Discovering groups of time series with similar behavior in multiple small intervals of time

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

3 Scopus citations

Abstract

The focus of this paper is to address the problem of discovering groups of time series that share similar behavior in multiple small intervals of time. This problem has two characteristics: i) There are exponentially many combinations of time series that needs to be explored to find these groups, ii) The groups of time series of interest need to have similar behavior only in some subsets of the time dimension. We present an Apriori based approach to address this problem. We evaluate it on a synthetic dataset and demonstrate that our approach can directly find all groups of intermittently correlated time series without finding spurious groups unlike other alternative approaches that find many spurious groups. We also demonstrate, using a neuroimaging dataset, that groups of intermittently coherent time series discovered by our approach are reproducible on independent sets of time series data. In addition, we demonstrate the utility of our approach on an S & P 500 stocks data set.

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining 2014, SDM 2014
EditorsPang Ning-Tan, Arindam Banerjee, Srinivasan Parthasarathy, Zoran Obradovic, Chandrika Kamath, Mohammed Zaki
PublisherSociety for Industrial and Applied Mathematics Publications
Pages1001-1009
Number of pages9
ISBN (Electronic)9781510811515
DOIs
StatePublished - 2014
Event14th SIAM International Conference on Data Mining, SDM 2014 - Philadelphia, United States
Duration: Apr 24 2014Apr 26 2014

Publication series

NameSIAM International Conference on Data Mining 2014, SDM 2014
Volume2

Other

Other14th SIAM International Conference on Data Mining, SDM 2014
CountryUnited States
CityPhiladelphia
Period4/24/144/26/14

Bibliographical note

Funding Information:
This work was supported by NSF Grant IIS-1355072.

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