the ship roll data. The results show that the mixed model has better prediction accuracy than the single depth feedforward neural network and AR model. [PERSON] et al. (2015) [5] developed a novel order selection approach based on ship impulse response function (IRF). Additionally, the AR model using the IRF order selection is designated as an IRF-AR model. The simulation results of an S175 container ship showed the superiority of the IRF-AR model to the conventional model in prediction accuracy, efficiency, and algorithm adaptation. [PERSON] et al. (2018) [6] established an ensemble mechanism by combining the discrete wavelet transform and variable radial basis function (RBF) networks. The effectiveness of the proposed discrete wavelet transform (DWT)-based real-time roll prediction scheme was demonstrated by short-term ship roll motion prediction experiments based on the actual ship roll motion measurements collected. [PERSON] and [PERSON] (2019) [7] optimized the least squares support vector regression (SVR) model through the chaotic cloud particle swarm optimization algorithm and combined it with the periodogram estimation method. They developed this hybrid model algorithm for predicting ship motion and carried out prediction analysis with two sets of real ship data. The results show that the hybrid model has better prediction accuracy than the classical prediction model. Based on the SVR, [PERSON] et al. (2015) [8] proposed a hybrid model for forecasting the nonlinear and non-stationary short-term prediction of ship motion. The difficulty of processing non-stationary data in the SVR model was overcome by using the mode decomposition (EMD) technology, which has higher accuracy for the very short-term prediction results of the ship motion. In recent years, the artificial neural network has been widely used in the ship motion prediction. The cyclic neural network is a kind of time recursive neural network that can process the information of sequence structure. [PERSON] (2005) [9] established a diagonal recurrent neural network (RNN) model for large ship motion prediction. The results were compared with the AR prediction method and the periodogram prediction method. The prediction results show the feasibility of the algorithm. The long short-term memory (LSTM) neural network [10] is a special cyclic neural network. Its special gating structure makes it have the ability of long-term memory and can process time series data more effectively. [PERSON] et al. (2017) [11] used the LSTM neural network and traditional RNN and AR models to carry out a comparative analysis of ship motion prediction, proving that the LSTM neural network has better prediction accuracy. [PERSON] et al. (2021) [12] assessed and compared the performance of RNN, LSTM, and gated recurrent units (GRU) models using a data set obtained from computational fluid dynamics (CFD) simulations of a self-propelled destroyer-type vessel. The objective was to obtain about a prediction of 20 s ahead. The three methods provided promising and comparable results. [PERSON] et al. (2019) [13] carried out prediction research on USV roll data by combining the convolution neural network (CNN) and LSTM neural network. The results proved that the combined prediction model has better a prediction effect. The EMD-LSTM combined forecasting model established by [PERSON] et al. (2020) [14] has better forecasting accuracy than the single LSTM model. [PERSON] et al. (2019) [15] integrated the particle swarm optimization algorithm (PSO) and the EMD algorithm on the LSTM neural network algorithm. The results show that the hybrid model can effectively improve the prediction accuracy. [PERSON] et al. (2023) [16] proposed a prediction algorithm based on EMD and adaptive PSO-LSTM with the sliding window approach. The simulation results demonstrate that the proposed model performs well in terms of predicting the nonlinear and nonstationary ship roll and pitch. [PERSON] et al. (2021) [17] combined an attention mechanism (AM) with LSTM, and carried out prediction tests using real ship data.