  Temporal Knowledge Graph (TKG) Forecasting aims at predicting links in
Knowledge Graphs for future timesteps based on a history of Knowledge Graphs.
To this day, standardized evaluation protocols and rigorous comparison across
TKG models are available, but the importance of simple baselines is often
neglected in the evaluation, which prevents researchers from discerning actual
and fictitious progress. We propose to close this gap by designing an intuitive
baseline for TKG Forecasting based on predicting recurring facts. Compared to
most TKG models, it requires little hyperparameter tuning and no iterative
training. Further, it can help to identify failure modes in existing
approaches. The empirical findings are quite unexpected: compared to 11 methods
on five datasets, our baseline ranks first or third in three of them, painting
a radically different picture of the predictive quality of the state of the
art.
