基于分段聚合时间弯曲距离的时间序列挖掘

Time series mining based on piecewise aggregate time warping distance

针对时间序列的相似性度量问题,提出基于分段聚合时间弯曲距离的时间序列挖掘方法。首先运用经典分段聚合近似方法来对时间序列进行数据变换,实现时间序列的特征提取和数据降维,然后利用动态时间弯曲距离进行距离计算,最后将其应用于时间序列聚类和分类的数值实验中。新方法不仅过程简单、易于实现,而且实验结果表明其平均分类错误率与传统分段时间弯曲相比,几乎降低了50%。同时,新方法在运行时间和聚类挖掘结果上都具有一定的优势。

To measure the similarity of time series,a method of time series mining based on piecewise aggregate time warping distance was proposed.Piecewise aggregate approximation was used to transform the data and to extract features from time series so as to reduce dimensionality.After completing the transformation of time series,dynamic time warping was applied to measure the distance between two time series.The proposed method is easy to carry out and the classification result demonstrates that it gets approximately 50% decline of classification error rate of the traditional segmented time warping distance.The new method also has a good performance at running time and clustering in data mining.