  Multiple algorithms of time series analysis are briefly reviewed and
partially illustrated by application to the visual observations of the
semi-regular variable DY Per from the AFOEV database. These algorithms were
implemented in the software MCV (Andronov and Baklanov, 2004), MAVKA (Andrych
and Andronov, 2019; Andrych et al., 2019). Contrary to the methods of physical
modeling, which need to use too many parameters, many of which may not be
determined from pure photometry (like temperature/spectral class, radial
velocities, mass ratio), phenomenological algorithms use smaller number of
parameters. Beyond the classical algebraic polynomials, in the software MAVKA
are implemented other algorithms, totally 21 approximations from 11 classes.
Photometric observations of DY Per from the AFOEV international database were
analyzed. The photometric period has switched from P=851.1d(4.1) to
P=780.5d(2.7) after JD 2454187(9)d. A parameter of sinusoidality is introduced,
which is equal to the ratio of effective semi-amplitudes of the signal
determined from a sine fit and the running parabola scalegram.
