Discovering All-Chain Set in Streaming Time Series

  • Shaopeng WangEmail author
  • Ye Yuan
  • Hua Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11439)


Time series chains discovery is an increasingly popular research area in time series mining. Previous studies on this topic process fixed-length time series. In this work, we focus on the issue of all-chain set mining over the streaming time series, where the all-chain set is a very important kind of the time series chains. We propose a novel all-chain set mining algorithm about streaming time series (ASMSTS) to solve this problem. The main idea behind the ASMSTS is to obtain the mining results at current time-tick based on the ones at the last one. This makes the method more efficiency in time and space than the Naïve. Our experiments illustrate that ASMSTS does indeed detect the all-chain set correctly and can offer dramatic improvements in speed and space cost over the Naive method.


Streaming time series Time series chains All-chain set 



This research is supported by: the Natural Science Foundation of Inner Mongolia in China (Grant nos. 2018BS06001), the National Natural Science Foundation of China (Grant nos. 61862047, 61572119, 61622202), and the Fundamental Research Funds for the Central universities (Grant No.N150402005).


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Inner Mongolia UniversityHohhotChina
  2. 2.Northeastern UniversityShenyangChina

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