Learning Unified Representations of Normalcy for Time Series Anomaly Detection

2026-05-10

AI Summary

This work addresses the challenge of unsupervised anomaly detection in multivariate time series, where modeling normal patterns remains difficult due to the absence of a unified and robust representation of normality. To this end, the paper proposes the U²AD framework, which uniquely integrates time-varying score networks with a unified training objective. By leveraging score-based generative modeling, U²AD learns the manifold structure of normal data across both local and global temporal contexts and enables deterministic reconstruction through an ordinary differential equation solver. Without requiring labeled data, the method precisely characterizes the distribution of normality and facilitates early anomaly identification. Extensive experiments demonstrate that U²AD significantly outperforms state-of-the-art approaches across multiple benchmarks, achieving notable improvements in both detection accuracy and early warning capability.

Abstract

The core challenge in unsupervised anomaly detection is identifying abnormal patterns without prior knowledge of their characteristics. While existing methods have addressed aspects of this problem, they often struggle to learn a robust representation of the normal data distribution that is distinct from anomalous patterns. In this paper, we present a novel framework, Unified Unsupervised Anomaly Detection ($\text{U}^2\text{AD}$), that comprehensively addresses anomaly detection in multivariate time series. Our approach learns the underlying data distribution of normal samples by utilizing score-based generative modeling. We introduce a novel time-dependent score network and a unified training objective that together delineate the manifold of normal data while considering both local and global temporal contexts. Reconstruction is then performed via a deterministic sampling process using an ordinary differential equation solver. Our extensive experimental evaluations demonstrate that $\text{U}^2\text{AD}$ not only outperforms current state-of-the-art methods in detection accuracy but also identifies anomalies at significantly earlier stages of their occurrence.

Problem

• anomaly detection
• unsupervised learning
• time series
• normalcy representation
• multivariate time series

Innovation

• score-based generative modeling
• time-dependent score network
• unified training objective
• deterministic sampling
• multivariate time series anomaly detection