Detecting noise in a time series

C. J. Cellucci; A. M. Albano; P. E. Rapp; R. A. Pittenger; R. C. Josiassen

doi:10.1063/1.166214

Detecting noise in a time series

C. J. Cellucci^*
, A. M. Albano
, P. E. Rapp
, R. A. Pittenger
, R. C. Josiassen

^*Corresponding author for this work

Military and Emergency Medicine

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

27 Scopus citations

Abstract

A numerical algorithm is presented for estimating whether, and roughly to what extent, a time series is noise corrupted. Using phase-randomized surrogates constructed from the original signal, metrics are defined which can be used to quantify the noise level. A saturation occurs in these metrics at signal to noise ratios (SNRs) of around O dB and below, and also at around 20 dB and above. In between these two regions there is a monotonic transition in the value of the metrics from one region to the other corresponding to changes in the SNR.

Original language	English
Pages (from-to)	414-422
Number of pages	9
Journal	Chaos
Volume	7
Issue number	3
DOIs	https://doi.org/10.1063/1.166214
State	Published - Sep 1997

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title = "Detecting noise in a time series",

abstract = "A numerical algorithm is presented for estimating whether, and roughly to what extent, a time series is noise corrupted. Using phase-randomized surrogates constructed from the original signal, metrics are defined which can be used to quantify the noise level. A saturation occurs in these metrics at signal to noise ratios (SNRs) of around O dB and below, and also at around 20 dB and above. In between these two regions there is a monotonic transition in the value of the metrics from one region to the other corresponding to changes in the SNR.",

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AU - Cellucci, C. J.

AU - Albano, A. M.

AU - Rapp, P. E.

AU - Pittenger, R. A.

AU - Josiassen, R. C.

PY - 1997/9

Y1 - 1997/9

N2 - A numerical algorithm is presented for estimating whether, and roughly to what extent, a time series is noise corrupted. Using phase-randomized surrogates constructed from the original signal, metrics are defined which can be used to quantify the noise level. A saturation occurs in these metrics at signal to noise ratios (SNRs) of around O dB and below, and also at around 20 dB and above. In between these two regions there is a monotonic transition in the value of the metrics from one region to the other corresponding to changes in the SNR.

AB - A numerical algorithm is presented for estimating whether, and roughly to what extent, a time series is noise corrupted. Using phase-randomized surrogates constructed from the original signal, metrics are defined which can be used to quantify the noise level. A saturation occurs in these metrics at signal to noise ratios (SNRs) of around O dB and below, and also at around 20 dB and above. In between these two regions there is a monotonic transition in the value of the metrics from one region to the other corresponding to changes in the SNR.

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DO - 10.1063/1.166214

M3 - Article

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SN - 1054-1500

VL - 7

SP - 414

EP - 422

JO - Chaos

JF - Chaos

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Detecting noise in a time series

Abstract

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