Research on real-time analysis and decision support system of intelligent ship navigation data

In the study of real-time analysis and decision support system of intelligent ship navigation data, how to efficiently process and predict complex navigation data has become a key problem. In this paper, a new method combining Support Vector Machine (SVM) and AutoRegressive Integrated Moving Average (AutoRegressive Integrated Moving Average) is proposed. ARIMA's new method supra-autoregressive Integrated (SARI). SARI method uses SVM technology to deal with the nonlinear feature relationship, and uses ARIMA model to capture the trend and periodic change of time series data, so as to realize the real-time analysis and prediction of intelligent ship navigation data. Ultimately, the results from the comparative experimentation and analysis between the presented framework and the conventional optimization algorithm demonstrate that the SARI method exhibits notable advantages in boosting prediction accuracy and fortifying the model's robustness. This proves the reliability and validity of the model in real-time analysis of intelligent ship navigation data.