摘要： 针对原始序列特征表征能力有限，以及现有“分解-集成”模型在长时序预测任务中分解策略引发的信息丢失问题，本文提出一种融合注意力机制的高维特征序列增强网络（HDFSENet）。该网络通过整合嵌入技术、混合专家分解模块（MOEDecomp）与特征序列增强模块（FSE）以捕捉时间序列的内在特征，同时减少分解策略中的信息丢失。首先，该方法借助三种嵌入技术（数值、位置与时间嵌入），强化原始时间序列的特征信息。其次，通过MOEDecomp模块将增强后的时间序列分解为趋势特征序列与季节特征序列。随后，构建基于注意力机制的特征序列增强模块，以捕捉分解后趋势特征序列与季节特征序列间的相互作用，从而提升特征的表征能力。之后，将这些交互特征作为关键变量整合到模型中，进一步提高预测精度。最后，在多个基准数据集上对该模型的有效性展开验证。实验结果显示，HDFSENet在MSE、MAE等评价指标上，显著优于多个基准模型，表明本文提出的模型为更精准地实现时间序列预测提供了可靠方法。

Abstract: To address the insufficient representation of original sequence features and the information loss caused by the decomposition strategy of existing "decomposition-ensemble" forecasting models in long-term time series prediction tasks, this paper proposes a High-Dimensional Feature Series Enhancement Network (HDFSENet) incorporating an attention mechanism. The network integrates embedding techniques, the Mixture of Experts Decomposition (MOEDecomp) block, and the Feature Series Enhancement (FSE) block to capture the inherent characteristics of time series while reducing information loss in decomposition strategies. Firstly, the method strengthens the feature information of the original time series through three embedding techniques: value embedding, position embedding, and temporal embedding. Secondly, the enhanced time series is decomposed into trend feature series and seasonal feature series via the MOEDecomp block. Subsequently, an FSE block based on the attention mechanism is constructed to capture the interactions between the decomposed trend and seasonal feature series, thereby improving the representation capability of these features. Afterwards, these interaction features are integrated into the model as key variables to further enhance forecasting accuracy. Finally, the effectiveness of the model is verified on multiple benchmark datasets. Experimental results demonstrate that HDFSENet significantly outperforms several benchmark models in evaluation metrics such as MSE and MAE, indicating that the proposed model provides a reliable approach for more accurate time series forecasting.