基于规则迭代的时间序列特征提取模型  

Model of time series feature extraction based on rule iteration

作　　者：谭海 陈利军 张军[2] 张鑫 高方方 徐华健 TAN Hai;CHEN Li-jun;ZHANG Jun;ZHANG Xin;GAO Fang-fang;XU Hua-jian(School of Mechanical and Electronic Engineering,East China University of Technology,Nanchang 330013,China;School of Information Engineering,East China University of Technology,Nanchang 330013,China;School of Geophysics and Measurement Control Technology,East China University of Technology,Nanchang 330013,China)

机构地区：[1]东华理工大学机械与电子工程学院,江西南昌330013 [2]东华理工大学信息工程学院,江西南昌330013 [3]东华理工大学地球物理与测控技术学院,江西南昌330013

出　　处：《计算机工程与设计》2019年第1期236-240,共5页Computer Engineering and Design

基　　金：国家自然科学基金项目(61462004);教育部核技术应用工程研究中心开放基金项目(HJSJYB2016-4);东华理工大学研究生创新专项资金基金项目(DHYC-2016013);国家级大学生创新创业训练计划基金项目(201510405020;201710405025)

摘　　要：针对时间序列分类特征提取困难、提取的特征不明显等问题,提出一种基于普通规则嵌套迭代的时间序列特征提取模型。以常规的序列特征提取规则为基本单元,构建多级迭代规则对时间序列数据进行空间变换产生特征序列集,采用基本的特征提取方法对序列集进行特征提取得到最终的特征集合,模型的迭代次数和规则可由用户自己设定。以用户实际用电序列数据为例,对原始用电序列数据作为特征和该模型提取的特征进行分类比较,实验结果表明,在规则迭代模型下提取的特征,分类评价指标MAP最高可达到0.5389,对分类性能提升的增幅最高可达48.83%。A feature extraction model of time series based on regular nested iteration was proposed,which deal with the difficulty of feature extraction and the lack of obvious features in time series classification.Conventional sequence feature extraction rules were regarded as the basic unit,a multi-level iterative rule was constructed to transform the time series data to generate the feature sequence set,and the basic feature extraction method was used to extract the feature from the sequence set to get the feature set.The number of iterations and the rules of the model could be set by the user.Taking the user actual electricity consumption sequence data as an example,the original time series was compared as a feature with the feature extracted from this model.Experimental results show that the features extracted using the rule iteration model,the classification evaluation index MAP can reach up to 0.5389,and the increase in classification performance is up to 48.83%.

关 键 词：时间序列 特征提取 规则迭代 序列分类 长短时记忆 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]