传感器技术的普及使得时间序列数据受到人们越来越多的关注.早期时间序列分类(Early Time Series Classification, ETSC)希望通过观测尽可能短的时序数据而对其做出尽可能准确的分类,已在科技金融领域发挥着重要的作用.首先概述了常见...传感器技术的普及使得时间序列数据受到人们越来越多的关注.早期时间序列分类(Early Time Series Classification, ETSC)希望通过观测尽可能短的时序数据而对其做出尽可能准确的分类,已在科技金融领域发挥着重要的作用.首先概述了常见的时间序列分类器,并综述了基于最小预测长度、基于最大区分子序列和基于模型的3类早期时间序列分类框架的最新研究进展.然后在每类方法中,分析了具有代表性的早期时间序列分类模型的关键技术及其优缺点;整理了科技金融领域的公开数据集和常见的评价指标.最后对未来的发展趋势做了展望.展开更多
关于时间序列分类的问题在过去十多年时间里已经引起极大的兴趣.并且已经有实验表明传统流行的分类算法像KNN等,已经很难处理时间序列的分类问题.基于Shapelet和DTW(动态时间规整,Dynamic Time Warping)的这一分类方法的时间复杂度又太...关于时间序列分类的问题在过去十多年时间里已经引起极大的兴趣.并且已经有实验表明传统流行的分类算法像KNN等,已经很难处理时间序列的分类问题.基于Shapelet和DTW(动态时间规整,Dynamic Time Warping)的这一分类方法的时间复杂度又太高.本文提出一种新的基于子段距离计算的时序分类方法,通过对时间序列进行切分然后对切分后的子段用k-shape算法进行聚类,在聚类结果中寻找两类时间序列各自比较有区分性的片段,并以此来作为分类的依据,该方法思路更为简单且时间复杂度不高.通过实验验证了我们算法的分类精度和适用性,并与shaplet算法相比我们算法在时间复杂度上更具优势.展开更多
Classification of multi-dimension time series(MTS) plays an important role in knowledge discovery of time series. Many methods for MTS classification have been presented. However, most of these methods did not conside...Classification of multi-dimension time series(MTS) plays an important role in knowledge discovery of time series. Many methods for MTS classification have been presented. However, most of these methods did not consider the kind of MTS whose discriminative subsequence was not restricted to one dimension and dynamic. In order to solve the above problem, a method to extract new features with extended shapelet transformation is proposed in this study. First, key features is extracted to replace k shapelets to calculate distance, which are extracted from candidate shapelets with one class for all dimensions. Second, feature of similarity numbers as a new feature is proposed to enhance the reliability of classification. Third, because of the time-consuming searching and clustering of shapelets, distance matrix is used to reduce the computing complexity. Experiments are carried out on public dataset and the results illustrate the effectiveness of the proposed method. Moreover, anode current signals(ACS) in the aluminum reduction cell are the aforementioned MTS, and the proposed method is successfully applied to the classification of ACS.展开更多
文摘传感器技术的普及使得时间序列数据受到人们越来越多的关注.早期时间序列分类(Early Time Series Classification, ETSC)希望通过观测尽可能短的时序数据而对其做出尽可能准确的分类,已在科技金融领域发挥着重要的作用.首先概述了常见的时间序列分类器,并综述了基于最小预测长度、基于最大区分子序列和基于模型的3类早期时间序列分类框架的最新研究进展.然后在每类方法中,分析了具有代表性的早期时间序列分类模型的关键技术及其优缺点;整理了科技金融领域的公开数据集和常见的评价指标.最后对未来的发展趋势做了展望.
文摘关于时间序列分类的问题在过去十多年时间里已经引起极大的兴趣.并且已经有实验表明传统流行的分类算法像KNN等,已经很难处理时间序列的分类问题.基于Shapelet和DTW(动态时间规整,Dynamic Time Warping)的这一分类方法的时间复杂度又太高.本文提出一种新的基于子段距离计算的时序分类方法,通过对时间序列进行切分然后对切分后的子段用k-shape算法进行聚类,在聚类结果中寻找两类时间序列各自比较有区分性的片段,并以此来作为分类的依据,该方法思路更为简单且时间复杂度不高.通过实验验证了我们算法的分类精度和适用性,并与shaplet算法相比我们算法在时间复杂度上更具优势.
基金Projects(61773405,61725306,61533020)supported by the National Natural Science Foundation of ChinaProject(2018zzts583)supported by the Fundamental Research Funds for the Central Universities,China
文摘Classification of multi-dimension time series(MTS) plays an important role in knowledge discovery of time series. Many methods for MTS classification have been presented. However, most of these methods did not consider the kind of MTS whose discriminative subsequence was not restricted to one dimension and dynamic. In order to solve the above problem, a method to extract new features with extended shapelet transformation is proposed in this study. First, key features is extracted to replace k shapelets to calculate distance, which are extracted from candidate shapelets with one class for all dimensions. Second, feature of similarity numbers as a new feature is proposed to enhance the reliability of classification. Third, because of the time-consuming searching and clustering of shapelets, distance matrix is used to reduce the computing complexity. Experiments are carried out on public dataset and the results illustrate the effectiveness of the proposed method. Moreover, anode current signals(ACS) in the aluminum reduction cell are the aforementioned MTS, and the proposed method is successfully applied to the classification of ACS.
