  Modern-day time-domain photometric surveys collect a lot of observations of
various astronomical objects and the coming era of large-scale surveys will
provide even more information on their properties. Spectroscopic follow-ups are
especially crucial for transients such as supernovae and most of these objects
have not been subject to such studies. }{Flux time series are actively used as
an affordable alternative for photometric classification and characterization,
for instance, peak identifications and luminosity decline estimations. However,
the collected time series are multidimensional and irregularly sampled, while
also containing outliers and without any well-defined systematic uncertainties.
This paper presents a search for the best-performing methods to approximate the
observed light curves over time and wavelength for the purpose of generating
time series with regular time steps in each passband.}{We examined several
light curve approximation methods based on neural networks such as multilayer
perceptrons, Bayesian neural networks, and normalizing flows to approximate
observations of a single light curve. Test datasets include simulated PLAsTiCC
and real Zwicky Transient Facility Bright Transient Survey light curves of
transients.}{The tests demonstrate that even just a few observations are enough
to fit the networks and improve the quality of approximation, compared to
state-of-the-art models. The methods described in this work have a low
computational complexity and are significantly faster than Gaussian processes.
Additionally, we analyzed the performance of the approximation techniques from
the perspective of further peak identification and transients classification.
The study results have been released in an open and user-friendly Fulu Python
library available on GitHub for the scientific community.
