Interval-Focused Similarity Search in Time Series Databases

Similarity search in time series databases usually deals with comparing entire time series objects or subsequence search. In this paper, we formalize the notion of interval-focused similarity queries which take a set of intervals specifying relevant time frames as additional parameter and compare the time series objects only within this user-defined time focus. We propose an original method to efficiently support interval-focused distance range and k-nearest neighbor queries implementing a filter/refinement architecture. In our broad experimental evaluation we show the superiority of our novel approach compared to existing approaches on several real-world data sets.

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