实时演进数据序列集的内在模式提取与行为预测  

Intrinsic Mode Extraction and Behavior Prediction for Real-Time Evolution Data Set

作　　者：艾锐峰 欧阳军 程杰 周凯 孙云鹏 AI Rui-Feng;OUYANG Jun;CHENG Jie;ZHOU Kai;SUN Yun-Peng(General Planning Institute,No.63850Troops of PLA,Baicheng 137001,China;Hydrologic Equipment Testing Station,No.63850Troops of PLA,Yantai 264100,China)

机构地区：[1]解放军63850部队总体所 [2]解放军63850部队水文装备试验站

出　　处：《计算机系统应用》2018年第12期75-82,共8页Computer Systems & Applications

基　　金：原总装备部试验技术研究项目(SYJS98170342)~~

摘　　要：复杂系统数据序列集未来行为的预测是一个难点,利用数据挖掘实现预测是有潜力的技术途径.针对包含多元时间序列和非时间序列的实时演进数据集,整合序列分割、聚类、模式在线匹配等处理流程,提出了一种主题发现与联合决策相结合的预测方法.在整个方法构建中,将拟构造的主题发现式预测和联合决策预测融合进前期的序列分割与聚类中,采用多时间粒度、多跨度对序列进行对应分层与分割,聚合形成各层的标准模式集.再以标准模式集,依照预测策略,反向搜索具有高稳定性延展行为的复合模式作为主题模式集,从而实现基于在线模式匹配的行为预测.最后,采用分布式并行计算的架构实现整个处理算法.理论推导和实验数据分析证明,相比传统的时间序列预测方法准确度得到提高.Prediction of future behavior of complex set of data sets is a difficult task. Data mining is a potential technical way. For the real-time evolutionary data sets containing multiple time series and non time sequence, a method of integrating the sequence segmentation, clustering, and pattern matching is proposed, which combines the theme discovery and joint decision. In the whole method construction, the topic discovery prediction and joint decision prediction are fused into the early sequence segmentation and clustering. The sequences are stratified and segmented for forming standard pattern sets of each layer, using multi time granularity and multi span. Then, according to the standard pattern set, with the prediction strategy, the compound pattern with high stability extension behavior is used as the theme pattern. This can predict with online pattern matching. Finally, a distributed parallel computing architecture is used to implement the whole processing algorithm. Theoretical deduction and experimental data analysis show that the accuracy of the method is improved compared with the traditional time series prediction method.

关 键 词：多元时间序列 时间粒度 聚类 主题发现 融合预测 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]