论文标题
时间序列域中的分发检测：一种新颖的季节性评分方法
Out-of-Distribution Detection in Time-Series Domain: A Novel Seasonal Ratio Scoring Approach
论文作者
论文摘要
用于现实世界应用程序的时间序列分类器的安全部署依赖于检测数据不是从与培训数据相同的分布中生成的数据的能力。此任务称为离分布（OOD）检测。我们考虑了时间序列域的OOD检测的新问题。我们讨论了时间序列数据带来的独特挑战，并解释了为什么来自图像域的先前方法会表现不佳。在这些挑战的推动下，本文提出了一种新颖的{\ em季节性评分（SRS）}方法。 SRS由三个关键算法步骤组成。首先，将每个输入分解为类别的语义组件和余数。其次，使用这种分解来估计输入的阶级条件可能性以及使用深层生成模型的剩余可能性。从这些估计值中计算出季节性比率得分。第三，从分布数据中确定阈值间隔以检测OOD示例。对不同现实世界基准测试的实验表明，与基线方法相比，SRS方法非常适合于时间序列OOD检测。 SRS方法的开源代码在https://github.com/tahabelkhouja/srs上提供
Safe deployment of time-series classifiers for real-world applications relies on the ability to detect the data which is not generated from the same distribution as training data. This task is referred to as out-of-distribution (OOD) detection. We consider the novel problem of OOD detection for the time-series domain. We discuss the unique challenges posed by time-series data and explain why prior methods from the image domain will perform poorly. Motivated by these challenges, this paper proposes a novel {\em Seasonal Ratio Scoring (SRS)} approach. SRS consists of three key algorithmic steps. First, each input is decomposed into class-wise semantic component and remainder. Second, this decomposition is employed to estimate the class-wise conditional likelihoods of the input and remainder using deep generative models. The seasonal ratio score is computed from these estimates. Third, a threshold interval is identified from the in-distribution data to detect OOD examples. Experiments on diverse real-world benchmarks demonstrate that the SRS method is well-suited for time-series OOD detection when compared to baseline methods. Open-source code for SRS method is provided at https://github.com/tahabelkhouja/SRS
