  Long-term observational data have information on the magnetic cycles of
active stars and that of the Sun. The changes in the activity of our central
star have basic effects on Earth, like variations in the global climate.
Therefore understanding the nature of these variations is extremely important.
The observed variations related to magnetic activity cannot be treated as
stationary periodic variations, therefore methods like Fourier transform or
different versions of periodogramms give only partial information on the nature
of the light variability. We demonstrate that time-frequency distributions
provide useful tools for analyzing the observations of active stars. With test
data we demonstrate that the observational noise has practically no effect on
the determination in the the long-term changes of time-series observations of
active stars. The rotational signal may modify the determined cycles, therefore
it is advisable to remove it from the data. Wavelets are less powerful in
recovering complex long-term changes than other distributions which are
discussed. Applying our technique to the sunspot data we find a complicated,
multi-scale evolution in the solar activity.
