  Predicting the future evolution of complex systems is one of the main
challenges in complexity science. Based on a current snapshot of a network,
link prediction algorithms aim to predict its future evolution. We apply here
link prediction algorithms to data on the international trade between
countries. This data can be represented as a complex network where links
connect countries with the products that they export. Link prediction
techniques based on heat and mass diffusion processes are employed to obtain
predictions for products exported in the future. These baseline predictions are
improved using a recent metric of country fitness and product similarity. The
overall best results are achieved with a newly developed metric of product
similarity which takes advantage of causality in the network evolution.
