Why farima models are brittle

Veitch, D; Gorst-Rasmussen, A; Gefferth, A

Why farima models are brittle

Veitch, D

Gorst-Rasmussen, A Gefferth, A

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Publication Type:: Journal Article
Citation:: Fractals, 2013, 21 (2)
Issue Date:: 2013-01-01

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Field	Value	Language
dc.contributor.author	Veitch, D https://orcid.org/0000-0001-8163-3464	en_US
dc.contributor.author	Gorst-Rasmussen, A	en_US
dc.contributor.author	Gefferth, A	en_US
dc.date.available	2013-04-25	en_US
dc.date.issued	2013-01-01	en_US
dc.identifier.citation	Fractals, 2013, 21 (2)	en_US
dc.identifier.issn	0218-348X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/114697
dc.description.abstract	The FARIMA models, which have long-range-dependence (LRD), are widely used in many areas. Through the derivation of a precise characterization of the spectrum and variance time function, we show that this family is very atypical among LRD processes, being extremely close to the fractional Gaussian noise in a precise sense which results in ultra-fast convergence to fGn under rescaling. Furthermore, we show that this closeness property is not robust to additive noise. We argue that the use of FARIMA, and more generally fractionally differenced time series, should be reassessed in some contexts, in particular when convergence rate under rescaling is important and noise is expected. © 2013 World Scientific Publishing Company.	en_US
dc.relation.ispartof	Fractals	en_US
dc.relation.isbasedon	10.1142/S0218348X13500126	en_US
dc.subject.classification	Fluids & Plasmas	en_US
dc.title	Why farima models are brittle	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	21	en_US
utslib.for	0105 Mathematical Physics	en_US
utslib.for	0102 Applied Mathematics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	open_access
dc.identifier.doi	10.1142/S0218348X13500126
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	21	en_US

Abstract:

The FARIMA models, which have long-range-dependence (LRD), are widely used in many areas. Through the derivation of a precise characterization of the spectrum and variance time function, we show that this family is very atypical among LRD processes, being extremely close to the fractional Gaussian noise in a precise sense which results in ultra-fast convergence to fGn under rescaling. Furthermore, we show that this closeness property is not robust to additive noise. We argue that the use of FARIMA, and more generally fractionally differenced time series, should be reassessed in some contexts, in particular when convergence rate under rescaling is important and noise is expected. © 2013 World Scientific Publishing Company.

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http://hdl.handle.net/10453/114697