Motif discovery is a fundamental operation in the analysis of time series data. Existing motif discovery algorithms that support Dynamic Time Warping require manual determination of the exact length of motifs. However, setting appropriate length for interesting motifs can be challenging and selecting inappropriate motif lengths may result in valuable patterns being overlooked. This paper addresses the above problem by proposing algorithms that automatically compute motifs of all lengths using Dynamic Time Warping. Specifically, a batch algorithm as well as an anytime algorithm are designed in this paper, which are refered as BatchMotif and AnytimeMotif respectively. The proposed algorithms achieve significant improvements in efficiency by fully leveraging the correlations between the motifs of different lengths. Experiments conducted on real datasets demonstrate the superiority of both of the proposed algorithms. On average, BatchMotif is 13 times faster than the baseline method. Additionally, AnytimeMotif is 7 times faster than the baseline method and is capable of providing relatively satisfying results with only a small portion of calculation.

基序发现是时间序列数据分析中的一项基本操作。支持动态时间扭曲的现有主题发现算法需要手动确定主题的确切长度。然而，为有趣的图案设置适当的长度可能具有挑战性，并且选择不合适的图案长度可能会导致有价值的图案被忽视。本文通过提出使用动态时间扭曲自动计算所有长度的图案的算法来解决上述问题。具体来说，本文设计了批处理算法和随时算法，分别称为BatchMotif和AnytimeMotif分别。所提出的算法通过充分利用不同长度的主题之间的相关性，实现了效率的显着提高。在真实数据集上进行的实验证明了两种算法的优越性。平均而言，BatchMotif比基线方法快 13 倍。此外，AnytimeMotif 的速度比基线方法快 7 倍，并且只需很少的计算就能提供相对令人满意的结果。