document: Time series shapelets : a new primitive for data mining Classification of time series has been attracting great interest over the past decade . Recent empirical evidence has strongly suggested that the simple nearest neighbor algorithm is very difficult to beat for most time series problems . While this may be considered good news , given the simplicity of implementing the nearest neighbor algorithm , there are some negative consequences of this . First , the nearest neighbor algorithm requires storing and searching the entire dataset , resulting in a time and space complexity that limits its applicability , especially on resource-limited sensors . Second , beyond mere classification accuracy , we often wish to gain some insight into the data . In this work we introduce a new time series primitive , time series shapelets , which addresses these limitations . Informally , shapelets are time series subsequences which are in some sense maximally representative of a class . As we shall show with extensive empirical evaluations in diverse domains , algorithms based on the time series shapelet primitives can be interpretable , more accurate and significantly faster than state-of-the-art classifiers .
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