简单线性模型比深度模型更擅长时间序列异常检测
Strong Linear Baselines Strike Back: Closed-Form Linear Models as Gaussian Process Conditional Density Estimators for TSAD
- 用普通最小二乘法求解线性自回归模型，闭式解快速计算
- 在多变量/单变量数据集上精度超主流深度模型，资源消耗少百倍
- 适合追求高效、可解释性的工业场景应用
时间序列异常检测研究长期聚焦于复杂难训的深度神经网络架构。本文重新审视这一范式，表明一种基于普通最小二乘法（OLS）的简单线性自回归异常评分方法，在多个基准测试中始终达到或超越当前最优深度检测器性能。理论上，线性模型能捕捉广泛的异常类型，实现有限历史的高斯过程条件密度估计。实践中，在大量单变量与多变量基准上，该方法精度更高，且计算资源需求减少数个数量级。因此，未来研究应持续引入强线性基线，并构建具更丰富时序结构的新基准，以真正揭示深度学习的优势。
原文摘要 · Abstract (English)
Research in time series anomaly detection (TSAD) has largely focused on developing increasingly sophisticated, hard-to-train, and expensive-to-infer neural architectures. We revisit this paradigm and show that a simple linear autoregressive anomaly score with the closed-form solution provided by ordinary least squares (OLS) regression consistently matches or outperforms state-of-the-art deep detectors. From a theoretical perspective, we show that linear models capture a broad class of anomaly types, estimating a finite-history Gaussian process conditional density. From a practical side, across extensive univariate and multivariate benchmarks, the proposed approach achieves superior accuracy while requiring orders of magnitude fewer computational resources. Thus, future research should consistently include strong linear baselines and, more importantly, develop new benchmarks with richer temporal structures pinpointing the advantages of deep learning models.
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