Application of Improved LSTM Neural Network in Time-Series Prediction of Extreme Short-Term Wave

doi:10.16183/j.cnki.jsjtu.2021.438

Abstract

Abstract:

Efficient and accurate extreme short-term prediction is of great significance for the safety of ship and marine structures in actual sea waves. Due to the stochastic of actual sea waves, short-term prediction always uses time series analysis. The neural networks, particularly long short-term memory (LSTM) neural networks, have received increasing attention for their powerful forecasting capability in time series analysis. Based on this, an improved form of LSTM combining generative adversarial ideas is proposed, in which the frequency domain characteristics are embedded in the neural network to achieve coupled time-frequency domain information forecasting. The experimental test shows that the forecasting accuracy of this method is better than the results of traditional time series analysis methods and the LSTM neural network, and it is suitable for extreme short-term time series prediction for better ship maneuvering.

Key words: extreme short-term prediction, time-series analysis, long short-term memory (LSTM) neural network, generative adversarial

CLC Number:

U664.82

SHANG Fancheng, LI Chuanqing, ZHAN Ke, ZHU Renchuan. Application of Improved LSTM Neural Network in Time-Series Prediction of Extreme Short-Term Wave[J]. Journal of Shanghai Jiao Tong University, 2023, 57(6): 659-665.

Figures/Tables 8

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方法	MSE/MAE
方法	AR模型	LSTM模型	改进LSTM模型
提前5步	1.736/0.932	1.539/0.943	1.273/0.848
提前10步	2.499/1.207	2.311/1.175	2.294/1.179
提前15步	2.514/1.222	2.248/1.149	2.178/1.134
提前20步	3.839/1.501	3.256/1.395	3.236/1.385