针对地磁序列匹配中动态时间规整(dynamic time warping,DTW)算法计算效率低问题,文章提出一种基于加州大学河滨分校(University of California,Riverside,UCR)优化策略的地磁序列定位算法。在序列匹配时,首先利用重排序策略对序列进行...针对地磁序列匹配中动态时间规整(dynamic time warping,DTW)算法计算效率低问题,文章提出一种基于加州大学河滨分校(University of California,Riverside,UCR)优化策略的地磁序列定位算法。在序列匹配时,首先利用重排序策略对序列进行重新排列,然后利用级联下界约束策略在DTW计算前进行约束,提前筛选指纹库内的待匹配序列,最后将筛选得到的待匹配序列进行序列匹配,并利用全局约束策略与DTW提前抛弃策略对DTW计算进行约束。结果表明,文中提出的算法在使用轨迹长度2.5 m左右的地磁序列进行定位时,可以在保证定位精度几乎不变的情况下,将匹配效率提高90%以上。展开更多
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 archiv...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 incarnation 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.展开更多
文摘针对地磁序列匹配中动态时间规整(dynamic time warping,DTW)算法计算效率低问题,文章提出一种基于加州大学河滨分校(University of California,Riverside,UCR)优化策略的地磁序列定位算法。在序列匹配时,首先利用重排序策略对序列进行重新排列,然后利用级联下界约束策略在DTW计算前进行约束,提前筛选指纹库内的待匹配序列,最后将筛选得到的待匹配序列进行序列匹配,并利用全局约束策略与DTW提前抛弃策略对DTW计算进行约束。结果表明,文中提出的算法在使用轨迹长度2.5 m左右的地磁序列进行定位时,可以在保证定位精度几乎不变的情况下,将匹配效率提高90%以上。
文摘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 incarnation 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.
