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基于矩阵轮廓的时间序列数据挖掘:Motif发现、异常检测、分割、分类、聚类和相似性连接的统一视角

Time Series data Mining Using the Matrix Profile: A Unifying View of Motif Discovery, Anomaly Detection, Segmentation, Classification, Clustering and Similarity Joins
课程网址: http://videolectures.net/kdd2017_tutorial6_time_series_data_minin...  
主讲教师: Abdullah Al Mueen; Eamonn Keogh
开课单位: 新墨西哥大学;加州大学河滨分校
开课时间: 2017-11-21
课程语种: 英语
中文简介:
矩阵轮廓(以及计算它的算法:STAMP、STAMPI、STOMP、SCRIMP和GPU-STOMP)由于其通用性、多功能性、简单性和可扩展性,有可能彻底改变时间序列数据挖掘。特别是它对时间序列基序发现、时间序列连接、形状发现(分类)、密度估计、语义分割、可视化、聚类等具有重要意义。 教程链接:
课程简介: The Matrix Profile (and the algorithms to compute it: STAMP, STAMPI, STOMP, SCRIMP and GPU-STOMP), has the potential to revolutionize time series data mining because of its generality, versatility, simplicity and scalability. In particular it has implications for time series motif discovery, time series joins, shapelet discovery (classification), density estimation, semantic segmentation, visualization, clustering etc. Link to tutorial:
关 键 词: 矩阵轮廓; 数据挖掘; 时间序列
课程来源: 视频讲座网
数据采集: 2023-07-19:chenxin01
最后编审: 2023-07-19:chenxin01
阅读次数: 24