Harnessing Vision-Language Models for Time Series Anomaly Detection (2506.06836v1)

Abstract: Time-series anomaly detection (TSAD) has played a vital role in a variety of fields, including healthcare, finance, and industrial monitoring. Prior methods, which mainly focus on training domain-specific models on numerical data, lack the visual-temporal reasoning capacity that human experts have to identify contextual anomalies. To fill this gap, we explore a solution based on vision LLMs (VLMs). Recent studies have shown the ability of VLMs for visual reasoning tasks, yet their direct application to time series has fallen short on both accuracy and efficiency. To harness the power of VLMs for TSAD, we propose a two-stage solution, with (1) ViT4TS, a vision-screening stage built on a relatively lightweight pretrained vision encoder, which leverages 2-D time-series representations to accurately localize candidate anomalies; (2) VLM4TS, a VLM-based stage that integrates global temporal context and VLM reasoning capacity to refine the detection upon the candidates provided by ViT4TS. We show that without any time-series training, VLM4TS outperforms time-series pretrained and from-scratch baselines in most cases, yielding a 24.6 percent improvement in F1-max score over the best baseline. Moreover, VLM4TS also consistently outperforms existing language-model-based TSAD methods and is on average 36 times more efficient in token usage.

• The paper introduces a two-stage framework that converts 1-D time series into 2-D visuals and refines detection using VLMs, achieving a 24.6% improvement in F1-max score.
• It leverages visual reasoning with multi-scale embedding extraction and cross-patch comparisons to accurately localize and verify candidate anomalies.
• The approach outperforms traditional methods and is applicable to diverse fields like healthcare, finance, and industrial monitoring without specific domain training.

Harnessing Vision-LLMs for Time Series Anomaly Detection

The paper "Harnessing Vision-LLMs for Time Series Anomaly Detection" proposes an innovative approach to enhance time-series anomaly detection (TSAD) using Vision LLMs (VLMs). This research addresses a critical gap in current TSAD methods, which traditionally rely on domain-specific models trained solely on numerical data. These conventional approaches often lack the deep visual-temporal reasoning capabilities possessed by human experts.

Methodology

The authors introduce a two-stage framework leveraging VLMs, specifically tailored to improve anomaly detection in time series data by visual reasoning. The first stage is ViT4TS, which utilizes a relatively lightweight pre-trained vision encoder to convert 1-D time-series data into 2-D visual representations. This stage aims to localize candidate anomalies accurately within these visual representations. The second stage, VLM4TS, integrates the candidate anomalies identified by ViT4TS with global temporal context using a VLM's reasoning capacity to refine and verify detection.

Without any in-domain time-series training, the proposed VLM4TS demonstrably outperforms both time-series pre-trained and from-scratch baselines, yielding a 24.6% improvement in F1-max score over the best baseline. Additionally, VLM4TS surpasses existing LLM-based TSAD methods, achieving an average efficiency increase of 36 times in token usage.

Results and Analysis

The framework delivers standout performance across various domain benchmarks, demonstrating strong numerical results. It excels particularly on real-world datasets such as those in the NAB dataset group, significantly outperforming traditional and LLM-based approaches, including TAMA. This improvement highlights the efficacy of combining vision-based localization with VLM-based verification, enabling more contextual anomaly detection.

The paper includes an extensive ablation paper to validate the effectiveness of crucial components within the framework. Multi-scale embedding extraction and cross-patch comparison are identified as key contributors to the system’s performance, enhancing the ability to capture both localized and contextual anomalies.

Implications and Future Directions

This research presents significant implications for practical applications in numerous fields, including healthcare, finance, and industrial monitoring. By effectively incorporating visual reasoning into anomaly detection, the methodology opens avenues for more robust and generalized models that can handle diverse temporal patterns without needing extensive domain-specific tuning.

The theoretical implications of this work suggest promising developments in AI methodologies, particularly in leveraging multi-modal foundation models for tasks traditionally dominated by numerical or textual analysis. Future research could delve into the expansion of this approach to multivariate time series data and explore advanced prompting strategies to further enhance temporal reasoning capacity.

In conclusion, this paper provides a comprehensive exploration of how VLMs can be harnessed to revolutionize the landscape of time-series anomaly detection, presenting a methodology with significant improvements in both accuracy and efficiency.