UM  > 科技學院  > 電腦及資訊科學系
Fast multi-subsequence monitoring on streaming time-series based on Forward-propagation
Xueyuan Gong; Simon Fong; Yain-Whar Si
2018-06
Source PublicationINFORMATION SCIENCES
ISSN0020-0255
Volume450Pages:73-88
Abstract

Streaming time-series has drawn unprecedented interests from the computer science researchers. It requires faster execution time and less memory space than traditional approaches in processing historical time-series. Given the real-time constraint in the analysis over streaming time-series, a proper pre-processing step may not even be applicable. Subsequence monitoring is one of the main functions used in a wide range of time series related applications, e.g. quantitative trading in the stock market. In this paper, we propose a novel approach for multi-subsequence monitoring on streaming time-series. The proposed Forward-propagation NSPRING (FPNS) approach is inspired by the forward propagation mechanism in Artificial Neural Networks (ANN). In our proposed approach the concept of forward propagation is adopted to by-pass the unnecessary calculations as in NSPRING where the whole matrix is computed for the final result. FPNS computes a small part of the matrix by indexing only the necessary calculations with the aid of the forward propagation mechanism. As a result, FPNS can effectively reduce the execution time. In the experiments, we compared the scalability, execution time and memory requirement of FPNS, NSPRING, and UCR-DTW using synthetic and real datasets. The experimental results show that on average, FPNS is about three times faster than NSPRING and one order of magnitude faster than UCR-DTW. In addition, FPNS preserves the same accuracy with NSPRING while FPNS runs much faster than NSPRING. (C) 2018 Elsevier Inc. All rights reserved.

KeywordStreaming Time-series Subsequence Monitoring Spring Nspring Fpns Dtw
DOI10.1016/j.ins.2018.03.023
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems
WOS IDWOS:000432646100004
PublisherELSEVIER SCIENCE INC
The Source to ArticleWOS
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorXueyuan Gong; Simon Fong; Yain-Whar Si
AffiliationDepartment of Computer and Information Science, University of Macau, Macau, China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Xueyuan Gong,Simon Fong,Yain-Whar Si. Fast multi-subsequence monitoring on streaming time-series based on Forward-propagation[J]. INFORMATION SCIENCES,2018,450:73-88.
APA Xueyuan Gong,Simon Fong,&Yain-Whar Si.(2018).Fast multi-subsequence monitoring on streaming time-series based on Forward-propagation.INFORMATION SCIENCES,450,73-88.
MLA Xueyuan Gong,et al."Fast multi-subsequence monitoring on streaming time-series based on Forward-propagation".INFORMATION SCIENCES 450(2018):73-88.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Xueyuan Gong]'s Articles
[Simon Fong]'s Articles
[Yain-Whar Si]'s Articles
Baidu academic
Similar articles in Baidu academic
[Xueyuan Gong]'s Articles
[Simon Fong]'s Articles
[Yain-Whar Si]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Xueyuan Gong]'s Articles
[Simon Fong]'s Articles
[Yain-Whar Si]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.