Shapelets are subsequences of time-series that represent local patterns and can improve the accuracy and the interpretability of time-series classification. The major task of time-series classification using shapelets is to discover high quality shapelets. However, this is challenging since local patterns may have various scales/lengths rather than a unified scale. In this paper, we resolve this problem by discovering shapelets with multiple scales. We propose a novel Multi-Scale Shapelet Discovery (MSSD) algorithm to discover expressive multi-scale shapelets by extending initial single-scale shapelets (i.e., shapelets with a unified scale). MSSD adopts a bi-directional extension process and is robust to extend single-shapelets obtained by different methods. A supervised shapelet quality measurement is further developed to qualify the extension of shapelets. Comprehensive experiments conducted on 25 UCR time-series datasets show that multi-scale shapelets discovered by MSSD improve classification accuracy by around 10% (in average), compared with single-scale shapelets discovered by counterpart methods.

中文翻译：

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用于时间序列分类的多尺度 Shapelets 发现

Shapelets 是时间序列的子序列，代表局部模式，可以提高时间序列分类的准确性和可解释性。使用 shapelets 进行时间序列分类的主要任务是发现高质量的 shapelets。然而，这是具有挑战性的，因为局部模式可能具有不同的比例/长度，而不是统一的比例。在本文中，我们通过发现具有多个尺度的 shapelet 来解决这个问题。我们提出了一种新颖的多尺度Shapelet 发现(MSSD) 算法，通过扩展初始单尺度shapelet（即具有统一尺度的shapelets）来发现富有表现力的多尺度shapelets。MSSD采用双向扩展过程，对于扩展通过不同方法获得的单个shapelets具有鲁棒性。进一步开发了一种有监督的 shapelet 质量测量来限定 shapelet 的扩展。在 25 个 UCR 时间序列数据集上进行的综合实验表明，与对应方法发现的单尺度 shapelet 相比，MSSD 发现的多尺度 shapelet 将分类精度提高了约 10%（平均）。