  Optimal frequency identification in astronomical datasets is crucial for
variable star studies, exoplanet detection, and asteroseismology. Traditional
period-finding methods often rely on specific parametric assumptions, employ
binning procedures, or overlook the regression nature of the problem, limiting
their applicability and precision. We aim to introduce a universal,
nonparametric kernel regression method for optimal frequency determination that
is generalizable, efficient, and robust across various astronomical data types.
FINKER uses nonparametric kernel regression on folded datasets at different
frequencies, selecting the optimal frequency by minimizing squared residuals.
This technique inherently incorporates a weighting system that accounts for
measurement uncertainties and facilitates multiband data analysis. We evaluate
our method's performance across a range of frequencies pertinent to diverse
data types and compare it with an established period-finding algorithm,
conditional entropy. The method demonstrates superior performance in accuracy
and robustness compared to existing algorithms, requiring fewer observations to
identify significant frequencies reliably. It exhibits resilience against noise
and adapts well to datasets with varying complexity.
