CSTSINR: improving temporal continuity via convolutional structured implicit neural representations for time series anomaly detection.

Abstract

Time series anomaly detection plays a crucial role in identifying significant deviations from expected behavior. Implicit Neural Representation (INR) has been explored for time series modeling due to its ability to learn continuous functions. The inherent spectral bias of INRs, which prioritizes low-frequency signal fitting, further enables the detection of high-frequency anomalies. However, current INR-based approaches demonstrate limited capability in representing complex temporal patterns, particularly when the normal data itself contains significant high-frequency components. To address these challenges, we propose CSTSINR, a novel anomaly detection model that integrates the structured feature map and convolutional mechanisms with the INR continuous function. By leveraging a structured feature map and convolutional layers, CSTSINR addresses the limitations of directive prediction of all parameters and point-wise query processing, providing improved modeling of temporal continuity and enhanced anomaly detection. Our extensive experiments demonstrate that CSTSINR outperforms existing state-of-the-art methods across ten benchmark datasets, highlighting its superior ability to detect anomalies, particularly in high-frequency or complex time series data.