计算机科学 ›› 2023, Vol. 50 ›› Issue (6A): 220700094-7.doi: 10.11896/jsjkx.220700094
张国华, 燕雪峰, 关东海
ZHANG Guohua, YAN Xuefeng, GUAN Donghai
摘要: 多元时间序列的有效异常检测对于数据的分析挖掘具有重要意义。然而,已有的检测方法大多基于单模态,不能有效利用时间序列在多模态空间中的分布信息,对于多模态特征缺乏自适应融合方式且难以提取其时空依赖关系。为此,提出了一种多模态特征融合的时间序列异常检测方法,建立了一个多模态特征自适应融合模块,通过一维卷积网络和软选择方式对多元时间序列的多模态特征进行自适应融合。对于融合后的多模态特征,构建由时间注意力和空间注意力组成的时空注意力模块,同时提取其时间和空间依赖关系得到时空注意力向量,由时空注意力向量得到模型预测结果。通过学习正常样本分布,根据预测值与真实值的误差度量实现异常检测。在4个公开数据集上进行测试,结果表明,所提方法优于其他模型,证明了所提方法的有效性。
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