In this paper we focus on finding all maximal correlated intervals where a given pair of time series have correlation above a user provided threshold for all its subintervals and for none of its immediate subsuming intervals. Our objective then is to find a longest set of such maximal correlated intervals. We propose a two step solution to achieve this objective. In the first step an efficient bottom-up approach is proposed to discover maximal correlated intervals. In the second step we use a dynamic programming approach to select the longest non-overlapping set. We evaluate the efficiency of our approach on synthetic datasets and compare it with that of a bruteforce approach. Using neuroimaging data that contains activity time series from brain regions, we show the utility of our approach in studying transient nature of relationships between different brain regions.
Atluri, Gowtham; Steinbach, Michael; Lim, Kelvin; MacDonald, Angus; Kumar, Vipin.
Discovering the Longest Set of Distinct Maximal Correlated Intervals in Time Series Data.
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