research-article

Co-occurrence Order-preserving Pattern Mining with Keypoint Alignment for Time Series

Authors:

Xindong WuAuthors Info & Claims

ACM Transactions on Management Information Systems, Volume 15, Issue 2

Article No.: 9, Pages 1 - 27

https://doi.org/10.1145/3658450

Published: 12 June 2024 Publication History

Abstract

Recently, order-preserving pattern (OPP) mining has been proposed to discover some patterns, which can be seen as trend changes in time series. Although existing OPP mining algorithms have achieved satisfactory performance, they discover all frequent patterns. However, in some cases, users focus on a particular trend and its associated trends. To efficiently discover trend information related to a specific prefix pattern, this article addresses the issue of co-occurrence OPP mining (COP) and proposes an algorithm named COP-Miner to discover COPs from historical time series. COP-Miner consists of three parts: extracting keypoints, preparation stage, and iteratively calculating supports and mining frequent COPs. Extracting keypoints is used to obtain local extreme points of patterns and time series. The preparation stage is designed to prepare for the first round of mining, which contains four steps: obtaining the suffix OPP of the keypoint sub-time series, calculating the occurrences of the suffix OPP, verifying the occurrences of the keypoint sub-time series, and calculating the occurrences of all fusion patterns of the keypoint sub-time series. To further improve the efficiency of support calculation, we propose a support calculation method with an ending strategy that uses the occurrences of prefix and suffix patterns to calculate the occurrences of superpatterns. Experimental results indicate that COP-Miner outperforms the other competing algorithms in running time and scalability. Moreover, COPs with keypoint alignment yield better prediction performance.

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Cited By

Lan DSun CDong XQiu PGong YLiu XFournier-Viger PZhang C(2025) TK-RNSP: Efficient Top- Repetitive Negative Sequential Pattern mining Information Processing & Management10.1016/j.ipm.2025.10407762:3(104077)Online publication date: May-2025
https://doi.org/10.1016/j.ipm.2025.104077
Andrzejewski WBoinski P(2025)Co-location pattern mining using approximate Euclidean measureInformation Sciences10.1016/j.ins.2025.122000706(122000)Online publication date: Jul-2025
https://doi.org/10.1016/j.ins.2025.122000
Li LZuba WLoukides GPissis SMatsangidou M(2024)Scalable Order-Preserving Pattern Mining2024 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM59182.2024.00028(211-220)Online publication date: 9-Dec-2024
https://doi.org/10.1109/ICDM59182.2024.00028
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Index Terms

Co-occurrence Order-preserving Pattern Mining with Keypoint Alignment for Time Series
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Transactions on Management Information Systems

ACM Transactions on Management Information Systems Volume 15, Issue 2

June 2024

102 pages

EISSN:2158-6578

DOI:10.1145/3613621

Editor:
Heng Xu
University of Florida, USA

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Publication History

Published: 12 June 2024

Online AM: 13 April 2024

Accepted: 30 March 2024

Revised: 20 October 2023

Received: 29 April 2023

Published in TMIS Volume 15, Issue 2

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Cited By

Lan DSun CDong XQiu PGong YLiu XFournier-Viger PZhang C(2025) TK-RNSP: Efficient Top- Repetitive Negative Sequential Pattern mining Information Processing & Management10.1016/j.ipm.2025.10407762:3(104077)Online publication date: May-2025
https://doi.org/10.1016/j.ipm.2025.104077
Andrzejewski WBoinski P(2025)Co-location pattern mining using approximate Euclidean measureInformation Sciences10.1016/j.ins.2025.122000706(122000)Online publication date: Jul-2025
https://doi.org/10.1016/j.ins.2025.122000
Li LZuba WLoukides GPissis SMatsangidou M(2024)Scalable Order-Preserving Pattern Mining2024 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM59182.2024.00028(211-220)Online publication date: 9-Dec-2024
https://doi.org/10.1109/ICDM59182.2024.00028
Yuan JWu FZhao LPan DYu X(2024)DCFA-iTimeNet: Dynamic cross-fusion attention network for interpretable time series predictionApplied Intelligence10.1007/s10489-024-05973-255:2Online publication date: 6-Dec-2024
https://dl.acm.org/doi/10.1007/s10489-024-05973-2

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