The elasticity of the DTW metric provides a more flexible comparison between time series and is used in numerous machine learning domains such as classification or clustering. However, it does not align the measurements at the beginning and end of time series if they have a shift occurring right at the start of one series, with the omitted part appearing at the end of that series. Due to the cyclicity of such series - which lack a definite beginning or end - we rely on the Cyclic DTW approach to propose a less computationally expensive approximation of this calculation method. This approximation will then be employed in conjunction with the K-Means clustering method.

翻译：DTW度量的弹性特性为时间序列提供了更灵活的比较方式，并被广泛应用于分类或聚类等机器学习领域。然而，若某时间序列起始处出现偏移，导致该序列末尾出现缺失部分时，该度量无法对齐序列首尾处的测量值。鉴于此类序列的周期性（即缺乏明确的起点或终点），我们基于Cyclic DTW方法提出一种计算成本更低的近似计算方法。该近似方法将与K-Means聚类算法结合使用。