The performance of link prediction on Temporal Knowledge Graphs (TKGs) has improved in the last decade via development of several new and diverse Knowledge Graph Embedding (KGE) methods. In this paper, the strengths and weaknesses of several temporal KGE methods are examined, with focus on the temporal data density of the overall Knowledge Graph (KG) and temporal properties of relations determined by the structure of the surrounding KG. The results of this analysis are used to create a new ensemble voting method ORB-E with query-specific model weights based on the characteristics of the query and model performance relative to that characteristic. The characteristics are target of prediction, temporal data density, properties of relations and overall scores of models. The rules that determine the weights of each model reflect which query characteristics have the most importance for different methods, datasets, and overall. We find that prediction target is the most influential characteristic, and that query-specific weight assignment has better performance than a static weight assignment. Link prediction preformance is increased for time prediction on temporally dense data but not other prediction targets and the theoretical expressivity of methods does not always reflect the actual performance of the models. Additionally, the domain of time predictions is analyzed to determine their accuracy under different circumstances and we find that most methods are not capable of predicting time within an acceptable error margin. A strategy that utilizes the continuous nature of time information and combines predictions of multiple models is presented, and can be used to improve time predictions when models have equal precision.