Modern Strategies for Time Series Regression

Clark, S; Hyndman, RJ; Pagendam, D; Ryan, LM

Modern Strategies for Time Series Regression

Clark, S

Hyndman, RJ Pagendam, D Ryan, LM

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Publisher:: WILEY
Publication Type:: Journal Article
Citation:: INTERNATIONAL STATISTICAL REVIEW, 2020, 88, (S1), pp. S179-S204
Issue Date:: 2020-12-03

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Field	Value	Language
dc.contributor.author	Clark, S https://orcid.org/0000-0002-1452-9912
dc.contributor.author	Hyndman, RJ
dc.contributor.author	Pagendam, D
dc.contributor.author	Ryan, LM
dc.date.accessioned	2021-01-07T22:19:29Z
dc.date.available	2021-01-07T22:19:29Z
dc.date.issued	2020-12-03
dc.identifier.citation	INTERNATIONAL STATISTICAL REVIEW, 2020, 88, (S1), pp. S179-S204
dc.identifier.issn	0306-7734
dc.identifier.issn	1751-5823
dc.identifier.uri	http://hdl.handle.net/10453/145197
dc.description.abstract	This paper discusses several modern approaches to regression analysis involving time series data where some of the predictor variables are also indexed by time. We discuss classical statistical approaches as well as methods that have been proposed recently in the machine learning literature. The approaches are compared and contrasted, and it will be seen that there are advantages and disadvantages to most currently available approaches. There is ample room for methodological developments in this area. The work is motivated by an application involving the prediction of water levels as a function of rainfall and other climate variables in an aquifer in eastern Australia.
dc.language	English
dc.publisher	WILEY
dc.relation	http://purl.org/au-research/grants/arc/CE140100049
dc.relation.ispartof	INTERNATIONAL STATISTICAL REVIEW
dc.relation.isbasedon	10.1111/insr.12432
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0104 Statistics, 0199 Other Mathematical Sciences
dc.subject.classification	Statistics & Probability
dc.title	Modern Strategies for Time Series Regression
dc.type	Journal Article
utslib.citation.volume	88
utslib.for	0104 Statistics
utslib.for	0199 Other Mathematical Sciences
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2021-01-07T22:19:14Z
pubs.issue	S1
pubs.publication-status	Published
pubs.volume	88
utslib.citation.issue	S1

Abstract:

This paper discusses several modern approaches to regression analysis involving time series data where some of the predictor variables are also indexed by time. We discuss classical statistical approaches as well as methods that have been proposed recently in the machine learning literature. The approaches are compared and contrasted, and it will be seen that there are advantages and disadvantages to most currently available approaches. There is ample room for methodological developments in this area. The work is motivated by an application involving the prediction of water levels as a function of rainfall and other climate variables in an aquifer in eastern Australia.

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http://hdl.handle.net/10453/145197