Existing methods for finding correlations between bursty time series are limited to collections consisting of a small number of time series. In this paper, we present a novel approach for mining correlation in collections consisting of a large number of time series. In our approach, we use bursts co-occurring in different streams as the measure of their relatedness. By exploiting the pruning properties of our measure we develop new indexing structures and algorithms that allow for efficient mining of related pairs from millions of streams. An experimental study performed on a large time series collection demonstrates the efficiency and scalability of the proposed approach.
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