中文摘要：

多变量时间序列（MTS）在金融、医学、科学、工程等领域是非常普遍的．本文提出一种在MTS中识别异常模式的方法．采用白底向上的分割算法将MTS分割成互不重叠的子序列，使用扩展的Frobenius范数来计算2个MTS子序列之间的相似性，通过K-均值聚类将MTS子序列分为若干个类．根据异常模式的定义，从这若干个类中识别出异常模式．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．

英文摘要：

Multivariate time series （MTS） is widely available in many fields including finance, medicine, science and engineering . An approach for identifying outlier patterns in MTS is proposed . By using bottomrup segmentation algorithm, MTS is divided into non-overlapping subsequences. An extended Frobenius norm is used to compare the similarity between two MTS subsequences. K-means algorithm is employed to cluster MTS subsequences into some classes. According to the definitions of outlier patterns , the outlier patterns in MTS can be identified from the classes . Experiments are performed on two real-world datasets： stock market dataset and brain computer interface dataset. The experimental results show the effectiveness of the algorithm.