Comparative Evaluation of Statistical Time-Series Models for Marine Heatwave Forecasting in the North Indian Ocean

Over the last forty years, there is an increasing rate in the occurrence of intense oceanic heatwaves in the Indian Ocean. Marine heatwaves are extremely dangerous to the ecosystems, climatic conditions, and the economies. Sea surface temperature (SST) anomalies also need to be forecasted accurately so as to be able to detect and monitor these events at an early stage. This paper uses the OISST data to create an all-encompassing statistical forecasting model of Indian Ocean SST anomalies. The work considers seven models, ARIMA, SARIMA, Holt-Winters, Seasonal Exponential Smoothing, Theta, STL+ETS, and Prophet, which are applied and tested on the use of anomaly time-series based on 1981-2025. STL+ETS turned out to be the most productive model with the smallest errors (MAE: 0.134-0.161, MSE: 0.032-0.041, RMSE: 0.179–0.203). The results indicate the potential high usefulness of classical statistical models in operational MHW early-warning applications and provide a basis on how statistical forecasting may be used together with machine-learning and hybrid ocean prediction systems