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.