基于小波与t检验的时序异常检测方法，速度快且准确率高。
Fast and Accurate Anomaly Detection in Time Series
- 利用哈尔小波分解时序数据，结合定制t检验识别异常
- 在343个数据集上超越现有主流无监督与自监督方法
- 适合对误报率敏感的安全关键场景使用
异常检测是机器学习中关键且不断演进的领域，广泛应用于网络安全、金融、医疗、制造和物联网系统。传统方法采用有监督或无监督学习范式，但真实场景面临标签极度不均衡（异常样本稀少）及标注成本高昂的问题。有监督方法受限于标注数据稀缺，而无监督方法虽无需标注却常在安全关键应用中产生高误报率。本文提出一种新型无监督时序异常检测算法，基于哈尔离散小波变换与定制t检验。建立了该t检验的理论基础，并通过覆盖343个数据集的大量实验，证明该方法在性能上超越当前最先进的无监督与自监督基准。
原文摘要 · Abstract (English)
Anomaly detection is a critical and evolving field in Machine Learning, with applications targeting different domains such as cybersecurity, finance, healthcare, manufacturing and IoT (Internet of Things) systems. Traditionally, anomaly detection algorithms have been designed using both supervised and unsupervised learning paradigms. The fundamental challenge in real-world anomaly detection scenarios is related to the inherent class imbalance (anomalies are typically rare) and, for supervised methods, to the scarcity of labelled anomalous data. Indeed, labelling is both expensive and time-consuming. Conversely unsupervised methods do not require labelling, but may suffer from high false positive rates when deployed in safety-critical applications. In this work we introduce a novel unsupervised algorithm for anomaly detection in time series based on the Haar discrete wavelet and a suitably designed $t$-test. We establish the theoretical foundation of the proposed $t$-test and, through extensive experimentation across 343 datasets, demonstrate that our algorithm outperforms state-of-the-art unsupervised and self-supervised benchmarks.
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