一种基于数据驱动的动态时序分类算法

针对物联网时序数据中存在的数据冗余现象和动态信息难以捕捉的问题，提出了一种基于数据驱动的动态时序分类算法。通过动态内部主元分析法（dynamic internal principal component analysis,DiPCA）提取传感设备采集的时间序列中的动态信息，实现降维及提炼动态信息的作用；利用麻雀搜索算法优化分类算法参数，强化支持向量机(support vector machines,SVM)算法性能并使其对含有shapelet局部特征的时序特征进行建模，最终构成双向演进算法框架，实现时序分类功能。利用UCR时序数据集和边缘计算模拟数据检验该算法的性能，结果表明，与基本算法相比，该算法的综合性能明显提高，并验证算法分类功能在仿真环境中的有效性与优越性。

A data-driven dynamic time series classification algorithm

Aiming at the problems of data redundancy and difficulty in capturing dynamic information in IoT time series data, this paper proposes a data-driven dynamic time series classification algorithm. The dynamic information in the time series collected by sensing devices is extracted by DiPCA (dynamic internal principal component analysis) to realize the role of dimensionality reduction and refining dynamic information; the parameters of the classification algorithm are optimized by using the sparrow search algorithm to enhance the performance of the SVM algorithm and make it model the temporal features containing shapelet local features, which finally constitutes a two-way evolutionary algorithm framework to realize the temporal classification function. The performance of the algorithm is examined using UCR temporal data set and edge computing simulation data, and the results show that the comprehensive performance of the algorithm is significantly improved compared with the basic algorithm, and the effectiveness and superiority of the classification function of the algorithm in the simulation environment is verified.