  There is a recent growing interest in applying Deep Learning techniques to
tabular data, in order to replicate the success of other Artificial
Intelligence areas in this structured domain. Specifically interesting is the
case in which tabular data have a time dependence, such as, for instance
financial transactions. However, the heterogeneity of the tabular values, in
which categorical elements are mixed with numerical items, makes this
adaptation difficult. In this paper we propose a Transformer architecture to
represent heterogeneous time-dependent tabular data, in which numerical
features are represented using a set of frequency functions and the whole
network is uniformly trained with a unique loss function.
