政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/153390

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/153390

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政大典藏 > College of Informatics > Department of Computer Science > Theses > Item 140.119/153390

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/153390

Title:	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
Authors:	陳筱詩 Chen, Hsiao-Shih
Contributors:	沈錳坤 Shan, Man-Kwan 陳筱詩 Chen, Hsiao-Shih
Keywords:	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
Date:	2024
Issue Date:	2024-09-04 15:01:58 (UTC+8)
Abstract:	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.
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Description:	碩士國立政治大學資訊科學系 111753201
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0111753201
Data Type:	thesis
Appears in Collections:	[Department of Computer Science ] Theses

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