Time Series Motif Discovery (TSMD) refers to the task of identifying patterns that occur multiple times (possibly with minor variations) in a time series. All existing methods for TSMD have one or more of the following limitations: they only look for the two most similar occurrences of a pattern; they only look for patterns of a pre-specified, fixed length; they cannot handle variability along the time axis; and they only handle univariate time series. In this paper, we present a new method, LoCoMotif, that has none of these limitations. The method is motivated by a concrete use case from physiotherapy. We demonstrate the value of the proposed method on this use case. We also introduce a new quantitative evaluation metric for motif discovery, and benchmark data for comparing TSMD methods. LoCoMotif substantially outperforms the existing methods, on top of being more broadly applicable.

翻译：时间序列模式发现（TSMD）是指识别时间序列中多次出现（可能带有微小变化）模式的任务。现有TSMD方法均存在以下一个或多个局限性：仅寻找模式的两个最相似出现；仅寻找预先指定的固定长度模式；无法处理时间轴上的变异性；以及仅处理单变量时间序列。本文提出一种名为LoCoMotif的新方法，该方法不存在上述任何局限。该方法受物理治疗领域的具体应用场景启发，我们在此应用场景上论证了所提方法的实用价值。同时，我们引入了一种新的模式发现定量评估指标，以及用于比较TSMD方法的基准数据。LoCoMotif在具有更广泛适用性的同时，显著优于现有方法。