Generalized Network Autoregressive Processes and the GNAR Package
Dublin Core
Title
Generalized Network Autoregressive Processes and the GNAR Package
Subject
multivariate time series, networks, missing data, network time series.
Description
This article introduces the GNAR package, which fits, predicts, and simulates from
a powerful new class of generalized network autoregressive processes. Such processes
consist of a multivariate time series along with a real, or inferred, network that provides
information about inter-variable relationships. The GNAR model relates values of a
time series for a given variable and time to earlier values of the same variable and of
neighboring variables, with inclusion controlled by the network structure. The GNAR
package is designed to fit this new model, while working with standard ‘ts’ objects and
the igraph package for ease of use
a powerful new class of generalized network autoregressive processes. Such processes
consist of a multivariate time series along with a real, or inferred, network that provides
information about inter-variable relationships. The GNAR model relates values of a
time series for a given variable and time to earlier values of the same variable and of
neighboring variables, with inclusion controlled by the network structure. The GNAR
package is designed to fit this new model, while working with standard ‘ts’ objects and
the igraph package for ease of use
Creator
Marina Knight
Source
https://www.jstatsoft.org/article/view/v096i05
Publisher
University of York
Date
November 2020
Contributor
Fajar Bagus W
Format
PDF
Language
Inggris
Type
Text
Files
Collection
Citation
Marina Knight, “Generalized Network Autoregressive Processes and the GNAR Package,” Repository Horizon University Indonesia, accessed April 5, 2025, https://repository.horizon.ac.id/items/show/8171.