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The paper presents DHG-AD, an unsupervised method for detecting anomalies in multivariate time series using directed hypergraph neural networks, addressing the limitations of traditional models that overlook group interactions. The model constructs directed hypergraphs to capture complex relationships among variables, employing a convolutional architecture for representation learning and anomaly scoring. Experimental results demonstrate that DHG-AD outperforms existing methods, achieving higher AUC-F1 and A

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yuvakishore5022

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EN