Ship Traffic Flow Prediction with Bidimensional Matrix Mode Decomposition

Bidimensional empirical mode decomposition and temporal regularized matrix factorization are used for ship traffic flow prediction. Normal traffic flow time series are reorganized as 2 D time matrix（day X Time slot） and the latter is decomposed into high frequency matrix and low frequency matrix. The high frequency matrix reflects the effects of fast changing factors and the low frequency matrix reflects the effects of steady factors. The two matrixes are processed separately with temporal regularized matrix factorization and fused to get the resultant prediction, The resultant prediction is compared with results obtained by using other models, such as BEMD-TRMF, GM（1,1）, ARIMA, BPNN, WNN, LSTM and TRMF, showing the advantage of the BEMD-TRMF model in accuracy（accuracy of 3% is achieved）.