Self-Supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detection

TL;DR

This paper introduces STEN, a novel self-supervised method that models both spatial and temporal aspects of time series data to improve anomaly detection accuracy across various domains.

Contribution

The paper proposes a combined spatial-temporal learning framework, STEN, which captures both temporal correlations and spatial relations in time series data for anomaly detection.

Findings

STEN significantly outperforms existing methods on five benchmark datasets.

The approach effectively captures complex spatial-temporal patterns in time series.

Experimental results demonstrate improved detection accuracy and robustness.

Abstract

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.