Maritime Autonomous Surface Ships (MASS) are in the development stage and they play an important role in the upcoming future. Present generation ships are semi-autonomous and controlled by the ship crew. The performance of the ship is predicted using the data collected from the ship with the help of machine learning and deep learning methods. Path planning for an autonomous ship is necessary for estimating the best possible route with minimum travel time and it depends on the weather. However, even during the navigation, there will be changes in weather and it should be predicted in order to reroute the ship. The weather information such as wave height, wave period, seawater temperature, humidity, atmospheric pressure, etc., is collected by ship external sensors, weather stations, buoys, and satellites. The present paper investigates the ensemble machine learning approaches and seasonality approach for wave data prediction. The historical meteorological data is collected from six stations near Puerto Rico offshore and Hawaii offshore. We explore ensemble machine learning techniques on the data collected. The collected data is divided into training and testing data and apply machine learning models to predict the test data. The hyperparameter optimization is performed to find the best parameters before fitting on train data, this is essential to find the best results. Multivariate analysis is performed with all the methods and errors are computed to find the best models.

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