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<p>Time series forecasting finds extensive application across numerous real-world scenarios, yet faces challenges in modelling multi-period variability and exogenous variables. To address this, this paper proposes P2D-Former, a patch-based Transformer model integrating period-aware 2D structures with exogenous information fusion. This approach incorporates MSUnit and P2D-FFN to model complex periodic structures, while explicitly integrating exogenous variable information through an Intersect-Attention mechanism. Experiments demonstrate that P2D-Former achieves superior performance across multiple benchmark datasets for long-term forecasting, short-term forecasting, and high-missing-value imputation tasks, exhibiting significant improvements in both accuracy and robustness compared to existing approaches.</p>

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P2D-former for time-series forecasting with period-aware 2D representations and exogenous covariates

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月2日

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  • Hu Hong,
  • Guorong Chen,
  • Wang Jian,
  • Haixin Yi,
  • Zhang Bao,
  • Weijie Zhang,
  • Zhikang Ding

摘要

Time series forecasting finds extensive application across numerous real-world scenarios, yet faces challenges in modelling multi-period variability and exogenous variables. To address this, this paper proposes P2D-Former, a patch-based Transformer model integrating period-aware 2D structures with exogenous information fusion. This approach incorporates MSUnit and P2D-FFN to model complex periodic structures, while explicitly integrating exogenous variable information through an Intersect-Attention mechanism. Experiments demonstrate that P2D-Former achieves superior performance across multiple benchmark datasets for long-term forecasting, short-term forecasting, and high-missing-value imputation tasks, exhibiting significant improvements in both accuracy and robustness compared to existing approaches.