通过因果注意力与通道重校准提升多变量时间序列分类精度
CASE-NET: Deep Spatio-Temporal Representation Learning via Causal Attention and Channel Recalibration for Multivariate Time Series Classification
- 用因果注意力和掩码自注意力约束时间方向，避免非平稳动态中的混淆偏差
- 在六个不同领域测试中，四个任务达新基准，最高准确率达98.6%（AWR数据集）
- 适合处理噪声干扰强、动态变化大的多变量时间序列场景
多变量时间序列（MTS）分类是普适计算与金融分析的基础，但现有多尺度方法常受限于表征保真度不足。我们识别出两大瓶颈：标准编码器中的时间非因果性导致非平稳动态中的时间混淆，以及缺乏显式的通道显著性机制，使噪声污染潜在空间。为此，我们提出因果注意力与时空编码网络（CASE-NET），用于结构流形预调理。CASE-NET融合因果时间编码器，通过掩码自注意力和因果卷积施加物理时间箭头约束，以及自适应通道重校准模块，作为信息瓶颈抑制有害噪声。在六个异构领域上的全面评估表明，CASE-NET在四个任务上建立新SOTA基准，在AWR数据集上达到98.6%的峰值准确率，并在非平稳环境下表现出更强鲁棒性。
原文摘要 · Abstract (English)
Multivariate time series (MTS) classification is foundational to pervasive computing and financial analysis, yet existing multi-scale paradigms are often constrained by suboptimal representation fidelity. We identify two critical bottlenecks: temporal non-causality in standard encoders that induces temporal confounding in non-stationary dynamics, and the absence of explicit channel saliency mechanisms that allows noise to contaminate the latent space. To address these challenges, we propose the Causal Attention and Spatio-temporal Encoder Network (CASE-NET), an architecture designed for structural manifold pre-conditioning. CASE-NET synergizes a Causal Temporal Encoder, which enforces physical arrow-of-time constraints via masked self-attention and causal convolutions, with an Adaptive Channel Recalibration module functioning as an information bottleneck to suppress detrimental noise. Comprehensive evaluations across six heterogeneous domains demonstrate that CASE-NET establishes new state-of-the-art benchmarks on four tasks, achieving a peak accuracy of 98.6% on the AWR dataset and superior robustness in non-stationary regimes.
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