The UCR Time Series Archive

• 作 者: Hoang Anh Dau Anthony Bagnall Kaveh Kamgar Chin-Chia Michael Yeh Yan Zhu Shaghayegh Gharghabi Chotirat Ann Ratanamahatana Eamonn Keogh
• 机 构: Department of Computer Science and Engineering, University of California, Riverside, CA 92521 USA;Sc
• 出 处: 自动化学报（英文版）
• 发表日期: 2019年6期
• 关键词: Data mining time series classification UCR time series archive

The UCR time series archive-introduced in 2002,has become an important resource in the time series data mining community,with at least one thousand published papers making use of at least one data set from the archive.The original incation of the archive had sixteen data sets but since that time,it has gone through periodic expansions.The last expansion took place in the summer of 2015 when the archive grew from 45 to 85 data sets.This paper introduces and will focus on the new data expansion from 85 to 128 data sets.Beyond expanding this valuable resource,this paper offers pragmatic advice to anyone who may wish to evaluate a new algorithm on the archive.Finally,this paper makes a novel and yet actionable claim:of the hundreds of papers that show an improvement over the standard baseline (1-nearest neighbor classification),a fraction might be mis-attributing the reasons for their improvement.Moreover,the improvements claimed by these papers might have been achievable with a much simpler modification,requiring just a few lines of code.