FRUITS: feature extraction using iterated sums for time series classification

IF 2.8 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Data Mining and Knowledge Discovery Pub Date : 2024-09-14 DOI:10.1007/s10618-024-01068-1
Joscha Diehl, Richard Krieg
{"title":"FRUITS: feature extraction using iterated sums for time series classification","authors":"Joscha Diehl, Richard Krieg","doi":"10.1007/s10618-024-01068-1","DOIUrl":null,"url":null,"abstract":"<p>We introduce a pipeline for time series classification that extracts features based on the iterated-sums signature (ISS) and then applies a linear classifier. These features are intrinsically nonlinear, capture chronological information, and, under certain settings, are invariant to a form of time-warping. We achieve competitive results, both in accuracy and speed, on the UCR archive. We make our code available at https://github.com/irkri/fruits.</p>","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data Mining and Knowledge Discovery","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10618-024-01068-1","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

We introduce a pipeline for time series classification that extracts features based on the iterated-sums signature (ISS) and then applies a linear classifier. These features are intrinsically nonlinear, capture chronological information, and, under certain settings, are invariant to a form of time-warping. We achieve competitive results, both in accuracy and speed, on the UCR archive. We make our code available at https://github.com/irkri/fruits.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
FRUITS:利用迭代和进行时间序列分类的特征提取
我们介绍了一种时间序列分类方法,它根据迭和特征(ISS)提取特征,然后应用线性分类器。这些特征本质上是非线性的,能捕捉时间信息,而且在某些设置下,不受某种形式的时间扭曲的影响。我们在 UCR 档案中取得了极具竞争力的准确性和速度。我们在 https://github.com/irkri/fruits 网站上提供了我们的代码。
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 去求助
来源期刊
Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery 工程技术-计算机:人工智能
CiteScore
10.40
自引率
4.20%
发文量
68
审稿时长
10 months
期刊介绍: Advances in data gathering, storage, and distribution have created a need for computational tools and techniques to aid in data analysis. Data Mining and Knowledge Discovery in Databases (KDD) is a rapidly growing area of research and application that builds on techniques and theories from many fields, including statistics, databases, pattern recognition and learning, data visualization, uncertainty modelling, data warehousing and OLAP, optimization, and high performance computing.
期刊最新文献
FRUITS: feature extraction using iterated sums for time series classification Bounding the family-wise error rate in local causal discovery using Rademacher averages Evaluating the disclosure risk of anonymized documents via a machine learning-based re-identification attack Efficient learning with projected histograms Opinion dynamics in social networks incorporating higher-order interactions
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1