Ultra fast warping window optimization for Dynamic Time Warping

Chang Wei Tan, Matthieu Herrmann, Geoffrey I. Webb
{"title":"Ultra fast warping window optimization for Dynamic Time Warping","authors":"Chang Wei Tan, Matthieu Herrmann, Geoffrey I. Webb","doi":"10.1109/ICDM51629.2021.00070","DOIUrl":null,"url":null,"abstract":"The Dynamic Time Warping (DTW) similarity measure is widely used in many time series data mining applications. It computes the cost of aligning two series, smaller costs indicating more similar series. Most applications require tuning of DTW’s Warping Window (WW) parameter in order to achieve good performance. This parameter controls the amount of warping allowed, reducing pathological alignments, with the added benefit of speeding up computation. However, since DTW is in itself very costly, learning the WW is a burdensome process, requiring days even for datasets containing only a few thousand series. In this paper, we propose ULTRAFASTWWSEARCH, a new algorithm able to learn the WW significantly faster than the state-of-the-art FASTWWSEARCH method. ULTRAFASTWWSEARCH builds upon the latter, exploiting the properties of a new efficient exact DTW algorithm which supports early abandoning and pruning (EAP). We show on 128 datasets from the UCR archive that ULTRAFASTWWSEARCH is up to an order of magnitude faster than the previous state of the art.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on Data Mining (ICDM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDM51629.2021.00070","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

The Dynamic Time Warping (DTW) similarity measure is widely used in many time series data mining applications. It computes the cost of aligning two series, smaller costs indicating more similar series. Most applications require tuning of DTW’s Warping Window (WW) parameter in order to achieve good performance. This parameter controls the amount of warping allowed, reducing pathological alignments, with the added benefit of speeding up computation. However, since DTW is in itself very costly, learning the WW is a burdensome process, requiring days even for datasets containing only a few thousand series. In this paper, we propose ULTRAFASTWWSEARCH, a new algorithm able to learn the WW significantly faster than the state-of-the-art FASTWWSEARCH method. ULTRAFASTWWSEARCH builds upon the latter, exploiting the properties of a new efficient exact DTW algorithm which supports early abandoning and pruning (EAP). We show on 128 datasets from the UCR archive that ULTRAFASTWWSEARCH is up to an order of magnitude faster than the previous state of the art.
动态时间翘曲的超快速翘曲窗口优化
动态时间翘曲(DTW)相似性度量在时间序列数据挖掘中得到了广泛的应用。它计算对齐两个序列的成本,较小的成本表示更相似的序列。大多数应用程序需要调整DTW的翘曲窗口(WW)参数以获得良好的性能。该参数控制允许的扭曲量,减少病态对齐,并带来加速计算的额外好处。然而,由于DTW本身是非常昂贵的,学习WW是一个繁重的过程,即使只包含几千个序列的数据集也需要几天的时间。在本文中,我们提出了一种新的算法ULTRAFASTWWSEARCH,它能够比最先进的FASTWWSEARCH方法更快地学习WW。ULTRAFASTWWSEARCH建立在后者的基础上,利用了一种新的高效精确DTW算法的特性,该算法支持早期放弃和修剪(EAP)。我们展示了来自UCR存档的128个数据集,ULTRAFASTWWSEARCH比以前的技术水平快了一个数量级。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信