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

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

PeerJ Comput Sci

State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China.

Published: August 2024

AI Article Synopsis

  • Multivariate time series anomaly detection is crucial in fields like IT, finance, and medicine, but existing models struggle with capturing complex, multi-scale patterns, leading to lower accuracy in detecting anomalies.
  • To tackle this challenge, the MFAM-AD model combines convolutional neural networks (CNNs) and bi-directional long short-term memory (Bi-LSTM) to improve detection by integrating temporal dependencies and multi-scale features effectively.
  • Experimental results show that MFAM-AD outperforms or closely matches state-of-the-art models, achieving higher F1 scores on several datasets, which confirms its effectiveness for real-world anomaly detection applications.

Article 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 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11419642PMC
http://dx.doi.org/10.7717/peerj-cs.2201DOI Listing

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