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Fast multi-subsequence monitoring on streaming time-series based on Forward-propagation
Xueyuan Gong; Simon Fong; Yain-Whar Si

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
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Indexed BySCI
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems
WOS IDWOS:000432646100004
The Source to ArticleWOS
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Document TypeJournal article
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.
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