An Approach to Restoring Missing Data in an Experimental Sample

The paper deals with the problem of restoring missing experimental data in modeling tasks. Real data samples may have missing values for some variables or a study period. All this leads to the risk of building an inaccurate model and, as a result, the experiment failed. In the paper, this problem is considered on data of a real task. A non-linear interpolation model was built for the dependence of the concentration of chlorophyll in algae on the concentration of the pollutant dependent on time. The found model made it possible to restore missing data for the days when measurements were absent, as well as to find out on which day and at what concentration of the pollutant the algae would die.