MFAM-AD: an anomaly detection model for multivariate time series using attention mechanism to fuse multi-scale features.

ABSTRACT: Multivariate time series anomaly detection has garnered significant attention in fields such as IT operations, finance, medicine, and industry. However, a key challenge lies in the fact that anomaly patterns often exhibit multi-scale temporal variations, which existing detection models often fail to capture effectively. This limitation significantly impacts detection accuracy. To address this issue, we propose the MFAM-AD model, which combines the strengths of convolutional neural networks (CNNs) and bi-directional long short-term memory (Bi-LSTM). The MFAM-AD model is designed to enhance anomaly detection accuracy by seamlessly integrating temporal dependencies and multi-scale spatial features. Specifically, it utilizes parallel convolutional layers to extract features across different sc

SUBMITTER: Xia S 

PROVIDER: S-EPMC11419642 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature