Non-Stationary Order of Vector Autoregression in Significant Ocean Wave Forecasting

This paper studies the implementation of non-stationary multivariate time series model to fit the ocean wave data. A model comprises from a regression term and associate with exogenous variables in a particular time horizon. Because of the trend fluctuation in the data leading to unstable process, differentiated data are used in fitting the model. The approach suggested is applied to the finite order of Vector Autoregression for an improvement in prediction simultaneously of ocean wave by carrying out wind-related information to waves. The proposed model is compared with linear simple autoregressive model. The performance of both forecasting procedures is assessed by RMSE of well-known error measures. The forecast based on the proposed methodology indicated that it can be regarded as a promising method for wave ocean prediction, it outperforms using 4-order Vector Autoregression.