Study on the prediction performance of ship motion in waves by LSTM under missing data

Ship motion prediction is essential in marine engineering, but missing data caused by sensor faults or signal interruptions often degrades the accuracy of long short-term memory (LSTM) models. This study investigates how different missing data rates and imputation methods affect LSTM prediction performance. A ship-motion dataset under various speeds and wave conditions was used to examine model feasibility and hyperparameter sensitivity. Traditional filling strategies, including zero and mean filling, were compared under missing data scenarios. Results show that data loss significantly reduces prediction accuracy. The mean-filling method generally performs better than zero-filling, though its effectiveness decreases with higher data diversity. Proper data clustering can effectively enhance its performance.