The inference of the network traffic matrix from partial measurement data becomes increasingly critical for various network engineering tasks, such as capacity planning, load balancing, path setup, network provisioning, anomaly detection, and failure recovery. The recent study shows it is promising to more accurately interpolate the missing data with a three-dimensional tensor as compared to interpolation methods based on two-dimensional matrix. Despite the potential, it is difficult to form a tensor with measurements taken at varying rate in a practical network. To address the issues, we propose Reshape-Align scheme to form the regular tensor with data from dynamic measurements, and introduce user-domain and temporal-domain factor matrices which takes full advantage of features from both domains to translate the matrix completion problem to the tensor completion problem based on CP decomposition for more accurate missing data recovery. Our performance results demonstrate that our Reshape-Align scheme can achieve significantly better performance in terms of two metrics: error ratio and mean absolute error (MAE).