Local correlation tracking in time series

Spiros Papadimitriou, Jimeng Sun, Philip S. Yu

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

74 Scopus citations

Abstract

We address the problem of capturing and tracking local correlations among time evolving time series. Our approach is based on comparing the local auto-covariance matrices (via their spectral decompositions) of each series and generalizes the notion of linear cross-correlation. In this way, it is possible to concisely capture a wide variety of local patterns or trends. Our method produces a general similarity score, which evolves over time, and accurately reflects the changing relationships. Finally, it can also be estimated incrementally, in a streaming setting. We demonstrate its usefulness, robustness and efficiency on a wide range of real datasets.

Original languageEnglish (US)
Title of host publicationProceedings - Sixth International Conference on Data Mining, ICDM 2006
Pages456-465
Number of pages10
DOIs
StatePublished - 2006
Externally publishedYes
Event6th International Conference on Data Mining, ICDM 2006 - Hong Kong, China
Duration: Dec 18 2006Dec 22 2006

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other6th International Conference on Data Mining, ICDM 2006
Country/TerritoryChina
CityHong Kong
Period12/18/0612/22/06

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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