Temporal knowledge graph (TKG) reasoning has attracted significant attention of researchers. Existing TKG reasoning methods have made great progress through modeling historical information. However, the time-variability problem and unseen entity (relation) problem are still two major challenges that hinder the further improvement of this field. Moreover, since the structural information and temporal dependencies of the historical subgraph sequence have to be modeled, the traditional embedding-based methods often have high time consumption in the training and predicting processes, which greatly limits the application of the reasoning model in real-world scenarios. To address these issues, this study proposes a frequency statistical network for TKG reasoning, namely FS-Net. On the one hand, FS-Net continuously generates time-varying scores for the predictions at the changing timestamps based on the latest short-term historical fact frequency statistics. On the other hand, based on the fact frequency statistics at the current timestamp, FS-Net supplements the historical unseen entities (relations) for the predictions; specially, FS-Net does not need training, and has a very high time efficiency. The experiments on two TKG benchmark datasets demonstrate that FS-Net has a great improvement compared with the baseline models.