  In the framework of collinear QCD factorization, the leading twist scattering
amplitudes for deeply virtual Compton scattering (DVCS) and timelike Compton
scattering (TCS) are intimately related thanks to analytic properties of
leading and next-to-leading order amplitudes. We exploit this welcome feature
to make data-driven predictions for TCS observables to be measured in near
future experiments. Using a recent extraction of DVCS Compton form factors from
most of the existing experimental data for that process, we derive TCS
amplitudes and calculate TCS observables only assuming leading-twist dominance.
Artificial neural network techniques are used for an essential reduction of
model dependency, while a careful propagation of experimental uncertainties is
achieved with replica methods. Our analysis allows for stringent tests of the
leading twist dominance of DVCS and TCS amplitudes. Moreover, this study helps
to understand quantitatively the complementarity of DVCS and TCS measurements
to test the universality of generalized parton distributions, which is crucial
e.g. to perform the nucleon tomography.
