Modeling heterogeneous time series dynamics to profile big sensor data in complex physical systems

Bin Liu, Haifeng Chen, Abhishek Sharma, Guofei Jiang, Hui Xiong

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

10 Scopus citations

Abstract

While a massive amount of time series can now be collected in many physical systems, it is a challenge to build an analytic model that can correctly profile the data because those time series usually exhibit various behaviors. In this paper we propose an integrated method to address the heterogeneity issue in modeling big time series data. We first extracts relevant features to summarize the underlying dynamics of those series. We present both linear and nonlinear feature extraction techniques, as well as a procedure to determine the right extraction method for individual time series. Given extracted features, our method further models the trajectory pattern of time series in the feature space. Both a regression based and a density based method are presented to profile different types of feature trajectories. Experimental results in a real power plant illustrate that our feature extraction and trajectory model are effective to profile various time series. Our method has been used to successfully detect anomalies in the system.

Original languageEnglish (US)
Title of host publicationProceedings - 2013 IEEE International Conference on Big Data, Big Data 2013
Pages631-638
Number of pages8
DOIs
StatePublished - 2013
Event2013 IEEE International Conference on Big Data, Big Data 2013 - Santa Clara, CA, United States
Duration: Oct 6 2013Oct 9 2013

Publication series

NameProceedings - 2013 IEEE International Conference on Big Data, Big Data 2013

Other

Other2013 IEEE International Conference on Big Data, Big Data 2013
Country/TerritoryUnited States
CitySanta Clara, CA
Period10/6/1310/9/13

All Science Journal Classification (ASJC) codes

  • Software

Keywords

  • Anomaly detection
  • Time series
  • Trajectory model

Fingerprint

Dive into the research topics of 'Modeling heterogeneous time series dynamics to profile big sensor data in complex physical systems'. Together they form a unique fingerprint.

Cite this