely utilized spectrum by primary users (PUs) without causing any harmful interference to PUs. Consequently, spectrum occupancy modeling appears as an essential task in cognitive radio networks (CRNs). In this paper, we model spectrum occupancy using a queueing theory based approach in order to evaluate the performance of CRNs in terms of network capacity and number of cognitive radio users waiting for services etc. The queue adopted in this model has variable service capacity and can be considered as a multi-service queue with server failure where each channel acts as a server. When a cha…
Online Machine Learning for Graph Topology Identification from Multiple Time Series
2020
High dimensional time series data are observed in many complex systems. In networked data, some of the time series are influenced by other time series. Identifying these relations encoded in a graph structure or topology among the time series is of paramount interest in certain applications since the identified structure can provide insights about the underlying system and can assist in inference tasks. In practice, the underlying topology is usually sparse, that is, not all the participating time series in influence each other. The goal of this dissertation pertains to study the problem of sparse topology identification under various settings. Topology identification from time series is a …
Dynamic network identification from non-stationary vector autoregressive time series
2018
Learning the dynamics of complex systems features a large number of applications in data science. Graph-based modeling and inference underpins the most prominent family of approaches to learn complex dynamics due to their ability to capture the intrinsic sparsity of direct interactions in such systems. They also provide the user with interpretable graphs that unveil behavioral patterns and changes. To cope with the time-varying nature of interactions, this paper develops an estimation criterion and a solver to learn the parameters of a time-varying vector autoregressive model supported on a network of time series. The notion of local breakpoint is proposed to accommodate changes at individu…
Dynamic Regret Analysis for Online Tracking of Time-varying Structural Equation Model Topologies
2020
Identifying dependencies among variables in a complex system is an important problem in network science. Structural equation models (SEM) have been used widely in many fields for topology inference, because they are tractable and incorporate exogenous influences in the model. Topology identification based on static SEM is useful in stationary environments; however, in many applications a time-varying underlying topology is sought. This paper presents an online algorithm to track sparse time-varying topologies in dynamic environments and most importantly, performs a detailed analysis on the performance guarantees. The tracking capability is characterized in terms of a bound on the dynamic re…