A periodogram-based test for weak stationarity and consistency between sections in time series

D. M. Halliday; J. R. Rosenberg; A. Rigas; B. A. Conway

doi:10.1016/j.jneumeth.2009.03.009

A periodogram-based test for weak stationarity and consistency between sections in time series

D. M. Halliday, J. R. Rosenberg, A. Rigas, B. A. Conway

Electronic Engineering

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Abstract

in one approach to spectral estimation, a sample record is broken into a number of disjoint sections, or data is collected over a number of discrete trials. Spectral parameters are formed by averaging periodograms across these discrete sections or trials. A key assumption in this approach is that of weak stationarity. This paper describes a simple test that checks if periodogram ordinates are consistent across sections as a means of assessing weak stationarity. The test is called the Periodogram Coefficient of Variation (PCOV) test, and is a frequency domain test based on a technique of spectral analysis. Application of the test is illustrated to both simulated and experimental data (EMC, physiological tremor, EEG). An additional role for the test as a useful tool in exploratory analysis of time series is highlighted. (C) 2009 Elsevier B.V. All rights reserved.

Original language	English
Pages (from-to)	138-146
Number of pages	9
Journal	Journal of Neuroscience Methods
Volume	180
Issue number	1
DOIs	https://doi.org/10.1016/j.jneumeth.2009.03.009
Publication status	Published - 30 May 2009

Keywords

Spectral analysis
Periodogram
Time series
Stationarity
EEG
EMG
PHYSIOLOGICAL TREMOR
MOTOR TASK
SYNCHRONIZATION
ELECTROMYOGRAMS
IDENTIFICATION
FREQUENCY
SPECTRUM
SIGNALS

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10.1016/j.jneumeth.2009.03.009

http://www.sciencedirect.com/science/article/pii/S0165027009001587

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title = "A periodogram-based test for weak stationarity and consistency between sections in time series",

abstract = "in one approach to spectral estimation, a sample record is broken into a number of disjoint sections, or data is collected over a number of discrete trials. Spectral parameters are formed by averaging periodograms across these discrete sections or trials. A key assumption in this approach is that of weak stationarity. This paper describes a simple test that checks if periodogram ordinates are consistent across sections as a means of assessing weak stationarity. The test is called the Periodogram Coefficient of Variation (PCOV) test, and is a frequency domain test based on a technique of spectral analysis. Application of the test is illustrated to both simulated and experimental data (EMC, physiological tremor, EEG). An additional role for the test as a useful tool in exploratory analysis of time series is highlighted. (C) 2009 Elsevier B.V. All rights reserved.",

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AU - Rosenberg, J. R.

AU - Rigas, A.

AU - Conway, B. A.

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N2 - in one approach to spectral estimation, a sample record is broken into a number of disjoint sections, or data is collected over a number of discrete trials. Spectral parameters are formed by averaging periodograms across these discrete sections or trials. A key assumption in this approach is that of weak stationarity. This paper describes a simple test that checks if periodogram ordinates are consistent across sections as a means of assessing weak stationarity. The test is called the Periodogram Coefficient of Variation (PCOV) test, and is a frequency domain test based on a technique of spectral analysis. Application of the test is illustrated to both simulated and experimental data (EMC, physiological tremor, EEG). An additional role for the test as a useful tool in exploratory analysis of time series is highlighted. (C) 2009 Elsevier B.V. All rights reserved.

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KW - Spectral analysis

KW - Periodogram

KW - Time series

KW - Stationarity

KW - EEG

KW - EMG

KW - PHYSIOLOGICAL TREMOR

KW - MOTOR TASK

KW - SYNCHRONIZATION

KW - ELECTROMYOGRAMS

KW - IDENTIFICATION

KW - FREQUENCY

KW - SPECTRUM

KW - SIGNALS

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EP - 146

JO - Journal of Neuroscience Methods

JF - Journal of Neuroscience Methods

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ER -

A periodogram-based test for weak stationarity and consistency between sections in time series

Abstract

Keywords

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