Numerous methods for time series anomaly detection (TSAD) methods have
emerged in recent years. Most existing methods are unsupervised and assume the
availability of normal training samples only, while few supervised methods have
shown superior performance by incorporating labeled anomalous samples in the
training phase. However, certain anomaly types are inherently challenging for
unsupervised methods to differentiate from normal data, while supervised
methods are constrained to detecting anomalies resembling those present during
training, failing to generalize to unseen anomaly classes. This paper is the
first attempt in providing a novel approach for the open-set TSAD problem, in
which a small number of labeled anomalies from a limited class of anomalies are
visible in the training phase, with the objective of detecting both seen and
unseen anomaly classes in the test phase. The proposed method, called
Multivariate Open-Set timeseries Anomaly Detection (MOSAD) consists of three
primary modules: a Feature Extractor to extract meaningful time-series
features; a Multi-head Network consisting of Generative-, Deviation-, and
Contrastive heads for capturing both seen and unseen anomaly classes; and an
Anomaly Scoring module leveraging the insights of the three heads to detect
anomalies. Extensive experiments on three real-world datasets consistently show
that our approach surpasses existing methods under various experimental
settings, thus establishing a new state-of-the-art performance in the TSAD
field.

本研究提出了一种新方法，名为多变量开放式时间序列异常检测（MOSAD），旨在解决开放式时间序列异常检测问题，该问题在训练阶段可见一小部分有标签的异常样本，测试阶段需要检测到既有的异常类别又有未知的异常类别，并通过在特征提取、多头网络和异常评分模块等方面的创新实现在时间序列异常检测领域的全新的最佳性能。