Time series forecasting (TSF) faces challenges in modeling complex intra-channel temporal dependencies and inter-channel correlations. Although recent research has highlighted the efficiency of linear architectures in capturing global trends, these models often struggle with non-linear signals. To address this gap, we conducted a systematic receptive field analysis of convolutional neural network (CNN) TSF models. We introduce the "individual receptive field" to uncover granular structural dependencies, revealing that convolutional layers act as feature extractors that mirror channel-wise attention while exhibiting superior robustness to non-linear fluctuations. Based on these insights, we propose ACFormer, an architecture designed to reconcile the efficiency of linear projections with the non-linear feature-extraction power of convolutions. ACFormer captures fine-grained information through a shared compression module, preserves temporal locality via gated attention, and reconstructs variable-specific temporal patterns using an independent patch expansion layer. Extensive experiments on multiple benchmark datasets demonstrate that ACFormer consistently achieves state-of-the-art performance, effectively mitigating the inherent drawbacks of linear models in capturing high-frequency components.

翻译：时间序列预测（TSF）在建模复杂的通道内时间依赖性和通道间相关性方面面临挑战。尽管近期研究强调了线性架构在捕捉全局趋势方面的效率，但这些模型往往难以处理非线性信号。为弥补这一不足，我们对卷积神经网络（CNN）TSF模型进行了系统性的感受野分析。我们引入"个体感受野"概念以揭示细粒度结构依赖性，发现卷积层作为特征提取器，能够反映通道注意力机制，同时对非线性波动表现出更强的鲁棒性。基于这些发现，我们提出了ACFormer架构，旨在融合线性投影的高效性与卷积的非线性特征提取能力。ACFormer通过共享压缩模块捕捉细粒度信息，借助门控注意力机制保持时间局部性，并利用独立的分块扩展层重建变量特定的时间模式。在多个基准数据集上的大量实验表明，ACFormer始终能实现最先进的性能表现，有效缓解了线性模型在捕捉高频分量方面的固有缺陷。