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• W2400366940 abstract "Previous chapter Next chapter Full AccessProceedings Proceedings of the 2014 SIAM International Conference on Data Mining (SDM)Discovering Groups of Time Series with Similar Behavior in Multiple Small Intervals of TimeG. Atluri, M. Steinbach, K. O. Lim, A. MacDonald III, and V. KumarG. Atluri, M. Steinbach, K. O. Lim, A. MacDonald III, and V. Kumarpp.1001 - 1009Chapter DOI:https://doi.org/10.1137/1.9781611973440.114PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract 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. Previous chapter Next chapter RelatedDetails Published:2014eISBN:978-1-61197-344-0 https://doi.org/10.1137/1.9781611973440Book Series Name:ProceedingsBook Code:PRDT14Book Pages:1-1086" @default.
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• W2400366940 title "Discovering Groups of Time Series with Similar Behavior in Multiple Small Intervals of Time" @default.
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