  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.
