Abstract: The temproral link prediction method is investigated in this paper. The disadvantages of the static link prediction methods are analyzed, considering that ignoring the evolving information of networks will lead to a negative impact on link predicting. The concept of link prediction error is proposed to describe the evolving information of networks, and a temporal link prediction method is proposed based on the prediction error correction. Firstly, several static link prediction are carried out using each graph in the previous periods window, and then the prediction errors are recorded and used for calculating the modification value. At last, the final prediction result is acquired through refining the static prediction result with the modification value. Several experiments are conducted using two real network datasets. The results show that the proposed method achieves better performance than the static link prediction methods and a typical temporal link prediction method. In addition, it can be found that a relation of mirror symmetry exists between prediction error series and total link number series, which demonstrates the universality of the proposed method.