HPMixer：用于多变量时间序列预测的分层拼接
HPMixer: Hierarchical Patching for Multivariate Time Series Forecasting
AI总结：
HPMixer通过分层拼接机制结合周期性建模与多尺度残差学习，提升多变量时间序列预测性能。
摘要：
在长期多变量时间序列预测中，有效捕捉周期性模式和残差动态至关重要。为在标准深度学习基准设置中解决这一问题，我们提出了分层拼接混合器（HPMixer），以解耦且互补的方式建模周期性和残差。周期性组件利用可学习的循环模块[7]，并增强非线性通道级MLP以提高表达能力。残差组件通过可学习的平稳小波变换（LSWT）提取稳定且位移不变的频域表示。随后，通道混合编码器建模显式的跨通道依赖性，而两级非重叠分层拼接机制捕捉粗粒度和细粒度的残差变化。通过将解耦的周期性建模与结构化、多尺度残差学习相结合，HPMixer提供了一个有效的框架。在标准多变量基准上的广泛实验表明，HPMixer在与最近基线相比时实现了竞争性或最先进的性能。
英文摘要：
In long-term multivariate time series forecasting, effectively capturing both periodic patterns and residual dynamics is essential. To address this within standard deep learning benchmark settings, we propose the Hierarchical Patching Mixer (HPMixer), which models periodicity and residuals in a decoupled yet complementary manner. The periodic component utilizes a learnable cycle module [7] enhanced with a nonlinear channel-wise MLP for greater expressiveness. The residual component is processed through a Learnable Stationary Wavelet Transform (LSWT) to extract stable, shift-invariant frequency-domain representations. Subsequently, a channel-mixing encoder models explicit inter-channel dependencies, while a two-level non-overlapping hierarchical patching mechanism captures coarse- and fine-scale residual variations. By integrating decoupled periodicity modeling with structured, multi-scale residual learning, HPMixer provides an effective framework. Extensive experiments on standard multivariate benchmarks demonstrate that HPMixer achieves competitive or state-of-the-art performance compared to recent baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
