基于线性分段与HMM的时间序列分类算法

Time Series Classification Algorithm Based on Linear Segmentation and HMM

抽象出时间序列的多段线性特征,并提出一种时间序列分类算法.该算法包括3个模块:导数估值函数,线性分段方法,DDHMM模型(基于HMM).首先,利用导数估值函数与线性分段方法检测多段线性特征,若满足多线段特征,则将时间序列转化为特定结构的观察值序列;然后,利用训练观察值序列训练DDHMM模型,通过比较各模型产生测试观察值序列的概率值进行分类.实验表明,针对满足多段线性特征的时间序列,该算法具有较高的分类精度,应用在UCI数据集和实际工程中,分类效果好.

The multi - segment linear （ MSL ） feature of the time series are collected, and a time series classification algorithm is proposed, which consists of derivative estimation function, linear segmentation method and DDHMM model （base on HMM）. Firstly, segmentation method can be used together to detect the the derivative estimation function and the linear MSL feature. If they are matched, time series can be converted into observed sequence with a special structure. Next, the training observed sequences can be used to train DDHMM models. After training, the time series are classified through comparing the probability value of testing observed sequences generated by each model. The experimental results show that the proposed algorithm has a high accuracy when classifying the time series that match the MSL feature, and it has good performance in the classification on the UCI dataset and the actual projects.