有监督时间序列分割与状态识别算法

时间序列分割与状态识别是一项重要的时间序列挖掘任务,可用于识别被监测对象的运行状态,然而目前多数无监督时间序列分割算法得到的结果无法满足用户的状态识别期望。为实现符合用户意图的时间序列分割,提出一种有监督的时间序列分割算法。构造特征集合并自动训练特征概率模型参数,以此构建特征高斯概率分布模型进行相关序列的特征设计,同时利用匹配损失计算和改进的贪心策略设定特征权重约束,通过增加分割位置约束条件及增量计算2种优化方式提高分割效率。在多个真实数据集上的实验结果表明,与pHMM和AutoPlait算法相比,该算法可以全面表达状态类别,对时间序列进行更精准的分割。

Algorithm of Supervised Time Series Segmentation and State Recognition

Time series segmentation and state recognition is an important time series mining task that can be used to automatically identify the running state of the monitored object,but servel unsupervised time series segmentation algorithms fail to meet the state recognition expectation of users.To address the problem,this paper proposes a supervised time series segmentation algorithm.It constructs a characteristic set and on this basis trains the parameters of the characteristic probability model automatically,so as to build the characteristic Gaussian probability distribution model and design the characteristics of the related sequence.Meanwhile,the matching loss calculation and improved greedy strategy are used to design feature weight constraints,and the segmentation efficiency is increased by using two optimization methods:adding constraints of segmentation positions and incremental calculation.Experimental results on multiple real data sets show that,compared with the pHMM and AutoPlait algorithms,the proposed algorithm can fully express all categories of states and implement more accurate segmentation of time series.