CAJ | 학술논문

有效趋势的提取可为监控对象提供早期预警、状态评估和决策支持.传统的曲线趋势分析算法有滑动窗口(SW)算法、外推式在线数据分割(OSO)算法,二者均采用常规最小二乘法进行曲线拟合.与常规最小二乘法相比,总体最小二乘法具有更高的直线拟合精度.此外,针对SW算法的滑动窗口最大长度没有限制,当检测点阈值比较大时,窗口的长度可能很长;而OSD算法限定了最小滑动窗口长度,使得在最小滑动窗口内的突变点无法检测.针对SW算法和OSD算法的缺陷,提出了一种新的数据流趋势分析方法,该方法采用总体最小二乘法对数据流进行分段拟合,提高了趋势分析精度;还提出了可变滑动窗口算法解决SW算法和OSD算法的固定窗口问题,以实现对数据流的合理分割.实验结果表明,有效性较为明显.
유효추세적제취가위감공대상제공조기예경、상태평고화결책지지.전통적곡선추세분석산법유활동창구(SW)산법、외추식재선수거분할(OSO)산법,이자균채용상규최소이승법진행곡선의합.여상규최소이승법상비,총체최소이승법구유경고적직선의합정도.차외,침대SW산법적활동창구최대장도몰유한제,당검측점역치비교대시,창구적장도가능흔장;이OSD산법한정료최소활동창구장도,사득재최소활동창구내적돌변점무법검측.침대SW산법화OSD산법적결함,제출료일충신적수거류추세분석방법,해방법채용총체최소이승법대수거류진행분단의합,제고료추세분석정도;환제출료가변활동창구산법해결SW산법화OSD산법적고정창구문제,이실현대수거류적합리분할.실험결과표명,유효성교위명현.
Efficient trend extraction methods can provide early warnings,severity assessments of monitored subjects and information for decision support.The traditional algorithms for trend analysis of Curves include Sliding Window algorithm(SW) and Extrapolation for On-line Segmentation of Data algorithm(OSD),which use total least squares for curve fitting.Compared with conventional least squares,the total least squares has a higher accuracy of fitting a straight line.In addition,since there is no restriction on the maximum length of the sliding window for SW algorithm.the length of window can be very long when threshold for Detection of point becomes larger.As OSD algorithm restricts the minimum length of sliding window,mutations within minimum sliding window cannot be detected for defects of the SW algorithm and the OSD algorithm.This paper presents a new method for trend analysis of data streams.The method uses total least squares to improve the accuracy of trend analysis.It also presents variable sliding window algorithm to solve the fixed window problem with the SW algorithm and OSD algorithm to achieve a reasonable segmentation for data streams.The experimental results show that the method is effective.