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    题名: 基於圖神經網路的多變量時間序列相關性異常偵測
    Correlation Anomaly Detection in Multivariate Time Series Based on Graph Neural Networks
    作者: 陳筱詩
    Chen, Hsiao-Shih
    贡献者: 沈錳坤
    Shan, Man-Kwan
    陳筱詩
    Chen, Hsiao-Shih
    关键词: 圖神經網路
    多變量時間序列
    相關性異常偵測
    Graph Neural Network
    Multivariate Time Series
    Correlation Anomaly Detection
    日期: 2024
    上传时间: 2024-09-04 15:01:58 (UTC+8)
    摘要: 隨著感測技術的進步，人們能夠在各應用領域取得時間序列資料。時間序列 資料是隨著時間變化的資訊。這些資料隨著時間推移而不斷變化，並在許多應用 中存在複雜而密切的關係，彼此之間相互影響。在這些變動中，隱藏許多有價值 的訊息，透過捕捉多條時間序列資料中相關性的模式與變化，可預測未來趨勢以 及應對突發事件，得以進行有效的決策支援與風險管理。
    本研究旨在進行多變量時間序列相關性的異常偵測。由於多個變量之間存在 複雜的相關性並且隨時間而變化，本論文提出 MCAD-wsGAT (Multivariate Correlation Anomaly Detection - wsGAT) 方法，結合圖神經網路 (Graph Neural Network) 與遞迴神經網路 (Recurrent Neural Networks) 學習與捕捉多變量時間 序列隨時間變化之間的關聯性。根據所學習出的模型預測未來的相關係數，進而 偵測超乎預期的相關性異常 (Correlation Anomaly)。
    本研究將實驗應用於伺服器運行中，偵測不同類型異常所造成的相關性異常， 並且觀察與分析時間序列走勢。由實驗結果顯示 MCAD-wsGAT 不僅在相關係數 的預測上有極高的準確率，在相關性異常偵測上也有極佳的效果。
    With advancements in sensing technology, time series data—capturing time- related information across various fields—have become increasingly prevalent. These data evolve over time, revealing complex, intertwined relationships that can offer valuable insights. By analyzing patterns and changes in correlations among multiple time series, we can predict future trends and respond to unexpected events, supporting effective decision-making and risk management.
    We introduce the MCAD-wsGAT (Multivariate Correlation Anomaly Detection - wsGAT) method for anomaly detection in multivariate time series correlations. MCAD- wsGAT combines Graph Neural Networks (GNN) and Recurrent Neural Networks (RNN) to capture temporal correlations and predict future correlation values, enabling the detection of anomalies.
    We applied this method to server operations to identify correlation anomalies caused by various disruptions. The results show that MCAD-wsGAT effectively models correlation dynamics, reducing MAE loss in predictive models and demonstrating excellent performance in correlation anomaly detection.
    參考文獻: [1] P. Esling and C. Agon, Time-Series Data Mining, ACM Computing Surveys (CSUR), vol. 45, no. 1, pp. 1-34, 2012.
    [2] K. Choi, J. Yi, C. Park, and S. Yoon, Deep Learning for Anomaly Detection in Time-Series Data: Review, analysis, and Guidelines, IEEE Access, vol. 9, pp. 120043-120065, 2021.
    [3] K. Golmohammadi and O. R. Zaiane, Time Series Contextual Anomaly Detection for Detecting Market Manipulation in Stock Market, in IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 1-10, 2015.
    [4] Y. Lei, F. Jia, J. Lin, S. Xing, and S. X. Ding, An Intelligent Fault Diagnosis Method using Unsupervised Feature Learning towards Mechanical Big Data, IEEE Transactions on Industrial Electronics, vol. 63, no. 5, pp. 3137-3147, 2016.
    [5] J. Pereira and M. Silveira, Learning Representations from Healthcare Time Series Data for Unsupervised Anomaly Detection, in IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 1-7, 2019.
    [6] A. Blázquez-García, A. Conde, U. Mori, and J. A. Lozano, A Review on Outlier/Anomaly Detection in Time Series Data, ACM Computing Surveys (CSUR), vol. 54, no. 3, pp. 1-33, 2021.
    [7] S. Papadimitriou, J. Sun, and C. Faloutsos, Streaming Pattern Discovery in Multiple Time-Series, in Proceedings of the 31st International Conference on Very Large Data Bases (VLDB), pp. 697-708, 2005.
    [8] E. J. Candès, X. Li, Y. Ma, and J. Wright, Robust Principal Component Analysis?, Journal of the ACM (JACM), vol. 58, no. 3, pp. 1-37, 2011.
    [9] R. J. Hyndman, E. Wang, and N. Laptev, Large-Scale Unusual Time Series Detection, in IEEE International Conference on Data Mining Workshop (ICDMW), pp. 1616-1619, 2015.
    [10] M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander, LOF: Identifying Density- Based Local Outliers, in Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 93-104, 2000.
    [11] V. Ishimtsev, A. Bernstein, E. Burnaev, and I. Nazarov, Conformal k-NN Anomaly Detector for Univariate Data Streams, in Conformal and Probabilistic Prediction and Applications, pp. 213-227, 2017.
    [12] Z. Fu, W. Hu, and T. Tan, Similarity Based Vehicle Trajectory Clustering And Anomaly Detection, in IEEE International Conference on Image Processing, vol. 2, pp. II-602, 2005.
    [13] A. H. Yaacob, I. K. Tan, S. F. Chien, and H. K. Tan, Arima Based Network Anomaly Detection, in Second International Conference on Communication Software and Networks, pp. 205-209, 2010.
    [14] Y. Mirsky, T. Doitshman, Y. Elovici, and A. Shabtai, Kitsune: an Ensemble of Autoencoders for Online Network Intrusion Detection, arXiv preprint arXiv:1802.09089, 2018.
    [15] P. Malhotra, A. Ramakrishnan, G. Anand, L. Vig, P. Agarwal, and G. Shroff, LSTM-based Encoder-Decoder for Multi-Sensor Anomaly Detection, arXiv preprint arXiv:1607.00148, 2016.
    [16] M. Munir, S. A. Siddiqui, A. Dengel, and S. Ahmed, DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series, IEEE Access, vol. 7, pp. 1991-2005, 2018.
    [17] A. Deng and B. Hooi, Graph Neural Network-based Anomaly Detection in Multivariate Time Series, in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 5, pp. 4027-4035, 2021.
    [18] H. Zhao et al., Multivariate Time-Series Anomaly Detection via Graph Attention Network, in IEEE International Conference on Data Mining (ICDM), pp. 841-850, 2020.
    [19] Y. W. Tsao, Anomaly Detection of Correlation Change over Multivariate Time Series, Master’s Thesis, Department of Computer Science, National Chengchi University, 2024.
    [20] T. N. Kipf and M. Welling, Semi-Supervised Classification with Graph Convolutional Networks, arXiv preprint arXiv:1609.02907, 2016.
    [21] P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, Graph Attention Networks, arXiv preprint arXiv:1710.10903, 2017.
    [22] K. Xu, W. Hu, J. Leskovec, and S. Jegelka, How Powerful Are Graph Neural Networks?, arXiv preprint arXiv:1810.00826, 2018.
    [23] A. Vaswani et al., Attention Is All You Need, Advances in Neural Information Processing Systems, vol. 30, 2017.
    [24] M. Grassia and G. Mangioni, wsGAT: Weighted and Signed Graph Attention Networks for Link Prediction, in Complex Networks & Their Applications X: Volume 1, Proceedings of the Tenth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2021 10, pp. 369-375, 2022.
    [25] K. He, X. Zhang, S. Ren, and J. Sun, Deep Residual Learning for Image Recognition, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016.
    [26] J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, Empirical Evaluation of Gated Recurrent Neural Networks On Sequence Modeling, arXiv preprint arXiv:1412.3555, 2014.
    [27] Z. Li et al., Multivariate Time Series Anomaly Detection and Interpretation using Hierarchical Inter-Metric and Temporal Embedding, in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 3220- 3230, 2021.
    描述: 碩士
    國立政治大學
    資訊科學系
    111753201
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0111753201
    数据类型: thesis
    显示于类别:[資訊科學系] 學位論文

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