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多变量时间序列异常样本的识别*

, PP. 463-468

Keywords: 多变量时间序列(MTS),局部稀疏系数,扩展的Frobenius范数,异常样本

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Abstract:

多变量时间序列(MTS)在金融、多媒体、医学等领域的应用是非常普遍的.与其它多变量时间序列样本显著不同的样本,我们称之为异常样本.本文提出一种基于局部稀疏系数的多变量时间序列异常样本的识别算法,使用扩展的Frobenius范数来计算2个MTS样本之间相似性.使用两阶段顺序查询来进行k近邻查找,将不可能成为候选异常样本的MTS样本剪去.在2个实际数据集上进行实验,实验结果验证算法的有效性.

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