TVF-AD: Temporal-Visual Fusion for Time-Series Anomaly Detection

Detecting abnormal temporal dependencies is fundamental to time series anomaly detection (TSAD), yet accurately modeling temporal dependencies, especially in multivariate time series, remains an open challenge. Recent approaches leverage pretrained visual models to capture temporal structures, showing promising performance by transferring structured visual semantics to sequential data and enhancing temporal structure modeling. However, such methods often rely heavily on visual priors and may fail to capture fine-grained temporal dynamics and domain-specific dependencies inherent in time series. To address this limitation, we propose TVF-AD, a novel hybrid temporal–visual representation-based framework for TSAD that integrates general visual priors with specialized temporal representations. Our framework effectively bridges cross-modal structural knowledge and sequential dynamics, enabling more accurate and robust anomaly detection, while maintaining competitive efficiency by optimizing only lightweight temporal encoding and fusion modules. Extensive experiments on benchmark datasets demonstrate that our method consistently outperforms state-of-the-art approaches.