  Time Series Anomaly Detection (TSAD) finds widespread applications across
various domains such as financial markets, industrial production, and
healthcare. Its primary objective is to learn the normal patterns of time
series data, thereby identifying deviations in test samples. Most existing TSAD
methods focus on modeling data from the temporal dimension, while ignoring the
semantic information in the spatial dimension. To address this issue, we
introduce a novel approach, called Spatial-Temporal Normality learning (STEN).
STEN is composed of a sequence Order prediction-based Temporal Normality
learning (OTN) module that captures the temporal correlations within sequences,
and a Distance prediction-based Spatial Normality learning (DSN) module that
learns the relative spatial relations between sequences in a feature space. By
synthesizing these two modules, STEN learns expressive spatial-temporal
representations for the normal patterns hidden in the time series data.
Extensive experiments on five popular TSAD benchmarks show that STEN
substantially outperforms state-of-the-art competing methods. Our code is
available at https://github.com/mala-lab/STEN.
