Efficient Dynamic Time Warping by Adaptively Controlling the Valid Warping Range

被引:0
|
作者
Seok-Woo Jang [1 ]
Gye-Young Kim [2 ]
Young-Jae Park [2 ]
Hyung-Il Choi [3 ]
机构
[1] Dept of Digital Media,Anyang University
[2] Dept of Computing,Soongsil-niversity
[3] Dept of Global Media,Soongsil-University
关键词
component; time series; dynamic time warping; valid range pruning;
D O I
暂无
中图分类号
TP273.2 [];
学科分类号
080201 ; 0835 ;
摘要
Dynamic time warping(DTW)spends most of the time in generating the correlation table,and it establishes the global path constraints to reduce the time complexity.However,the global constraints restrain just in terms of the time axis.In this paper,we therefore propose another version of DTW,to be called branch-and-bound DTW(BnB-DTW),which adaptively controb its global path constraints by reflecting the contents of input patterns. Experimental results show that the suggested BnB-DTW algorithm performs more efficiently than other conventional DTW approaches while not increasing the optimal warping cost.
引用
收藏
页码:168 / 172
页数:5
相关论文
共 50 条
  • [31] Stacking dynamic time warping for the diagnosis of dynamic systems
    Alonso, Carlos J.
    Prieto, Oscar J.
    Rodriguez, Juan J.
    Bregon, Anibal
    Pulido, Belarmino
    [J]. CURRENT TOPICS IN ARTIFICIAL INTELLIGENCE, 2007, 4788 : 11 - +
  • [32] Time Series Clustering Based on Dynamic Time Warping
    Wang, Weizeng
    Lyu, Gaofan
    Shi, Yuliang
    Liang, Xun
    [J]. PROCEEDINGS OF 2018 IEEE 9TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE (ICSESS), 2018, : 487 - 490
  • [33] SSDTW: Shape segment dynamic time warping
    Hong, Jae Yeol
    Park, Seung Hwan
    Baek, Jun-Geol
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2020, 150
  • [34] Branch-and-bound dynamic time warping
    Jang, S. W.
    Park, Y. J.
    Kim, G. Y.
    [J]. ELECTRONICS LETTERS, 2010, 46 (20) : 1374 - 1376
  • [35] Twadn: an efficient alignment algorithm based on time warping for pairwise dynamic networks
    Zhong, Yuanke
    Li, Jing
    He, Junhao
    Gao, Yiqun
    Liu, Jie
    Wang, Jingru
    Shang, Xuequn
    Hu, Jialu
    [J]. BMC BIOINFORMATICS, 2020, 21 (Suppl 13)
  • [36] Learning Discriminative Prototypes with Dynamic Time Warping
    Chang, Xiaobin
    Tung, Frederick
    Mori, Greg
    [J]. 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021, 2021, : 8391 - 8400
  • [37] Non-Markovian Dynamic Time Warping
    Uchida, Seiichi
    Fukutomi, Masahiro
    Ogawara, Koichi
    Feng, Yaokai
    [J]. 2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012), 2012, : 2294 - 2297
  • [38] Dynamic Time Warping Constraints for Semiconductor Processing
    Owens, Rachel
    Sun, Fan-Keng
    Venditti, Christopher
    Blake, Daniel
    Dillon, Jack
    Boning, Duane
    [J]. 2024 35TH ANNUAL SEMI ADVANCED SEMICONDUCTOR MANUFACTURING CONFERENCE, ASMC, 2024,
  • [39] Parallelization of Dynamic Time Warping on a Heterogeneous Platform
    Zheng, Yao
    Xiao, Limin
    Tang, Wenqi
    Shang, Lihong
    Yao, Guangchao
    Ruan, Li
    [J]. IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES, 2014, E97A (11) : 2258 - 2262
  • [40] Local Feature Based Dynamic Time Warping
    Zhang, Zheng
    Tang, Liang
    Tang, Ping
    [J]. 2014 INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA), 2014, : 425 - 429