You must have already imported your data into the app, as described in Preparing Time-Series Data.
To estimate time series spectral models in the System Identification app:
In the System Identification app, select Estimate > Spectral Models to open the Spectral Model dialog box.
In the Method list, select the spectral analysis method you want to use. For information about each method, see Selecting the Method for Computing Spectral Models.
Specify the frequencies at which to compute the spectral model in either of the following ways:
In the Frequencies field, enter either a
vector of values, a MATLAB® expression that evaluates to a vector, or a variable
name of a vector in the MATLAB workspace. For example,
Use the combination of Frequency Spacing and Frequencies to construct the frequency vector of values:
In the Frequency Spacing list, select
etfe, only the
Linear option is
In the Frequencies field, enter the number of frequency points.
For time-domain data, the frequency ranges from 0 to the Nyquist frequency. For frequency-domain data, the frequency ranges from the smallest to the largest frequency in the data set.
In the Frequency Resolution field, enter the
frequency resolution, as described in Controlling Frequency Resolution of Spectral Models. To use the default value,
default or leave the field empty.
In the Model Name field, enter the name of the correlation analysis model. The model name should be unique in the Model Board.
Click Estimate to add this model to the Model Board in the System Identification app.
In the Spectral Model dialog box, click Close.
To view the estimated disturbance spectrum, select the Noise spectrum check box in the System Identification app. For more information about working with this plot, see Noise Spectrum Plots.
To export the model to the MATLAB workspace, drag it to the To Workspace rectangle
in the System Identification app. You can view the power spectrum and the confidence
intervals of the resulting
idfrd model object using the
You can use the
spafdr commands to estimate power
spectra of time series for both time-domain and frequency-domain data. The following
table provides a brief description of each command.
You must have already prepared your data, as described in Preparing Time-Series Data.
The resulting models are stored as an
idfrd model object, which contains
SpectrumData and its variance. For multiple-output data,
SpectrumData contains power spectra of each output and the
cross-spectra between each output pair.
Estimating Frequency Response of Time Series
For example, suppose
y is time series data. The following
commands estimate the power spectrum
g and the periodogram
p, and plot both models with three standard deviation
g = spa(y); p = etfe(y); spectrum(g,p);
For detailed information about these commands, see the corresponding reference pages.