Xia Shengjie, Sun Wu, Zou Xiaofeng, Chen Panfeng, Ma Dan, Xu Huarong, Chen Mei, Li Hui
State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China.
PeerJ Comput Sci. 2024 Aug 30;10:e2201. doi: 10.7717/peerj-cs.2201. eCollection 2024.
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 scales, employing an attention mechanism for optimal feature fusion. Additionally, Bi-LSTM is leveraged to capture time-dependent information, reconstruct the time series and enable accurate anomaly detection based on reconstruction errors. In contrast to existing algorithms that struggle with inadequate feature fusion or are confined to single-scale feature analysis, MFAM-AD effectively addresses the unique challenges of multivariate time series anomaly detection. Experimental results on five publicly available datasets demonstrate the superiority of the proposed model. Specifically, on the datasets SMAP, MSL, and SMD1-1, our MFAM-AD model has the second-highest F1 score after the current state-of-the-art DCdetector model. On the datasets NIPS-TS-SWAN and NIPS-TS-GECCO, the F1 scores of MAFM-AD are 0.046 (6.2%) and 0.09 (21.3%) higher than those of DCdetector, respectively(the value ranges from 0 to 1). These findings validate the MFAMAD model's efficacy in multivariate time series anomaly detection, highlighting its potential in various real-world applications.
多变量时间序列异常检测在信息技术运营、金融、医学和工业等领域已引起广泛关注。然而，一个关键挑战在于异常模式往往呈现多尺度时间变化，而现有检测模型常常无法有效捕捉。这一局限性显著影响检测精度。为解决此问题，我们提出了MFAM-AD模型，该模型结合了卷积神经网络（CNN）和双向长短期记忆（Bi-LSTM）的优势。MFAM-AD模型旨在通过无缝整合时间依赖性和多尺度空间特征来提高异常检测精度。具体而言，它利用并行卷积层跨不同尺度提取特征，并采用注意力机制进行最优特征融合。此外，利用Bi-LSTM捕捉时间相关信息，重构时间序列并基于重构误差实现准确的异常检测。与现有算法在特征融合不足或局限于单尺度特征分析方面存在困难不同，MFAM-AD有效解决了多变量时间序列异常检测的独特挑战。在五个公开可用数据集上的实验结果证明了所提模型的优越性。具体来说，在数据集SMAP、MSL和SMD1-1上，我们的MFAM-AD模型在当前最先进的DCdetector模型之后具有第二高的F1分数。在数据集NIPS-TS-SWAN和NIPS-TS-GECCO上，MAFM-AD的F1分数分别比DCdetector高0.046（6.2%）和0.09（21.3%）（值范围从0到1）。这些发现验证了MFAMAD模型在多变量时间序列异常检测中的有效性，突出了其在各种实际应用中的潜力。
