With the increasing complexity of industrial Internet of Things systems and other intelligent technologies, anomaly detection in multivariate time series has become pivotal for applications in equipment health monitoring and industrial process control. Existing methodologies often struggle with addressing the challenges of multivariate dependencies, temporal dynamics, and computational efficiency. Therefore, this paper introduces the Multi-scale Adaptive Dependency Temporal Convolutional Network (MAD-TCN), a lightweight and efficient model designed to overcome these limitations. MAD-TCN leverages a dual-branch architecture, utilizing both local (short-term) and global (long-term) temporal feature extraction through depthwise separable dilated convolutions, which are fused to achieve multiscale integration. The model incorporates a cross-variable convolutional feedforward network and an adaptive gated unit to dynamically adjust dependency relationships between variables, enhancing the model’s ability to handle complex interdependencies across multiple dimensions. Comprehensive experiments on four public benchmark datasets (SMAP, SWaT, SMD, MBA) alongside 13 state-of-the-art methods (including LSTM-NDT, DAGMM, TimesNet, TranAD and DTAAD) demonstrate that MAD-TCN outperforms the competition in terms of anomaly detection accuracy, achieving the highest or second-highest AUC and F1-scores, while maintaining a parameter count of only approximately 0.026 M. In addition, compared to the best alternative, MAD-TCN achieves a 34% improvement in training and inference speed. In summary, these experimental results fully demonstrate the superior performance of MAD-TCN in the time series anomaly detection task with both high accuracy and computational efficiency.Source code: https://github.com/qianmo2001/MAD-TCN
扫码关注我们
求助内容:
应助结果提醒方式:
