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Spectral correction for locally stationary Shannon wavelet processes

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Spectral correction for locally stationary Shannon wavelet processes. / Eckley, Idris; Nason, Guy P. .

In: Electronic Journal of Statistics, Vol. 8, No. 1, 2014, p. 184-200.

Research output: Contribution to journalJournal article

Harvard

Eckley, I & Nason, GP 2014, 'Spectral correction for locally stationary Shannon wavelet processes', Electronic Journal of Statistics, vol. 8, no. 1, pp. 184-200. https://doi.org/10.1214/14-EJS880

APA

Eckley, I., & Nason, G. P. (2014). Spectral correction for locally stationary Shannon wavelet processes. Electronic Journal of Statistics, 8(1), 184-200. https://doi.org/10.1214/14-EJS880

Vancouver

Author

Eckley, Idris ; Nason, Guy P. . / Spectral correction for locally stationary Shannon wavelet processes. In: Electronic Journal of Statistics. 2014 ; Vol. 8, No. 1. pp. 184-200.

Bibtex

@article{98b2ed17586042aea69d7612c9a554d1,
title = "Spectral correction for locally stationary Shannon wavelet processes",
abstract = "It is well-known that if a time series is not sampled at a fast enough rate to capture all the high frequencies then aliasing may occur. Aliasing is a distortion of the spectrum of a series which can cause severe problems for time series modelling and forecasting. The situation is more complex and more interesting for nonstationary series as aliasing can be intermittent. Recent work has shown that it is possible to test for the absence of aliasing in nonstationary time series and this article demonstrates that additional benefits can be obtained by modelling a series using a Shannon locally stationary wavelet (LSW) process. We show that for Shannon LSW processes the effects of dyadic-sampling-induced aliasing can be reversed. We illustrate our method by simulation on Shannon LSW processes and also a time-varying autoregressive process where aliasing is detected. We present an analysis of a wind power time series and show that it can be adequately modelled by a Shannon LSW process, the absence of aliasing can not be inferred and present a dealiased estimate of the series.",
author = "Idris Eckley and Nason, {Guy P.}",
year = "2014",
doi = "10.1214/14-EJS880",
language = "English",
volume = "8",
pages = "184--200",
journal = "Electronic Journal of Statistics",
issn = "1935-7524",
publisher = "Institute of Mathematical Statistics",
number = "1",

}

RIS

TY - JOUR

T1 - Spectral correction for locally stationary Shannon wavelet processes

AU - Eckley, Idris

AU - Nason, Guy P.

PY - 2014

Y1 - 2014

N2 - It is well-known that if a time series is not sampled at a fast enough rate to capture all the high frequencies then aliasing may occur. Aliasing is a distortion of the spectrum of a series which can cause severe problems for time series modelling and forecasting. The situation is more complex and more interesting for nonstationary series as aliasing can be intermittent. Recent work has shown that it is possible to test for the absence of aliasing in nonstationary time series and this article demonstrates that additional benefits can be obtained by modelling a series using a Shannon locally stationary wavelet (LSW) process. We show that for Shannon LSW processes the effects of dyadic-sampling-induced aliasing can be reversed. We illustrate our method by simulation on Shannon LSW processes and also a time-varying autoregressive process where aliasing is detected. We present an analysis of a wind power time series and show that it can be adequately modelled by a Shannon LSW process, the absence of aliasing can not be inferred and present a dealiased estimate of the series.

AB - It is well-known that if a time series is not sampled at a fast enough rate to capture all the high frequencies then aliasing may occur. Aliasing is a distortion of the spectrum of a series which can cause severe problems for time series modelling and forecasting. The situation is more complex and more interesting for nonstationary series as aliasing can be intermittent. Recent work has shown that it is possible to test for the absence of aliasing in nonstationary time series and this article demonstrates that additional benefits can be obtained by modelling a series using a Shannon locally stationary wavelet (LSW) process. We show that for Shannon LSW processes the effects of dyadic-sampling-induced aliasing can be reversed. We illustrate our method by simulation on Shannon LSW processes and also a time-varying autoregressive process where aliasing is detected. We present an analysis of a wind power time series and show that it can be adequately modelled by a Shannon LSW process, the absence of aliasing can not be inferred and present a dealiased estimate of the series.

U2 - 10.1214/14-EJS880

DO - 10.1214/14-EJS880

M3 - Journal article

VL - 8

SP - 184

EP - 200

JO - Electronic Journal of Statistics

JF - Electronic Journal of Statistics

SN - 1935-7524

IS - 1

ER -