论文标题
动态时间翘曲距离的统计推断，并应用于异常时间序列检测
Statistical Inference for the Dynamic Time Warping Distance, with Application to Abnormal Time-Series Detection
论文作者
论文摘要
我们通过考虑对从动态时间扭曲（DTW）算法获得的距离进行统计假设测试，研究了不确定环境下两个时间序列之间相似性/距离的统计推断。 DTW距离的采样分布太难得出，因为它是基于DTW算法的解决方案获得的，该算法很复杂。为了避免这种困难，我们建议采用条件选择性推理框架，这使我们能够在DTW距离上得出有效的推理方法。据我们所知，这是第一种可以提供有效的P值来量化DTW距离的统计意义的方法，这有助于高标准决策，例如异常的时间序列检测问题。我们评估了在合成数据集和实际数据集上提出的推理方法的性能。
We study statistical inference on the similarity/distance between two time-series under uncertain environment by considering a statistical hypothesis test on the distance obtained from Dynamic Time Warping (DTW) algorithm. The sampling distribution of the DTW distance is too difficult to derive because it is obtained based on the solution of the DTW algorithm, which is complicated. To circumvent this difficulty, we propose to employ the conditional selective inference framework, which enables us to derive a valid inference method on the DTW distance. To our knowledge, this is the first method that can provide a valid p-value to quantify the statistical significance of the DTW distance, which is helpful for high-stake decision making such as abnormal time-series detection problems. We evaluate the performance of the proposed inference method on both synthetic and real-world datasets.
