2008
DOI: 10.1007/s11004-008-9170-8
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Cross-Wavelet Analysis: a Tool for Detection of Relationships between Paleoclimate Proxy Records

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Cited by 35 publications
(24 citation statements)
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“…Unlike many traditional mathematical methods (for example, Fourier analysis), the wavelet approach can be used to analyse time series that contain non-stationary spectral power at many different frequencies 21 . For geological time series, although visual comparison of plots is commonly used, cross-wavelet analysis permits detection, extraction and reconstruction of relationships between two non-stationary signals simultaneously in frequency (or scale) and time (or location) 69 . The continuous wavelet transform (CWT; Supplementary Fig.…”
Section: Methodsmentioning
confidence: 99%
“…Unlike many traditional mathematical methods (for example, Fourier analysis), the wavelet approach can be used to analyse time series that contain non-stationary spectral power at many different frequencies 21 . For geological time series, although visual comparison of plots is commonly used, cross-wavelet analysis permits detection, extraction and reconstruction of relationships between two non-stationary signals simultaneously in frequency (or scale) and time (or location) 69 . The continuous wavelet transform (CWT; Supplementary Fig.…”
Section: Methodsmentioning
confidence: 99%
“…In the present application, the modulus of the cross-wavelet transform W xy (a, b) represents the cross-amplitudes of x(t) and y(t), and it is defined as the cross-wavelet power (Prokoph and El Bilali, 2008) and the plot of W xy (a, b) 2 is called a coscalogram. It has the advantage of revealing pockets of high and low correlation in different frequency bands.…”
Section: Theoretical Analysismentioning
confidence: 99%
“…A cross-wavelet analysis was performed on the LIP area within the 10°N to 10°S humid belt and proxy-CO 2 time series using the Grinsted et al (2004) Matlab toolbox. Cross-wavelet analysis of time series was proposed by Prokoph and El Bilali (2008) to be used on geologic time series, particularly those with large uncertainties and noise. The analysis window is much narrower than that of other techniques such as a Fourier transform, allowing for greater temporal resolution and the method be applied to both cyclic, and as in this case, time series with chaotic elements.…”
Section: Methodsmentioning
confidence: 99%