3. Quick overview of spectral analysis methods¶
This section gives you a quick overview of the spectral analysis methods and classes that are available in spectrum. You will find the different classes associated to each PSD estimates. A functional approach is also possible but is not described here. See the reference guide for more details.
3.1. Non-parametric classes¶
In addition to the Fourier-based methods, there are 3 types of non-parametric methods:
3.2. Autoregressive spectral estimation¶
There are essentially 3 methods to estimate the autoregressive (AR) parameters.
The first one uses the autocorrelation sequence such as in the so-called
Yule-Walker method (see
pyule). A second method uses the reflection coefficient method such as in the Burg algorithm (see
pburg). These methods minimise the forward prediction error (and backward) using Levinson recursion. Finally, a third important category of AR parameter method is based on the least squares linear prediction, which can be further decomposed into 2 categories. One that separate the minimization of the forward and backward linear prediction squared errors such as the
autocorrelation or covariance methods (see
pcovar). Another one that performs a
combined minimization of the forward and backward prediction squared errors
(modified covariance) (see