发表机构
Harbin Institute of Technology（哈尔滨工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对时间序列异常检测中深度学习模型推理开销大的问题，提出与模型无关的级联框架CARE，结合LPM与CDM，经8个基准测试，实现2.7-4.8倍推理加速且保持检测性能。
AI 中文摘要
尽管深度学习模型在时间序列异常检测中已达到最先进（SOTA）性能，但其复杂架构会产生大量推理开销。现有方法通常对所有数据点采用统一推理策略，而异常本质上稀少，绝大多数时序数据由可预测的正常模式构成，因此该策略效率低下。为缓解此瓶颈，我们提出CARE，这是一种与模型无关的级联推理框架，将轻量级预筛选模型（LPM）与现有高容量复杂检测模型（CDM）相结合。LPM利用残差MLP自编码器和正态性条件门控机制快速筛选高置信度正常样本。关键在于，我们引入结构注意力模块以显式捕捉通道级异常贡献，并通过置信度引导的选择性路由目标优化门控网络，该目标学习可靠的路由决策以减少不必要的CDM调用。在8个真实世界基准上的大量实验表明，CARE能有效隔离高置信度正常样本，仅将不确定样本路由至CDM，与最准确的SOTA方法相比，该框架实现了2.7倍至4.8倍的推理加速，同时仍保持有竞争力的检测质量。
英文摘要
While deep learning models have achieved state-of-the-art performance in time series anomaly detection, their complex architectures incur substantial inference overhead. Existing methods typically apply a uniform inference strategy across all data points, which is inefficient given that anomalies are inherently scarce and the vast majority of temporal data consists of predictable normal patterns. To mitigate this bottleneck, we propose CARE, a model-agnostic cascaded inference framework that integrates a Lightweight Pre-filter Model (LPM) with an existing high-capacity Complex Detection Model (CDM). The LPM rapidly filters high-confidence normal samples using a Residual MLP AutoEncoder and a Normality-Conditioned Gating mechanism. Crucially, we introduce a Structure Attention module to explicitly capture channel-wise anomaly contributions, and optimize the gating network via a confidence-guided selective routing objective that learns reliable routing decisions to reduce unnecessary CDM invocations. Extensive experiments across eight real-world benchmarks demonstrate that CARE effectively isolates high-confidence normal samples. By routing only uncertain samples to the CDM, our framework achieves $2.7\times$ to $4.8\times$ inference speedup compared to the most accurate SOTA approaches, while still maintaining competitive detection quality.
