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Power-law spectra, also called "1/f-noise" is a ubiquitous property of the timeseries of many real world processes. In a sense, 1/f-noise is a universality class. http://www.scholarpedia.org/article/1/f_noise
It would be a great addition to this package if we could generate timeseies surrogates with a given spectral 1/f^beta power spectrum.
The type PowerLawSpectrum can either has as input the input timeseries, in which case it deduces beta by doing a linear fit to the power spectrum, or it could have as input beta directly.
The type PowerLawSpectrum can either has as input the input timeseries, in which case it deduces beta by doing a linear fit to the power spectrum, or it could have as input beta directly.
Do you mean a linear fit to the log transformed power spectrum, or am I missing something?
Power-law spectra, also called "1/f-noise" is a ubiquitous property of the timeseries of many real world processes. In a sense, 1/f-noise is a universality class. http://www.scholarpedia.org/article/1/f_noise
It would be a great addition to this package if we could generate timeseies surrogates with a given spectral 1/f^beta power spectrum.
The type
PowerLawSpectrum
can either has as input the input timeseries, in which case it deduces beta by doing a linear fit to the power spectrum, or it could have as input beta directly.The following paper, section 2.2.1, describes how to generate power law surrogates: https://npg.copernicus.org/articles/28/311/2021/#section2
Apparently, code to generate power-law surrogates exists here: https://github.com/EarthSystemDiagnostics/paleospec
https://earthsystemdiagnostics.github.io/paleospec/reference/SimPLS.html
https://earthsystemdiagnostics.github.io/paleospec/reference/SimFromEmpiricalSpec.html
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