Fast Time Series Classification Based on Infrequent Shapelets

Abstract

Time series shapelets are small and local time series subsequences which are in some sense maximally representative of a class. E.Keogh uses distance of the shapelet to classify objects. Even though shapelet classification can be interpretable and more accurate than many state-of-the-art classifiers, there is one main limitation of shapelets, i.e. shapelet classification training process is offline, and uses subsequence early abandon and admissible entropy pruning strategies, the time to compute is still significant. In this work, we address the later problem by introducing a novel algorithm that finds time series shapelet in significantly less time than the current methods by extracting infrequent time series shapelet candidates. Subsequences that are distinguishable are usually infrequent compared to other subsequences. The algorithm called ISDT (Infrequent Shapelet Decision Tree) uses infrequent shapelet candidates extracting to find shapelet. Experiments demonstrate the efficiency of ISDT algorithm on several benchmark time series datasets. The result shows that ISDT significantly outperforms the current shapelet algorithm.