Evaluating classical time-series models ARIMA, SARIMA, ARIMAX, and VAR for wave height forecasting

This study evaluates the suitability of classical time-series models for forecasting significant wave heights using real hydrometeorological observations, with the objective of improving data-driven operational decision-making in ship routing. Accurate short-term wave forecasts can enhance navigational safety, reduce fuel consumption, and minimize emissions. However, many existing Decision Support Systems (DSS) lack integrated forecasting capability to incorporate credible environmental datasets. To address this gap, this research develops a time series–based analytical framework employing four classical forecasting models: Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), ARIMA with Exogenous Variables (ARIMAX), and Vector Autoregressive (VAR). Models were trained and evaluated using observational data from the National Data Buoy Center station 41049 (South Bermuda) under both static and dynamic scenarios. The results indicate limited evidence of stable seasonality in the short-term wave record, and SARIMA showed weaker performance with higher percentage errors. ARIMAX provided comparatively better accuracy due to the influence of exogenous meteorological drivers, while overall findings support the null hypothesis that classical linear models have a restricted capability in capturing rapid wave fluctuations. These insights highlight the practical challenges of applying classical forecasting models to complex sea-state dynamics and underline the need for more advanced or hybrid approaches in DSS development for maritime operations. • Presents an objective-driven DSS framework for ship routing using classical time series models (ARIMA, ARIMAX, SARIMA, VAR). • Highlights the powerful impact of wave height forecasting in optimizing vessel routes and improving operational safety. • Supports cost savings through reduced fuel consumption, emissions, and crew discomfort in rough sea conditions. • Provides comparative insights into model selection for short-term maritime forecasting applications.