VETime: Vision Enhanced Zero-Shot Time Series Anomaly Detection

TL;DR

VETime is a novel framework that unifies visual and temporal data for zero-shot time series anomaly detection, achieving high localization accuracy by aligning and fusing multi-modal information.

Contribution

It introduces a new visual-temporal alignment and fusion method that enhances zero-shot anomaly detection in time series data.

Findings

Outperforms state-of-the-art models in zero-shot scenarios

Achieves higher localization precision

Reduces computational overhead

Abstract

Time-series anomaly detection (TSAD) requires identifying both immediate Point Anomalies and long-range Context Anomalies. However, existing foundation models face a fundamental trade-off: 1D temporal models provide fine-grained pointwise localization but lack a global contextual perspective, while 2D vision-based models capture global patterns but suffer from information bottlenecks due to a lack of temporal alignment and coarse-grained pointwise detection. To resolve this dilemma, we propose VETime, the first TSAD framework that unifies temporal and visual modalities through fine-grained visual-temporal alignment and dynamic fusion. VETime introduces a Reversible Image Conversion and a Patch-Level Temporal Alignment module to establish a shared visual-temporal timeline, preserving discriminative details while maintaining temporal sensitivity. Furthermore, we design an Anomaly Window Contrastive Learning mechanism and a Task-Adaptive Multi-Modal Fusion to adaptively integrate the complementary perceptual strengths of both modalities. Extensive experiments demonstrate that VETime significantly outperforms state-of-the-art models in zero-shot scenarios, achieving superior localization precision with lower computational overhead than current vision-based approaches. Code available at: https://github.com/yyyangcoder/VETime.