FastDTW是近似的，通常比它近似的算法慢
FastDTW is approximate and Generally Slower than the Algorithm it Approximates
摘要：
许多时间序列数据采掘问题可以通过重复使用距离测度来解决。这些任务的示例包括相似性搜索、聚类、分类、异常检测和分割。二十多年来，人们已经知道动态时间规整（DTW）距离测量是用于大多数领域中大多数任务的最佳测量。由于经典DTW算法具有二次时间复杂度，因此人们提出了许多方法来减少其摊销时间，或者快速逼近它，其中最常用的一种方法是FastDTW。FastDTW算法有超过一千次引用，并已明确用于数百项研究工作。在这项工作中，我们提出了一个令人惊讶的主张。在任何实际的数据采掘应用中，近似的FastDTW比精确的DTW慢得多。这一事实显然对使用该算法的社区有影响：允许它处理更大的数据集，获得准确的结果，并在更短的时间内完成。
英文摘要：
Many time series data mining problems can be solved with repeated use of distance measure. Examples of such tasks include similarity search, clustering, classification, anomaly detection and segmentation. For over two decades it has been known that the Dynamic Time Warping (DTW) distance measure is the best measure to use for most tasks, in most domains. Because the classic DTW algorithm has quadratic time complexity, many ideas have been introduced to reduce its amortized time, or to quickly approximate it. One of the most cited approximate approaches is FastDTW. The FastDTW algorithm has well over a thousand citations and has been explicitly used in several hundred research efforts. In this work, we make a surprising claim. In any realistic data mining application, the approximate FastDTW is much slower than the exact DTW. This fact clearly has implications for the community that uses this algorithm: allowing it to address much larger datasets, get exact results, and do so in less time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
