多源时间序列中具有显著时间间隔的Shapelet对挖掘

Shapelet作为时间序列特征，具有较好的可解释性。Shapelet在行为识别、聚类分析及异常检测等方向均得到了广泛应用。但在电力运行监测、医学图像分析以及流媒体监测等领域，时间序列具有多源、同步的特点，仅对单一源上的时间序列提取Shapelet可能丢失序列间相关性。在Shapelet概念基础上，本文提出p-Shapelet作为不同源的Shapelet间关于时间间隔的特征表达，从而实现分析不同源Shapelet间的相关性。具体地，为找出不同类别样本间时间间隔具有最显著差异的Shapelet对，设计并实现了并行化挖掘的算法p-Shapelet miner。算法采用信息增益对不同源间的Shapelet对进行评价，并找出能最大化信息增益的Shapelet对（p-Shapelet）。利用CMU人体动作捕捉数据集进行实验，验证了算法的有效性与执行效率。

Mining Pair of Shapelets with Significant Time Lags From Multi-Sources Synchronous Time Series

As the feature of a time series, Shapelet holds a good interpretability. Shapelet is widely applied recently in behavior reorganization, clustering and outlier detection, et al. However, time series data are synchronized and multi-sources in domains of electric power operation monitoring, medical image processing and streaming media, The relevance among time series are ignored if only extracting Shapelet from single source independently. Thus, to analyze the relevance of Shapelets from different sources, p-Shapelet is proposed as a new feature expressing time lags among Shapelets based on Shapelet. Specifically, for mining pair of Shapelets with most significant time lags from different classes, a parallel algorithm called the p-Shapelet miner is designed. It evaluates pair of Shapelets from different sources by information gain, and find the one(p-Shapelet) maximums information gain. The effectiveness and efficiency of the algorithm is verified by experiments using CMU motion capture datasets.