Traffic forecasting is an important part in realising intelligent traffic management, which helps traffic controllers and travellers make effective decisions. However, traffic forecasting accuracy is often affected by missing traffic data due to hardware and software failure. Therefore, accurate prediction based on incomplete traffic data is an important problem as well as a challenge. Though many approaches recover the missing values before prediction, the errors from the data‐filling step are likely to cause additional bias to the prediction result. Besides, this tactic is difficult to guarantee the timeliness and may impede real‐time prediction. In this case, a traffic forecasting model is proposed to directly predict traffic data with missing values. This model develops tensor formed dynamic mode decomposition, recording the dynamic information of traffic data into a state transition tensor. In addition, the model takes low rank property of the dynamic tensor and the similarity of temporal variation trend into consideration. In order to verify the effectiveness and the robustness of the proposed model, experiments were performed on two real‐world time series datasets. The results demonstrate that the model achieves better performance on forecasting than other baseline approaches under the impact of missing data.

Real‐time passenger‐flow anomaly detection at all metro stations is a very critical task for advanced Internet management. Robust principal component analysis (RPCA) based method has often been employed for anomaly detection task of multivariate time series data. However, it ignores the spatio‐temporal features of regular passenger‐flow patterns, resulting in a decrease in the accuracy of anomaly detection. In this paper, RT‐STRPCA model integrating temporal periodicity and spatial similarity is proposed to address the above issues. RT‐STRPCA model detects anomalies by decomposing the observation data into normal component and anomaly component. The spatio‐temporal constraints are taken into account to extract anomalies more accurately. The real‐time anomaly detection are realized by a sliding window. The performance of RT‐STRPCA model is evaluated on synthetic datasets and real‐world datasets. The experimental results on synthetic datasets demonstrate that the method achieves more accurate real‐time anomaly detection than baseline approaches and the result on real‐world datasets verify the utility and effectiveness of the proposed method.