Scalable Discovery of Time-Series Shapelets
This work addresses a scalability bottleneck for researchers and practitioners in data mining dealing with time-series data, offering a significant speedup with improved accuracy.
The paper tackles the computational expense of discovering shapelets for time-series classification by proposing a method that uses online clustering pruning and supervised selection, resulting in a 3-4 orders of magnitude speed improvement over existing methods while achieving better prediction accuracy.
Time-series classification is an important problem for the data mining community due to the wide range of application domains involving time-series data. A recent paradigm, called shapelets, represents patterns that are highly predictive for the target variable. Shapelets are discovered by measuring the prediction accuracy of a set of potential (shapelet) candidates. The candidates typically consist of all the segments of a dataset, therefore, the discovery of shapelets is computationally expensive. This paper proposes a novel method that avoids measuring the prediction accuracy of similar candidates in Euclidean distance space, through an online clustering pruning technique. In addition, our algorithm incorporates a supervised shapelet selection that filters out only those candidates that improve classification accuracy. Empirical evidence on 45 datasets from the UCR collection demonstrate that our method is 3-4 orders of magnitudes faster than the fastest existing shapelet-discovery method, while providing better prediction accuracy.