Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancy

The introduction of TimeRCD marks a significant advancement in time series anomaly detection (TSAD), a critical area in artificial intelligence. Traditional models often struggle with subtle anomalies due to their reliance on reconstruction-based objectives, which can misinterpret complex normal patterns, leading to high false positive and negative rates. TimeRCD overcomes these challenges by employing a new pre-training paradigm known as Relative Context Discrepancy (RCD). This model is designed to explicitly detect anomalies by identifying significant discrepancies between adjacent time windows, rather than reconstructing inputs. Implemented with a standard Transformer architecture, TimeRCD captures contextual shifts that previous methods often miss. Extensive experiments have demonstrated that TimeRCD significantly outperforms existing general-purpose and anomaly-specific models in zero-shot TSAD across diverse datasets, showcasing its potential to improve the reliability of anomaly…