用分块+事件驱动机制，实现快速异常检测。
Patched-DeltaNet: Token-Level Event-Driven Memory for Linear-Time Anomaly Detection
- 分块提取局部语义，仅在显著变化时更新状态。
- 在SMD数据集上达0.957的ROC-AUC和0.822的PA-F1。
- 计算复杂度降至O(L/P)，适合资源受限场景。
时间序列异常检测对保障关键系统可靠性至关重要。尽管基于Transformer的模型如PatchTST表现优异，但其$/mathcal{O}(L^2)$的计算复杂度严重限制了在资源受限环境中的部署。本文提出Patched-DeltaNet，结合时间序列分块与门控差分网络。通过融合两种范式，我们假设并证明了令牌级事件驱动记忆的出现：分块机制提取局部语义块，而误差驱动的DeltaNet仅在显著物理变化（即差分）发生时更新其递归状态。这种协同作用有效过滤背景噪声，捕捉突发异常漂移。在Server Machine Dataset（SMD）基准上的严格实验表明，Patched-DeltaNet具有结构优势和样本效率。在统一评估约束和相同计算预算下，该模型显著优于近期架构，达到0.957的ROC-AUC和0.822的PA-F1，同时将计算复杂度降低至理论最小值$/mathcal{O}(L/P)$。
原文摘要 · Abstract (English)
Time series anomaly detection is critical for maintaining the reliability of mission-critical systems. While Transformer-based models like PatchTST have shown remarkable performance, their $\mathcal{O}(L^2)$ computational complexity severely limits deployment in resource-constrained environments. In this paper, we propose Patched-DeltaNet, a novel architecture combining time-series patching with Gated Delta Networks. By integrating these paradigms, we hypothesize and demonstrate the emergence of token-level event-driven memory, whereby the patching mechanism extracts local semantic chunks, while the error-driven DeltaNet updates its recurrent state exclusively when significant physical changes, defined as deltas, occur. This synergy effectively filters out background noise and captures sudden anomalous drifts. Our rigorous experiments on the Server Machine Dataset (SMD) benchmark demonstrate the structural superiority and sample efficiency of Patched-DeltaNet. By strictly outperforming recent architectures under unified evaluation constraints and identical compute budgets, our model yields an ROC-AUC of 0.957 and PA-F1 of 0.822, while drastically reducing computational complexity to the theoretical minimum of $\mathcal{O}(L/P)$.
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