CAJ | 학술논문

本文提出了一种无限长时间序列的分段线性拟合(Infinite Tune Series-Piecewiee Linear Fitting,简称ITS_PLF)算法,该算法根据关键点保持时间段的统计特性,确定选择关键点的区间范围;若极值点的保持时间段不在区间范围,则根据包含极值点的连续三个时间数据之间的夹角与筛选角度之间的关系,判断该极值点成为关键点的可能性.实验表明,ITS_PLF算法的执行不依赖于时间序列长度及领域知识,可以有效识别关键点,并可根据数据压缩率的变化实现自适应拟合.
본문제출료일충무한장시간서렬적분단선성의합(Infinite Tune Series-Piecewiee Linear Fitting,간칭ITS_PLF)산법,해산법근거관건점보지시간단적통계특성,학정선택관건점적구간범위;약겁치점적보지시간단불재구간범위,칙근거포함겁치점적련속삼개시간수거지간적협각여사선각도지간적관계,판단해겁치점성위관건점적가능성.실험표명,ITS_PLF산법적집행불의뢰우시간서렬장도급영역지식,가이유효식별관건점,병가근거수거압축솔적변화실현자괄응의합.
In order to resolving the problem of depending on the length of time series and domain knowledge of traditional PLF algorithm, we proposed a Piecewise Linear Fitting algorithm for Infinite Time Series (ITS _ PLF).To determine the interval of Key Points selecting, the statistical attributes of maintaining time of these Key Paints was considered. If the maintaining time of a Extreme Point beyond the selection interval, the relation between the threshold angle and the angle of three consecutive data points containing the Extreme Point was selected to detennine whether the Extreme Point was a Key Point or not.The experimental results show that ITS _ PLF algorithm does not depend on the length of time series and domain knowledge,can effectively identify the Key Point and adaptively fit the time series according to the changing of the data compression ratio.