Local-shapelets for fast classification of spectrographic measurements

We address this challenge by introducing local-shapelets. This variant of the shapelet method restricts the search for a match between shapelets and time series to the vicinity of the location from which each shapelet was extracted. This significantly reduces the time required to examine each shapelet during both the learning and classification phases. Classification based on local-shapelets is well-suited for spectroscopic data sets as these are typically very tightly aligned. Our experimental results on such data sets demonstrate that the new approach reduces learning and classification time by two orders of magnitude while retaining the accuracy of regular (non-local) shapelets-based classification. In addition, we provide some theoretical justification for local-shapelets.