A TimeXer-Based Numerical Forecast Correction Model Optimized by an Exogenous-Variable Attention Mechanism
Yongmei Zhang*, Tianxin Zhang, Linghua Tian
School of Artificial Intelligence and Computer Science, North China University of Technology, Beijing, 100144, China
* Corresponding Author: Yongmei Zhang. Email: 
(This article belongs to the Special Issue: Advances in Time Series Analysis, Modelling and Forecasting)
Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.073159
Received 11 September 2025; Accepted 04 November 2025; Published online 04 December 2025
Abstract
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.
Keywords
TimeXer model; exogenous variable attention mechanism; sea surface temperature; temporal-spatial features; forecast correction
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