Graphics Programs Reference
In-Depth Information
subplot(1,2,2), plot(f,abs(Pxxt))
xlabel('Frequency')
ylabel('Power')
To eliminate the long-term trend, we use the function
detrend
.
ydt = detrend(yt);
subplot(2,1,1)
plot(t,y,'b-',t,yt,'r-'), axis([0 200 -4 4])
subplot(2,1,2)
plot(t,y,'b-',t,ydt,'r-'), axis([0 200 -4 4])
The corresponding spectrum does not show the low-frequency peak any-
more. Some data contain a high-order trend that can be removed by fi tting
a higher-order polynomial to the data and by subtracting the corresponding
x
(
t
) values.
5.4 Crossspectral Analysis
Crossspectral analysis correlates two time series in the frequency domain.
The crosscovariance is as a measure for the variance in two signals over a
time lag
k
. An unbiased estimator of the crosscovariance
cov
xy
of two signals
x
(
t
) and
y
(
t
) with
N
data points sampled at constant time intervals ยจ
t
is
The crosscovariance series again depends on the amplitudes of
x
(
t
) and
y
(
t
).
Normalizing the covariance by the standard deviations of
x
(
t
) and
y
(
t
) yields
the crosscorrelation sequence.
In practice, the same methods and modifi cations outlined in the previous
chapter are used to compute the crossspectral density. In addition to the two
autospectra of
x
(
t
) and
y
(
t
) and the crossspectrum,
