# Similarity Grouping-Guided Neural Network Modeling for Maritime Time Series Prediction

## TL;DR

This paper introduces a novel neural network framework that combines time series decomposition and similarity-based segment grouping to improve maritime time series prediction accuracy, especially for complex high-frequency components.

## Contribution

It proposes a three-step prediction framework utilizing EMD/EEMD decomposition and similarity grouping to enhance neural network predictions of maritime time series.

## Key findings

- Superior prediction accuracy demonstrated on port cargo throughput data
- Enhanced robustness in maritime time series forecasting
- Effective handling of high-frequency components through segment grouping

## Abstract

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.