Abstract
Accurate vessel traffic flow prediction is significant for maritime traffic guidance and control. According to the characteristics of vessel traffic flow data, a new hybrid model, named DWT–Prophet, is proposed based on the discrete wavelet decomposition and Prophet framework for the prediction of vessel traffic flow. First, vessel traffic flow was decomposed into a low-frequency component and several high-frequency components by wavelet decomposition. Second, Prophet was trained to predict the components, respectively. Finally, the prediction results of the components were reconstructed to complete the prediction. The experimental results demonstrate that the hybrid DWT–Prophet outperformed the single Prophet, long short-term memory, random forest, and support vector regression (SVR). Moreover, the practicability of the new forecasting method was improved effectively.
Highlights
Han et al [23] proposed ensemble learning model based on a recurrent neural network (RNN), extreme learning machine (ELM), support vector regression (SVR), and least squares support vector machine (LSSVM) to forecast the exhaust emissions, and the method made full use of all four models in this way. He et al [24] built a SARIMA–CNN–long short-term memory (LSTM) model to predict daily travel demand, used the seasonal autoregressive integrated moving average (SARIMA) model to capture linear features in the data, and used a convolutional neural network (CNN) and LSTM to mine nonlinear features; the results showed that the prediction accuracy of the hybrid model was significantly higher than those of a single SARIMA and LSTM
In order to verify the performance of the hybrid model based on discrete wavelet transform (DWT)–Prophet proposed in this paper, the Prophet framework, the LSTM, the RF, and the SVR model were selected as the comparison models to predict the vessel traffic flow in the testing data set
Prophet crete decomposition was employed to decompose the vessel and traffic data into framework was introduced to the prediction for every component separately; in this way, one low-frequency component and three high-frequency components, and the Prophet the final accurate prediction result was obtained by reconstructing the prediction result of framework was introduced to the prediction for every component separately; in this way, every component
Summary
The core idea of wavelet analysis is to decompose and reconstruct signals and overcome the limitations of short-time Fourier transform by providing time–frequency windows that vary with frequency. Compared with the original signal, the decomposed components can more effectively and accurately mine the potential information of the data. Compared with the original signal, the decom of 16 posed components can more effectively and accurately mine the potential information of the data
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