Abstract: Pavement anomalies can bring inconvenience to drivers and passengers，and even cause traffic acci-dents． A pavement anomaly detection algorithm based on sensor time series data was proposed． Considering the problem that sensing signals collected during driving are strongly high-order sequential correlative，high-order dy-namic Bayesian network classifier was constructed to realize the anomalies detection． Firstly，correlation analysis and Granger causality test were used to initialize the network structure． Secondly，the sensing signals were decom-posed by wavelet transform，and convolution neural network was used to realize network embedding． Finally，link prediction with minimal description length was used to optimize the network structure． The results show that，com-pared with the traditional method of time series classification，the proposed method can reduce fallout rate and missing rate，and increase F1 score on the sequential correlative signals，and thus is more robust．

Key words: dynamic Bayesian network, pavement anomaly detection algorithm, network embedding, correlation analysis, Granger causality test, time series classification