Frequent Temporal Inter-object Pattern Mining in Time Series

  • Nguyen Thanh VuEmail author
  • Vo Thi Ngoc Chau
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 244)


Nowadays, time series is present in many various domains such as finance, medicine, geology, meteorology, etc. Mining time series for useful hidden knowledge is very significant in those domains to help users get fascinating insights into important temporal relationships of objects/phenomena along the time. Hence, in this paper, we introduce a notion of frequent temporal inter-object pattern and accordingly propose two frequent temporal pattern mining algorithms on a set of different time series. As compared to frequent sequential patterns, frequent temporal inter-object patterns are more informative with explicit and exact temporal information automatically discovered from many various time series. The two proposed algorithms which are brute-force and tree-based are efficiently defined in a level-wise bottom-up approach dealing with the combinatorial explosion problem. As shown in experiments on real financial time series, our work can be further used to efficiently enhance the temporal rule mining process on time series.


Time Series Frequent Pattern Hash Table Pattern Mining Support Count 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Faculty of Computer Science & EngineeringHCMC Uni. of TechnologyHanoiVietnam

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