CAJ | 학술논문

异常序列作为时间序列的一种特殊模式有着极其重要的作用，但大多数的时间序列利用基于距离的方法进行序列间的相似性度量，忽略了时间序列本身的形态特征。为此，提出了一种基于趋势对比的异常序列检测算法，利用重要点和分段线性相结合的自底向上的线性逼近方法，并以最小化两个目标函数为目的进行相邻分段融合，从而使提取的趋势特征有较高的准确度。而且为了降低提取算法的复杂度问题，对采集到的时间序列先进行道格拉斯-普克算法的冗余点删除，保持序列整体形态的同时从一定程度上减少了计算量。最后通过仿真实验，验证了所提出的检测算法的有效性，不仅提高了检测的准确率，还增强了序列趋势变化观测的直观性。
이상서렬작위시간서렬적일충특수모식유착겁기중요적작용，단대다수적시간서렬이용기우거리적방법진행서렬간적상사성도량，홀략료시간서렬본신적형태특정。위차，제출료일충기우추세대비적이상서렬검측산법，이용중요점화분단선성상결합적자저향상적선성핍근방법，병이최소화량개목표함수위목적진행상린분단융합，종이사제취적추세특정유교고적준학도。이차위료강저제취산법적복잡도문제，대채집도적시간서렬선진행도격랍사-보극산법적용여점산제，보지서렬정체형태적동시종일정정도상감소료계산량。최후통과방진실험，험증료소제출적검측산법적유효성，불부제고료검측적준학솔，환증강료서렬추세변화관측적직관성。
As a special mode of time series,abnormal sequence plays a very important role. But most time series use the distance-based method in similarity measure,ignore the morphological feature of time series itself. Therefore,this paper proposes an abnormal series detection algorithm based on trend contrast,makes use of the bottom-up linear approximation method with combination of important point and the piecewise linearization. And to extract trend features with higher accuracy, the paper fuses the adjacent subsection on the premise of minimizing the two new optimal object function. Moreover, for the purpose of reducing the complexity of the algorithm, time series were collected after deleting redundant point by Douglas-Peucker algorithm,which keeps the whole trend feature of the sequence and to some extent reduces the amount of calculations. The results of simulation demonstrate that the detection algorithm is feasible,and it not only improves the detection accuracy,but also enhances the sequence visu-alization of the change in trend.