基于相似性分组的神经网络海事时间序列预测建模
Similarity Grouping-Guided Neural Network Modeling for Maritime Time Series Prediction
摘要：
可靠、准确的时间序列预测在航运业的经济投资、交通规划、港口规划设计等方面起着至关重要的作用。为了保证高质量的预测性能，我们建议首先采用经验模式分解（EMD）和集成EMD（EEMD）方法将原始时间序列分解为高频和低频分量。传统的神经网络（NN）方法可以很容易地直接预测低频分量。由于高频分量的数学规律性较弱，因此预测高频分量更加困难。为了利用高频分量内固有的自相似性，这些分量将被分成几个连续的小（重叠）段。然后选择具有高相似性的分组段，以形成更合适的训练数据集，用于传统的NN方法。这种重组策略可以帮助提高高频分量的预测精度。通过对预测的高频分量和低频分量进行积分来获得最终的预测结果。我们提出的三步预测框架受益于时间序列分解和相似段分组。港口货物吞吐量和船舶交通流量的实验表明，该方法在预测精度和鲁棒性方面具有优越的性能。
英文摘要：
Reliable and accurate prediction of time series plays a crucial role in maritime industry, such as economic investment, transportation planning, port planning and design, etc. The dynamic growth of maritime time series has the predominantly complex, nonlinear and non-stationary properties. To guarantee high-quality prediction performance, we propose to first adopt the empirical mode decomposition (EMD) and ensemble EMD (EEMD) methods to decompose the original time series into high- and low-frequency components. The low-frequency components can be easily predicted directly through traditional neural network (NN) methods. It is more difficult to predict high-frequency components due to their properties of weak mathematical regularity. To take advantage of the inherent self-similarities within high-frequency components, these components will be divided into several continuous small (overlapping) segments. The grouped segments with high similarities are then selected to form more proper training datasets for traditional NN methods. This regrouping strategy can assist in enhancing the prediction accuracy of high-frequency components. The final prediction result is obtained by integrating the predicted high- and low-frequency components. Our proposed three-step prediction frameworks benefit from the time series decomposition and similar segments grouping. Experiments on both port cargo throughput and vessel traffic flow have illustrated its superior performance in terms of prediction accuracy and robustness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
