Discovering Groups of Time Series with Similar Behavior in Multiple Small Intervals of Time

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 the short-living trends without finding spurious trends unlike other alternative approaches that find many spurious trends. We also demonstrate, using a neuroimaging dataset, that our approach can be used to discover significantly reproducible groups of shared trends when applied on independent sets of time series data. In addition, we demonstrate the utility of our approach on an S&P 500 stocks data set.