  Methods of deep learning have become increasingly popular in recent years,
but they have not arrived in compositional data analysis. Imputation methods
for compositional data are typically applied on additive, centered or isometric
log-ratio representations of the data. Generally, methods for compositional
data analysis can only be applied to observed positive entries in a data
matrix. Therefore one tries to impute missing values or measurements that were
below a detection limit. In this paper, a new method for imputing rounded zeros
based on artificial neural networks is shown and compared with conventional
methods. We are also interested in the question whether for ANNs, a
representation of the data in log-ratios for imputation purposes, is relevant.
It can be shown, that ANNs are competitive or even performing better when
imputing rounded zeros of data sets with moderate size. They deliver better
results when data sets are big. Also, we can see that log-ratio transformations
within the artificial neural network imputation procedure nevertheless help to
improve the results. This proves that the theory of compositional data analysis
and the fulfillment of all properties of compositional data analysis is still
very important in the age of deep learning.
