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Discovering semantics from multiple correlated time series stream

conference contribution
posted on 2013-12-01, 00:00 authored by Z Qiao, Guangyan HuangGuangyan Huang, J He, P Zhang, L Guo, J Cao, Y Zhang
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. © Springer-Verlag 2013.

History

Volume

7819

Pagination

509-520

Location

Gold Coast, Qld.

Start date

2013-04-14

End date

2013-04-17

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783642374562

Language

eng

Publication classification

E Conference publication, E1.1 Full written paper - refereed

Copyright notice

2013, Springer

Editor/Contributor(s)

Pei J, Tseng VS, Cao L, Motoda H, Xu G

Title of proceedings

17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part II

Event

Pacific-Asia Conference on Knowledge Discovery and Data Mining (17th ; 2013 : Gold Coast, QLD)

Issue

Part 2

Publisher

Springer

Place of publication

Berlin, Germany

Series

Lecture Notes in Artificial Intelligence

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