基于角度关键点和转向点的时间序列趋势特征提取  

Time Series Trend Feature Extraction Based on Angular Key Points and Inflection Points

作　　者：刘冰珂 任芮彬 王溪 LIU Bing-Ke;REN Rui-Bin;WANG Xi(School of Mathematics,Southwest Jiaotong University,Chengdu 611756,China;School of Information Science and Technology,Southwest Jiaotong University,Chengdu 611756,China)

机构地区：[1]西南交通大学数学学院,成都611756 [2]西南交通大学信息科学与技术学院,成都611756

出　　处：《计算机系统应用》2025年第1期285-293,共9页Computer Systems & Applications

基　　金：中央高校基本科研业务费专项资金(2682024ZTPY041);四川省科技计划(2023YFH0066);成都市科技项目(2023-RK00-00080-ZF)。

摘　　要：时间序列分段线性表示算法利用时间序列的趋势变化特征,用序列中较少点来表示整个时间序列.但是大多算法主要关注局部序列点信息,很少关注全局数据,且部分算法只关注算法在数据集上的拟合,很少应用到分类问题中.针对上述问题,本文提出了基于角度关键点和转向点的时间序列趋势特征提取算法,首先,该算法根据序列数据的角度变化值来选择角度显著点,然后基于角度关键点的基础上再提取转向点,根据分段的要求,判断是否进行插值操作,从而得到符合要求的分段点序列.本文在模拟数据和40个公开数据集上进行拟合和分类实验,实验结果表明,本文算法相较于分段聚合近似PAA、自底向下TD、自顶向上BU、基于拐点FFTO、基于转折点和趋势段Trend、基于趋势转折点ITTP等算法,在模拟数据集拟合效果更好;在UCR公开数据集平均拟合误差为1.165;分类准确性同Keogh团队公布的DTW-1NN算法高出2.8%.The piecewise linear representation algorithm of the time series represents the whole series with fewer points according to trend changes in the series.However,most of these algorithms focus on the information of local sequence points and rarely pay attention to global data.Some algorithms only focus on fitting on datasets instead of being applied to classification.To solve these problems,this study proposes an algorithm for extracting trend features from time series based on angle key points and inflection points.The algorithm selects angle key points according to the angle change values of the sequence data and then extracts inflection points based on these key points.It determines whether interpolation is needed according to segmentation requirements,so as to obtain a segmentation sequence meeting the requirements.Fitting and classification experiments are conducted on simulated data and 40 public datasets.Experimental results show that the proposed algorithm exhibits better fitting on the simulated data,compared with other algorithms such as piecewise aggregate approximation(PAA),the TD algorithm,the BU algorithm,the FFTO algorithm based on inflection points,the Trend algorithm based on turning points and trend segments,and the ITTP algorithm based on trend turning points.On the UCR public datasets,the proposed algorithm achieves an average fitting error of 1.165.Its classification accuracy is 2.8%higher than the DTW-1NN algorithm published by Keogh.

关 键 词：角度关键点 转向点 时间序列 分段线性表示 趋势特征 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]