Generalized Network Autoregressive Processes and the GNAR package

Marina Knight; Kathryn Leeming; Guy Nason; Matthew Nunes

doi:10.18637/jss.v096.i05

Generalized Network Autoregressive Processes and the GNAR package

Marina Knight, Kathryn Leeming, Guy Nason, Matthew Nunes

Research output: Contribution to journal › Article › peer-review

19 Citations (SciVal)

Abstract

This article introduces the GNAR package, which fits, predicts, and simulates from a powerful new class of generalised 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 neighbouring 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.

Original language	English
Pages (from-to)	1-36
Number of pages	36
Journal	Journal of Statistical Software
Volume	96
Issue number	5
Early online date	29 Nov 2020
DOIs	https://doi.org/10.18637/jss.v096.i05
Publication status	Published - 31 Dec 2020

Bibliographical note

Publisher Copyright:
© 2020, American Statistical Association. All rights reserved.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

Missing data
Multivariate time series
Network time series
Networks

ASJC Scopus subject areas

Software
Statistics and Probability
Statistics, Probability and Uncertainty

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10.18637/jss.v096.i05Licence: CC BY

Cite this

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Generalized Network Autoregressive Processes and the GNAR package

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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