Shapelet-based algorithms are widely used for time series classification because of their ease of interpretation, but they are currently outperformed by recent state-of-the-art approaches. We present a new formulation of time series shapelets including the notion of dilation, and we introduce a new shapelet feature to enhance their discriminative power for classification. Experiments performed on 112 datasets show that our method improves on the state-of-the-art shapelet algorithm, and achieves comparable accuracy to recent state-of-the-art approaches, without sacrificing neither scalability, nor interpretability.
翻译:基于形状的算法由于易于解释而被广泛用于时间序列分类,但目前其表现超过最新的最新方法。 我们提出了包括推算概念在内的时间序列形状的新配方,我们引入了新的形状特性,以增强其分类的歧视性力量。 在112个数据集上进行的实验表明,我们的方法改进了最先进的形状计算法,并实现了与最近最先进的方法可比的准确性,同时不牺牲可缩放性和可解释性。