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Time-averaged wavelet spectrum

returns the time-averaged wavelet power spectrum of the signal `tavgp`

= timeSpectrum(`fb`

,`x`

)`x`

using the continuous wavelet transform (CWT) filter bank `fb`

. By
default, `tavgp`

is obtained by time-averaging the magnitude-squared
scalogram over all times. The power of the time-averaged wavelet spectrum is normalized to
equal the variance of `x`

.

`[___] = timeSpectrum(___,`

specifies additional options using name-value pair arguments. These arguments can be added
to any of the previous input syntaxes. For example,
`Name,Value`

)`'Normalization','none'`

specifies no normalization of the
time-averaged wavelet spectrum.

`timeSpectrum(___)`

with no output arguments plots the
time-averaged wavelet power spectrum in the current figure.

[1] Lilly, J. M., and J.-C. Gascard.
“Wavelet Ridge Diagnosis of Time-Varying Elliptical Signals with Application to an Oceanic
Eddy.” *Nonlinear Processes in Geophysics* 13, no. 5 (September 14,
2006): 467–83. https://doi.org/10.5194/npg-13-467-2006.

[2] Torrence, Christopher, and Gilbert
P. Compo. “A Practical Guide to Wavelet Analysis.” *Bulletin of the American
Meteorological Society* 79, no. 1 (January 1, 1998): 61–78.
https://doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2.

[3] Percival, Donald B., and Andrew T.
Walden. *Wavelet Methods for Time Series Analysis*. Cambridge Series in
Statistical and Probabilistic Mathematics. Cambridge ; New York: Cambridge University Press,
2000.

[4] Lilly, J.M., and S.C. Olhede.
“Higher-Order Properties of Analytic Wavelets.” *IEEE Transactions on Signal
Processing* 57, no. 1 (January 2009): 146–60.
https://doi.org/10.1109/TSP.2008.2007607.