基于分段时间弯曲距离的时间序列挖掘

在时间序列库中的数据挖掘是个重要的课题，为了在挖掘的过程中比较序列的相似性，大量的研究都采用了欧氏距离度量或者其变形，但是欧氏距离及其变形对序列在时间轴上的偏移非常敏感．因此，采用了更鲁棒的动态时间弯曲距离，允许序列在时间轴上的弯曲，并且提出了一种新的序列分段方法，在此基础上定义了特征点分段时间弯曲距离．与经典时间弯曲距离相比，大大提高了效率，而且保证了近似的准确性．

Data Mining Based on Segmented Time Warping Distance in Time Series Database

Data mining in time series database is an important task, most research work are based comparing time series with Euclidean distance measure or its transformations. However Euclidean distance measure will change greatly when the compared time series move slightly along the time-axis. It' s impossible to get satisfactory result when using Euclidean distance in many cases. Dynamic time warping distance is a good way to deal with these cases, but it' s very difficult to compute which limits its application. In this paper, a novel method is proposed to avoid the drawback of Euclidean distance measure. It first divides time series into several line segments based on some feature points which are Chosen by some heuristic method. Each time series is converted into a segmented sequence, and then a new distance measure called feature points segmented time warping distance is defined based this segmentation. Compared with the classical dynamic time warping distance, this new method is much more fast in speed and almost no degrade in accuracy. Finally, implements two completed and detailed experiments to prove its superiority.