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特征点分段提取的时间序列模式匹配方法

李正欣 刘畅 吴诗辉 郭建胜

李正欣,刘畅,吴诗辉,等. 特征点分段提取的时间序列模式匹配方法[J]. 北京航空航天大学学报,2023,49(7):1593-1599 doi: 10.13700/j.bh.1001-5965.2021.0546
引用本文: 李正欣,刘畅,吴诗辉,等. 特征点分段提取的时间序列模式匹配方法[J]. 北京航空航天大学学报,2023,49(7):1593-1599 doi: 10.13700/j.bh.1001-5965.2021.0546
LI Z X,LIU C,WU S H,et al. Segmentation extraction of feature points for time series pattern matching[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(7):1593-1599 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0546
Citation: LI Z X,LIU C,WU S H,et al. Segmentation extraction of feature points for time series pattern matching[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(7):1593-1599 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0546

特征点分段提取的时间序列模式匹配方法

doi: 10.13700/j.bh.1001-5965.2021.0546
详细信息
    通讯作者:

    E-mail:lizhengxin_2005@163.com

  • 中图分类号: TP391

Segmentation extraction of feature points for time series pattern matching

More Information
  • 摘要:

    常用的时间序列模式匹配方法难以平衡计算复杂度与匹配精度,针对该问题,提出了一种特征点分段提取的时间序列模式匹配方法。提取时间序列每个变量维度上的特征点,降低序列长度;将特征点序列转化为分位点矩阵,利用欧氏距离对分位点矩阵进行相似性度量;在几组时间序列数据集上对所提方法进行分类实验。结果表明:所提方法在降低计算复杂度的同时,获得了较高的匹配精度。

     

  • 图 1  特征点提取示意图

    Figure 1.  Schematic diagram of feature point extraction

    图 2  输入序列(ASL_87, boy)

    Figure 2.  Input sequence (ASL_87, boy)

    图 3  不同方法在ASL数据集上寻找的最相似序列

    Figure 3.  The most similar sequence found by different methods on ASL dataset

    图 4  滑动窗口因子对匹配精度及计算效率的影响

    Figure 4.  Influence of sliding window factor on matching accuracy and calculation efficiency

    图 5  滑动窗口因子对特征点提取的影响

    Figure 5.  Influence of sliding window factor on feature point extraction

    图 6  TD、SFP-ED与DTW计算时间比较

    Figure 6.  Calculation time of TD、SFP-ED and DTW

    表  1  实验数据集

    Table  1.   Detail of experimental datasets

    数据集长度均长变量个数样本量类别
    ASL47~9559222168
    LP115156884
    JV7~2916126409
    EEG25625664222
    WR128~191836862442
    Trace27527512004
    下载: 导出CSV

    表  2  ASL数据集上的匹配精度

    Table  2.   Matching accuracy on ASL dataset

    kSVDDTWACM-DTWTDPDSFP-ED
    10.7180.9770.9260.9720.5790.986
    50.6390.9290.8700.9580.5700.947
    100.5700.9290.8560.9060.5570.944
    下载: 导出CSV

    表  3  6种模式匹配方法在不同准确率下的匹配次数

    Table  3.   Matching times of 6 pattern matching methods under different accuracy rates

    e/%SVDDTWACM-DTWTDPDSFP-ED
    N=1N=5N=10N=1N=5N=10N=1N=5N=10N=1N=5N=10N=1N=5N=10N=1N=5N=10
    0 61 20 12 5 0 0 16 9 0 6 0 0 91 33 14 3 0 0
    10 19 0 3 0 13 0
    20 27 15 0 0 4 0 0 0 25 17 2 0
    30 19 1 2 2 28 3
    40 34 19 13 0 10 5 4 3 36 18 9 4
    50 25 6 5 9 26 1
    60 29 26 12 5 10 13 9 8 34 17 6 3
    70 7 15 15 7 13 10
    80 22 7 14 11 29 17 15 22 23 6 10 7
    90 7 30 25 30 12 15
    100 155 84 60 211 177 148 200 154 125 210 188 135 125 65 52 213 189 173
    下载: 导出CSV

    表  4  6组数据集上的匹配精度

    Table  4.   Accuracy on 6 datasets

    数据集SVDDTWACM-DTWTDPDSFP-ED
    ASL0.7180.9770.9260.9720.5780.986
    LP10.4770.8860.9090.6360.8300.921
    JV0.4890.9630.7590.5480.5810.958
    EEG0.9090.7270.72710.5910.864
    WR0.9770.9770.955110.977
    Trace0.99510.9300.995
    下载: 导出CSV

    表  5  9个分位点提取结果

    Table  5.   Nine quantile results

    Δ最小值点5%10%25%50%75%90%95%最大值点
    1−0.1111−0.1052−0.0975−0.0528−0.0512−0.0506−0.0460−0.04140.0024
    5−0.1111−0.1111−0.1093 −0.0800−0.0516−0.0441−0.01080.00240.0024
    10−0.1111−0.1111−0.1099−0.0926−0.0528−0.0477−0.00750.00240.0024
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-09-13
  • 录用日期:  2021-12-10
  • 网络出版日期:  2022-02-15
  • 整期出版日期:  2023-07-31

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