Scalable and Accurate Subsequence Transform for Time Series Classification

Abstract

Time series classification using phase-independent subsequences called shapelets is one of the best approaches in the state of the art. This approach is especially characterized by its interpretable property and its fast prediction time. However, given a dataset of n time series of length at most m, learning shapelets requires a computation time of O(n^2m^4) which is too high for practical datasets. In this paper, we exploit the fact that shapelets are shared by the members of the same class to propose the SAST (Scalable and Accurate Subsequence Transform) algorithm which has a time complexity of O(nm^3). SAST is accurate, interpretable and does not learn redundant shapelets. The experiments we conducted on the UCR archive datasets showed that SAST is more accurate than the state of the art Shapelet Transform algorithm while being significantly more scalable.