A TimeXer-Based Numerical Forecast Correction Model Optimized by an Exogenous-Variable Attention Mechanism

Marine forecasting is critical for navigation safety and disaster prevention. However, traditional ocean numerical forecasting models are often limited by substantial errors and inadequate capture of temporal-spatial features. To address the limitations, the paper proposes a TimeXer-based numerical forecast correction model optimized by an exogenous-variable attention mechanism. The model treats target forecast values as internal variables, and incorporates historical temporal-spatial data and seven-day numerical forecast results from traditional models as external variables based on the embedding strategy of TimeXer. Using a self-attention structure, the model captures correlations between exogenous variables and target sequences, explores intrinsic multi-dimensional relationships, and subsequently corrects endogenous variables with the mined exogenous features. The model’s performance is evaluated using metrics including MSE (Mean Squared Error), MAE (Mean Absolute Error), RMSE (Root Mean Square Error), MAPE (Mean Absolute Percentage Error), MSPE (Mean Square Percentage Error), and computational time, with TimeXer and PatchTST models serving as benchmarks. Experiment results show that the proposed model achieves lower errors and higher correction accuracy for both one-day and seven-day forecasts.