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 language||English (US)|
|Title of host publication||SIAM International Conference on Data Mining 2014, SDM 2014|
|Editors||Pang Ning-Tan, Arindam Banerjee, Srinivasan Parthasarathy, Zoran Obradovic, Chandrika Kamath, Mohammed Zaki|
|Publisher||Society for Industrial and Applied Mathematics Publications|
|Number of pages||9|
|State||Published - 2014|
|Event||14th SIAM International Conference on Data Mining, SDM 2014 - Philadelphia, United States|
Duration: Apr 24 2014 → Apr 26 2014
|Name||SIAM International Conference on Data Mining 2014, SDM 2014|
|Other||14th SIAM International Conference on Data Mining, SDM 2014|
|Period||4/24/14 → 4/26/14|
Bibliographical noteFunding Information:
This work was supported by NSF Grant IIS-1355072.