  In this paper, we describe our approach to the Wikipedia Participation
Challenge which aims to predict the number of edits a Wikipedia editor will
make in the next 5 months. The best submission from our team, "zeditor",
achieved 41.7% improvement over WMF's baseline predictive model and the final
rank of 3rd place among 96 teams. An interesting characteristic of our approach
is that only temporal dynamics features (i.e., how the number of edits changes
in recent periods, etc.) are used in a self-supervised learning framework,
which makes it easy to be generalised to other application domains.
