caa a22 4500 44587144X CHVBK 20180317145513.0 cr unu---uuuuu 170323e20110101xx s 000 0 eng 10.1007/s10618-010-0179-5 doi (NATIONALLICENCE)springer-10.1007/s10618-010-0179-5 Time series shapelets: a novel technique that allows accurate, interpretable and fast classification [Elektronische Daten] [Lexiang Ye, Eamonn Keogh] Classification of time series has been attracting great interest over the past decade. While dozens of techniques have been introduced, recent empirical evidence has strongly suggested that the simple nearest neighbor algorithm is very difficult to beat for most time series problems, especially for large-scale datasets. 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 high 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 and to make the classification result more explainable, which global characteristics of the nearest neighbor cannot provide. 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. We can use the distance to the shapelet, rather than the distance to the nearest neighbor to classify objects. As we shall show with extensive empirical evaluations in diverse domains, classification algorithms based on the time series shapelet primitives can be interpretable, more accurate, and significantly faster than state-of-the-art classifiers. The Author(s), 2010 Time series nationallicence Data mining nationallicence Classification nationallicence Decision tree nationallicence Ye Lexiang Department of Computer Science and Engineering, University of California, 92521, Riverside, CA, USA aut Keogh Eamonn Department of Computer Science and Engineering, University of California, 92521, Riverside, CA, USA aut Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 22/1-2(2011-01-01), 149-182 1384-5810 22:1-2<149 2011 22 10618 https://doi.org/10.1007/s10618-010-0179-5 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10618-010-0179-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Ye Lexiang Department of Computer Science and Engineering, University of California, 92521, Riverside, CA, USA aut NATIONALLICENCE 700 1- Keogh Eamonn Department of Computer Science and Engineering, University of California, 92521, Riverside, CA, USA aut NATIONALLICENCE 773 0- Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 22/1-2(2011-01-01), 149-182 1384-5810 22:1-2<149 2011 22 10618 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer