论文标题
部分可观测时空混沌系统的无模型预测
Robust Time Series Dissimilarity Measure for Outlier Detection and Periodicity Detection
论文作者
论文摘要
动态时间扭曲（DTW）是许多时间序列应用中的有效差异度量。尽管它很受欢迎，但它容易出现噪音和异常值，这导致了测量中的奇异性问题和偏见。 DTW的时间复杂性与时间序列的长度相二次，使其在实时应用中不适用。在本文中，我们提出了一种新颖的时间序列差异度度量，称为RubustDW，以减少噪音和异常值的影响。具体而言，RubustDTW通过利用我们设计的时间图趋势滤波来估算趋势并以交替的方式优化时间扭曲。为了提高效率，我们提出了一个多级框架，该框架以较低的分辨率估算趋势和扭曲功能，然后以较高的分辨率反复完善它们。基于提出的鲁棒性，我们将其进一步扩展到周期性检测和超出时间序列检测。在现实世界数据集上的实验证明了与DTW变体相比，在离群时间序列检测和周期性检测中，鲁棒的表现出色。
Dynamic time warping (DTW) is an effective dissimilarity measure in many time series applications. Despite its popularity, it is prone to noises and outliers, which leads to singularity problem and bias in the measurement. The time complexity of DTW is quadratic to the length of time series, making it inapplicable in real-time applications. In this paper, we propose a novel time series dissimilarity measure named RobustDTW to reduce the effects of noises and outliers. Specifically, the RobustDTW estimates the trend and optimizes the time warp in an alternating manner by utilizing our designed temporal graph trend filtering. To improve efficiency, we propose a multi-level framework that estimates the trend and the warp function at a lower resolution, and then repeatedly refines them at a higher resolution. Based on the proposed RobustDTW, we further extend it to periodicity detection and outlier time series detection. Experiments on real-world datasets demonstrate the superior performance of RobustDTW compared to DTW variants in both outlier time series detection and periodicity detection.
