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Parsimonious modelling, testing and forecasting of long-range dependence in wind speed

Hussain, S; Elbergali, A; Almasri, Abdullah LU and Shukur, G (2004) In Environmetrics 15(2). p.155-171
Abstract
Detecting and estimating long-range dependence are important in the analysis of many environmental time series. This article proposes a periodogram roughness (PR) estimator and describes its uses for testing and estimating the dependence structure. Asymptotic critical values are generated for performing the test, and special attention is given to investigating the properties of the PR regarding size and power. The conventional short-memory models, such as the autoregressive (AR), are shown to be less parsimonious. Forecasting errors of both fractional Gaussian noise (FGN) and fractional autoregressive moving average (FARMA) are investigated by conducting simulation studies. In addition to the PR, maximum likelihood (ML) and semi-parametric... (More)
Detecting and estimating long-range dependence are important in the analysis of many environmental time series. This article proposes a periodogram roughness (PR) estimator and describes its uses for testing and estimating the dependence structure. Asymptotic critical values are generated for performing the test, and special attention is given to investigating the properties of the PR regarding size and power. The conventional short-memory models, such as the autoregressive (AR), are shown to be less parsimonious. Forecasting errors of both fractional Gaussian noise (FGN) and fractional autoregressive moving average (FARMA) are investigated by conducting simulation studies. In addition to the PR, maximum likelihood (ML) and semi-parametric (SP) estimators are used and evaluated. Our results have shown that more accurate forecasted points are obtained when using the fractional forecasting. The methods are illustrated using Swedish wind speed data. Copyright (C) 2004 John Wiley Sons, Ltd. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
fractional forecasting, wind, frequency domain, periodogram roughness, long-range dependence, speed
in
Environmetrics
volume
15
issue
2
pages
155 - 171
publisher
John Wiley & Sons
external identifiers
  • wos:000220247500005
  • scopus:1642281241
ISSN
1099-095X
DOI
10.1002/env.632
language
English
LU publication?
yes
id
575979ea-9da1-4eb8-8ec0-b89369813c3e (old id 284900)
date added to LUP
2007-11-01 11:49:57
date last changed
2017-01-01 04:57:42
@article{575979ea-9da1-4eb8-8ec0-b89369813c3e,
  abstract     = {Detecting and estimating long-range dependence are important in the analysis of many environmental time series. This article proposes a periodogram roughness (PR) estimator and describes its uses for testing and estimating the dependence structure. Asymptotic critical values are generated for performing the test, and special attention is given to investigating the properties of the PR regarding size and power. The conventional short-memory models, such as the autoregressive (AR), are shown to be less parsimonious. Forecasting errors of both fractional Gaussian noise (FGN) and fractional autoregressive moving average (FARMA) are investigated by conducting simulation studies. In addition to the PR, maximum likelihood (ML) and semi-parametric (SP) estimators are used and evaluated. Our results have shown that more accurate forecasted points are obtained when using the fractional forecasting. The methods are illustrated using Swedish wind speed data. Copyright (C) 2004 John Wiley Sons, Ltd.},
  author       = {Hussain, S and Elbergali, A and Almasri, Abdullah and Shukur, G},
  issn         = {1099-095X},
  keyword      = {fractional forecasting,wind,frequency domain,periodogram roughness,long-range dependence,speed},
  language     = {eng},
  number       = {2},
  pages        = {155--171},
  publisher    = {John Wiley & Sons},
  series       = {Environmetrics},
  title        = {Parsimonious modelling, testing and forecasting of long-range dependence in wind speed},
  url          = {http://dx.doi.org/10.1002/env.632},
  volume       = {15},
  year         = {2004},
}