Efficient Shapelet Discovery for Time Series Classification

Time-series shapelets are discriminative subsequences, recently found effective for time series classification (
tsc
). It is evident that the quality of shapelets is crucial to the accuracy of
tsc
. However, major research has focused on building accurate models from some shapelet candidates. To determine such candidates, existing studies are surprisingly simple, e.g., enumerating subsequences of some fixed lengths, or randomly selecting some subsequences as shapelet candidates. The major bulk of computation is then on building the model from the candidates. In this paper, we propose a novel
efficient shapelet discovery
method, called
bspcover
, to discover a set of high-quality shapelet candidates for model building. Specifically,
bspcover
generates abundant candidates via Symbolic Aggregate approXimation with sliding window, then prunes identical and highly similar candidates via
Bloom filters
, and
similarity matching
, respectively. We next propose a
$p$p-Cover algorithm
to efficiently determine discriminative shapelet candidates that maximally represent each time-series class. Finally, any existing shapelet learning method can be adopted to build a classification model. We have conducted extensive experiments with well-known time-series datasets and representative state-of-the-art methods. Results show that
bspcover
speeds up the state-of-the-art methods by more than 70 times, and the accuracy is often comparable to or higher than existing works.