Series clustering and dynamic periodic patching-based transformer for multivariate time series forecasting

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Applied Soft Computing Pub Date : 2025-03-15 DOI:10.1016/j.asoc.2025.112980
Yijie Wang , Xiao Wu , Jiaying Zhang , Weiping Wang , Linjiang Zheng , Jiaxing Shang
{"title":"Series clustering and dynamic periodic patching-based transformer for multivariate time series forecasting","authors":"Yijie Wang ,&nbsp;Xiao Wu ,&nbsp;Jiaying Zhang ,&nbsp;Weiping Wang ,&nbsp;Linjiang Zheng ,&nbsp;Jiaxing Shang","doi":"10.1016/j.asoc.2025.112980","DOIUrl":null,"url":null,"abstract":"<div><div>Multivariate time series forecasting (MTSF) is widely employed in research-intensive domains, such as weather forecasting. Recently, Transformer-based models have outstanding ability to achieve SOTA performance, benefiting from its self-attention mechanism. However, existing models fall short in capturing multivariate inter-dependencies and local semantic representations. To tackle the above limitations, we propose a series clustering and dynamic periodic patching-based Transformer model named CMDPPformer, with two distinctive characteristics: (1) A channel-mixing module based on series clustering is proposed which can strengthen the association between variables with high sequence similarity, and weaken the effect of uncorrelated variables. Concretely, we use whole-time series clustering to group multivariate time series into clusters. After that, variables in the same cluster share the same Transformer backbone while variables in different clusters do not affect each other. (2) A dynamic periodic patching module is introduced which can better capture semantic information and improve Transformer’s local semantic representation. Concretely, multivariate time series after clustering are dynamically segmented into periodic patches as Transformer’s input token. Experimental results show that CMDPPformer can achieve an overall 13.76% and 10.16% relative improvements than SOTA Transformer-based models on seven benchmarks, covering four real-world applications: energy, weather, illness and economic.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112980"},"PeriodicalIF":7.2000,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Soft Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1568494625002911","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Multivariate time series forecasting (MTSF) is widely employed in research-intensive domains, such as weather forecasting. Recently, Transformer-based models have outstanding ability to achieve SOTA performance, benefiting from its self-attention mechanism. However, existing models fall short in capturing multivariate inter-dependencies and local semantic representations. To tackle the above limitations, we propose a series clustering and dynamic periodic patching-based Transformer model named CMDPPformer, with two distinctive characteristics: (1) A channel-mixing module based on series clustering is proposed which can strengthen the association between variables with high sequence similarity, and weaken the effect of uncorrelated variables. Concretely, we use whole-time series clustering to group multivariate time series into clusters. After that, variables in the same cluster share the same Transformer backbone while variables in different clusters do not affect each other. (2) A dynamic periodic patching module is introduced which can better capture semantic information and improve Transformer’s local semantic representation. Concretely, multivariate time series after clustering are dynamically segmented into periodic patches as Transformer’s input token. Experimental results show that CMDPPformer can achieve an overall 13.76% and 10.16% relative improvements than SOTA Transformer-based models on seven benchmarks, covering four real-world applications: energy, weather, illness and economic.
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
求助全文
约1分钟内获得全文 去求助
来源期刊
Applied Soft Computing
Applied Soft Computing 工程技术-计算机:跨学科应用
CiteScore
15.80
自引率
6.90%
发文量
874
审稿时长
10.9 months
期刊介绍: Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities. Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.
期刊最新文献
Series clustering and dynamic periodic patching-based transformer for multivariate time series forecasting Editorial Board Geometrical invariant generative invisible hyperlinks based on feature points Saccade inspired Attentive Visual Patch Transformer for image sentiment analysis Unmanned aerial vehicle takeoff point search algorithm with information sharing strategy of random trees for multi-area coverage task
×
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