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Discovering Semantics from Multiple Correlated Time Series Stream

  • Zhi Qiao
  • Guangyan Huang
  • Jing He
  • Peng Zhang
  • Li Guo
  • Jie Cao
  • Yanchun Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7819)

Abstract

In this paper, we study a challenging problem of mining data generating rules and state transforming rules (i.e., semantics) underneath multiple correlated time series streams. A novel Correlation field-based Semantics Learning Framework (CfSLF) is proposed to learn the semantic. In the framework, we use Hidden Markov Random Field (HMRF) method to model relationship between latent states and observations in multiple correlated time series to learn data generating rules. The transforming rules are learned from corresponding latent state sequence of multiple time series based on Markov chain character. The reusable semantics learned by CfSLF can be fed into various analysis tools, such as prediction or anomaly detection. Moreover, we present two algorithms based on the semantics, which can later be applied to next-n step prediction and anomaly detection. Experiments on real world data sets demonstrate the efficiency and effectiveness of the proposed method.

Keywords

Semantics correlated time series streams prediction anomaly detection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Zhi Qiao
    • 1
    • 2
  • Guangyan Huang
    • 1
  • Jing He
    • 1
  • Peng Zhang
    • 2
  • Li Guo
    • 2
  • Jie Cao
    • 3
  • Yanchun Zhang
    • 1
  1. 1.Victoria UniversityMelbourneAustralia
  2. 2.Institute of Information EngineeringChinese Academy of ScienceBeijingChina
  3. 3.Nanjing University of Finance and EconomicsChina

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