document: Logical-shapelets : an expressive primitive for time series classification Time series shapelets are small , local patterns in a time series that are highly predictive of a class and are thus very useful features for building classifiers and for certain visualization and summarization tasks . While shapelets were introduced only recently , they have already seen significant adoption and extension in the community . Despite their immense potential as a data mining primitive , there are two important limitations of shapelets . First , their expressiveness is limited to simple binary presence\/absence questions . Second , even though shapelets are computed offline , the time taken to compute them is significant . In this work , we address the latter problem by introducing a novel algorithm that finds shapelets in less time than current methods by an order of magnitude . Our algorithm is based on intelligent caching and reuse of computations , and the admissible pruning of the search space . Because our algorithm is so fast , it creates an opportunity to consider more expressive shapelet queries . In particular , we show for the first time an augmented shapelet representation that distinguishes the data based on conjunctions or disjunctions of shapelets . We call our novel representation Logical-Shapelets . We demonstrate the efficiency of our approach on the classic benchmark datasets used for these problems , and show several case studies where logical shapelets significantly outperform the original shapelet representation and other time series classification techniques . We demonstrate the utility of our ideas in domains as diverse as gesture recognition , robotics , and biometrics .
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