Benchmark study on the statistical and machine learning on the prediction of Arctic sea ice concentration

The safe and efficient navigation of ships traversing the Arctic waters demands accurate information regarding sea ice concentrations. This study investigates the prediction of Arctic sea ice concentration using historical low-frequency sea ice concentration data, comparing the predictive effects of the SARIMA model and the XGBoost model. Moreover, the study examines the influence of the input data length on prediction accuracy when the XGBoost model is used for single-step predictions with high-frequency data and compares the forecasting performance of the XGBoost model for single-step and multi-step forecast. The experimental results demonstrate that the SARIMA model outperforms the XGBoost model in predicting low-frequency Arctic sea ice concentration data. Furthermore, the XGBoost model provides the most accurate predictions for single-step forecast with high-frequency data when the input data length is 50. The single-step forecast was more accurate than seven-step forecast when the input data length was 50 and the output data length was 7. Therefore, in practical applications, the appropriate model can be selected based on the specific sea ice prediction task and data situation, providing more reliable forecasting results for the planning and managing the Arctic shipping route.