动态时间归整
计算机科学
系列(地层学)
相似性(几何)
模式识别(心理学)
分类器(UML)
时间序列
集合(抽象数据类型)
算法
人工智能
字错误率
数据挖掘
机器学习
图像(数学)
程序设计语言
古生物学
生物
作者
Che Jui Hsu,Kuo Si Huang,Chang Biau Yang,Yuanyuan Guo
标识
DOI:10.1016/j.procs.2015.05.444
摘要
Measuring the similarity or distance between two time series sequences is critical for the classification of a set of time series sequences. Given two time series sequences, X and Y , the dynamic time warping (DTW) algorithm can calculate the distance between X and Y . But the DTW algorithm may align some neighboring points in X to the corresponding points which are far apart in Y . It may get the alignment with higher score, but with less representative information. This paper proposes the flexible dynamic time wrapping (FDTW) method for measuring the similarity of two time series sequences. The FDTW algorithm adds an additional score as the reward for the contiguously long one-to-one fragment. As the experimental results show, the DTW and DDTW and FDTW methods outperforms each other in some testing sets. By combining the FDTW, DTW and DDTW methods to form a classifier ensemble with the voting scheme, it has less average error rate than that of each individual method.
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