Detecting Asynchronous Periodic Patterns of Intervals in Temporal Sequence Data

Jian Liu, Zhenrui Wang, Mingyang Li, Biao Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Temporal sequence data, such as event logs collected from industrial operations, often contain both sequential and time interval information. These two types of information can be used to define periodic patterns. Due to random disturbance, the occurrence of such periodic patterns may be drifted from their expected positions or time, resulting in asynchronous periodic patterns (APPs). The major limitation of existing studies on APPs is their reliance on subjective and case-specific determination of tolerance to the asynchrony. In real-world practice, however, knowledge on such tolerance values may be extremely limited, if not unavailable. To address this limitation, this paper formulates the asynchrony tolerating as a hierarchical clustering of time intervals embedded in the temporal sequence data. The clustering method is improved to balance the trade-off between data similarity and pattern interpretability. Based on the symbol sequence generated by the improved clustering method, APPs are detected with the proposed convolution-based periodicity detection algorithm. The effectiveness of the proposed approach is demonstrated with both numerical simulation experiment and real-world case study.

Original languageEnglish (US)
Title of host publication2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages91-96
Number of pages6
ISBN (Print)9781538604892
DOIs
StatePublished - Aug 24 2018
Event7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017 - Honolulu, United States
Duration: Jul 31 2017Aug 4 2017

Publication series

Name2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017

Conference

Conference7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
CountryUnited States
CityHonolulu
Period7/31/178/4/17

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Optimization

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  • Cite this

    Liu, J., Wang, Z., Li, M., & Zhang, B. (2018). Detecting Asynchronous Periodic Patterns of Intervals in Temporal Sequence Data. In 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017 (pp. 91-96). [8446166] (2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CYBER.2017.8446166