  In practice , quite often there is a need to describe the values set by means
of a table in the form of some functional dependence . The observed values ,
due to certain circumstances , have an error . For approximation, it is
advisable to use a functional dependence that would allow smoothing out the
errors of the observation results. Approximation allows you to determine
intermediate values of functions that are not listed among the data in the
observation table. The use of exponential series for data approximation allows
you to get a result no worse than from approximation by polynomials In the
economic scientific literature, approximation in the form of power functions,
for example, the Cobb-Douglas function, has become widespread. The advantage of
this type of approximation can be called a simple type of approximating
function , and the disadvantage is that in nature not all processes can be
described by power functions with a given accuracy. An example is the GDP
indicator for several decades . For this case , it is difficult to find a power
function approximating a numerical series . But in this case, as shown in this
article, you can use exponential series to approximate the data. In this paper,
the time series of Hungary's GDP in the period from 1992 to 2022 was
approximated by a series of thirty exponents of a complex variable. The use of
data smoothing by the method of triangles allows you to average the data and
increase the accuracy of approximation . This is of practical importance if the
observed random variable contains outliers that need to be smoothed out.
