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    政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 >  Item 140.119/150173
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/150173

    Title: 時間序列資料的相關性異常偵測
    Anomaly Detection of Correlation Change over Multivariate Time Series
    Authors: 曹昱維
    Tsao, Yu-Wei
    Contributors: 沈錳坤
    Shan, Man-Kwan
    Tsao, Yu-Wei
    Keywords: 多變輛時間序列
    Multivariate Time Series
    Correlation Prediction
    Correlation Anomaly Detection
    Date: 2024
    Issue Date: 2024-03-01 13:42:40 (UTC+8)
    Abstract: 多變量時間序列的研究包括預測、分類、分群和異常偵測等。其中,時間序列異常偵測是近年熱門的研究主題。其目標在早期發現時間序列中的異常現象。針對多變量時間序列的相關性異常偵測,很少現有研究。本研究的主要目的是偵測多變量時間序列資料的相關性異常。
    本研究採取非監督式學習,根據正常多變量序列所學習的相關性預測模型,來偵測相關性異常。本研究提出 CARG模型 (Clustering-Attention-Residual-GRU Model)來預測多變量時間序列之間的相關性。CARG 模型不僅能夠捕捉相關性本身的時間性結構,同時還透過學習多變量之間的相依性資訊,從而提高預測效果。CARG 模型結合了時間序列分群、注意力機制和時間序列分析模型等技術,具有能夠有效捕捉多變量之間相依性結構的優勢。當 CARG 模型的預測值與實際值存在顯著差異時,可能發生相關性異常事件。
    在我們的實驗中,我們將 CARG 模型應用於金融市場資料上,CARG 模型展現出相當不錯的表現。此外,實驗結果也表明,在 CARG 模型中所設計的各結構對多變量時間序列相關性預測任務均有所助益。並且透過與Baseline模型的比較,CARG 模型確實加強預測的效能。
    The research domain of multivariate time series consists of forecasting, classification, clustering, and anomaly detection, among others. Anomaly detection in time series has become a popular research topic in recent years. Its goal is to discover anomalies in time series data at an early stage. Little research has been paid on the anomaly detection of correlations in multivariate time series. The primary objective of this thesis is to detect correlation anomalies in multivariate time series data.
    This research adopts unsupervised learning approach to detect correlation anomalies based on a correlation prediction model learned from normal multivariate series. We propose the CARG model (Clustering-Attention-Residual-GRU model) to predict the correlations among multivariate time series. The CARG model not only captures the temporal structure of the correlations but also improves prediction performance by learning the dependency information among the multivariates. Combining techniques of time series clustering, attention mechanisms, and time series analysis models, the CARG model is advantageous in effectively capturing the dependency structure among multivariates. To detect correlation anomalies, a significant discrepancy between the predicted values by the CARG model and the actual values may indicate a correlation anomaly event.
    In our experiments, we applied the CARG model to financial market data and it showed quite impressive performance. Additionally, the experimental results also demonstrate that the various structures designed in the CARG model contribute positively to the task of predicting correlations in multivariate time series. By comparison with baseline models, the CARG model indeed enhances the effectiveness of prediction.
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    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0110753201
    Data Type: thesis
    Appears in Collections:[資訊科學系] 學位論文

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