While Transformer-based architectures have established themselves as a dominant paradigm in Multivariate Time Series Forecasting (MTSF), their core self-attention mechanism inherently functions as a low-pass filter, systematically smoothing out high-frequency signals vital for sharp local changes. Recent advancements have increasingly incorporated frequency-domain operations to address this bias, however, most existing designs rely on fixed spectral bases and apply sequence-wise (uniform) modulation, implicitly assuming a time-invariant frequency response. This overlooks a key property of real-world series that their spectral characteristics often evolve over time, making uniform modulation insufficient for capturing fine-grained temporal dynamics. To tackle these limitations, we propose FAiT, a Frequency-Aware inverted Transformer. Specifically, FAiT rectifies the spectral bias internally through Inverted Attention, which interprets the attention map as a learnable low-pass operator and constructs a dedicated complementary high-pass branch by inverting the attention matrix to recover attenuated transient signals. Furthermore, FAiT introduces Dynamic Temporal-Frequency Modulation (DTFM), which synthesizes instance-conditioned weights to adaptively re-calibrate the energy of spectral sub-bands, enabling fine-grained control over evolving multi-scale patterns. Extensive experiments on widely used benchmarks demonstrate that FAiT consistently outperforms state-of-the-art Transformer-based and frequency-enhanced baselines, while maintaining computational efficiency.

翻译：尽管基于Transformer的架构已成为多变量时间序列预测（MTSF）中的主导范式，但其核心自注意力机制本质上充当低通滤波器，系统性地平滑掉对于捕捉局部剧烈变化至关重要的高频信号。近期研究逐渐引入频域操作以缓解这一偏差，然而现有设计大多依赖固定频谱基函数并采用序列级（统一）调制，隐含假设频率响应具有时不变性。这忽视了真实世界序列的关键特性——其频谱特征常随时间演化，导致统一调制不足以捕获细粒度的时间动态。为应对这些局限，我们提出FAiT（频率感知倒置Transformer）。具体而言，FAiT通过倒置注意力机制内部修正频谱偏差：将注意力图解释为可学习低通算子，并通过倒置注意力矩阵构建专用互补高通分支，以恢复被衰减的瞬态信号。此外，FAiT引入动态时频调制（DTFM），该模块合成实例条件权重，自适应地重新校准频谱子带的能量，实现对演化多尺度模式的细粒度调控。在广泛使用的基准数据集上的大量实验表明，FAiT在保持计算效率的同时，持续优于最先进的基于Transformer和频率增强的基线模型。