In order to improve the accuracy and stability of ship trajectory prediction, A combined prediction method based on the differential autoregressive moving average model and the bidirectional cyclic neural network is proposed. The method uses the ARIMA model to make a preliminary prediction of the track, and then uses the LSTM neural network to correct the residual sequence. The experimental results show that this kind of prediction method can predict the ship's trajectory more accurately.


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    Title :

    Prediction of ship track based on ARIMA-LSTM


    Contributors:
    Yu, Chen (author) / Fu, Yuhui (author)

    Conference:

    Sixth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2021) ; 2021 ; Chongqing,China


    Published in:

    Proc. SPIE ; 12081


    Publication date :

    2022-02-07





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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