用卷积结构提升时间序列预测对非线性信号的建模能力
ACFormer: Mitigating Non-linearity with Auto Convolutional Encoder for Time Series Forecasting
- 结合卷积与线性投影，增强对复杂时序模式的捕捉
- 在多个基准数据集上达到顶尖性能，尤其擅长高频成分建模
- 适合需要高精度时序预测的工业与金融场景
时间序列预测面临建模通道内复杂时序依赖与通道间相关性的挑战。尽管线性架构在捕捉全局趋势方面效率较高，但对非线性信号表现不佳。我们对卷积神经网络时间序列预测模型进行了系统的感受野分析，提出‘个体感受野’概念，揭示卷积层作为特征提取器，其行为类似通道注意力，且对非线性波动更具鲁棒性。基于此，我们提出ACFormer：通过共享压缩模块捕获细粒度信息，门控注意力保留时序局部性，独立补丁扩展层重构变量特异性时序模式。在多个基准数据集上的实验表明，ACFormer持续达到领先性能，有效缓解线性模型在捕捉高频成分时的固有缺陷。
原文摘要 · Abstract (English)
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.
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