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

翻译：将时间序列分类器安全部署至实际应用中，依赖于检测与训练数据分布不一致的数据的能力，该任务被称为分布外检测。本文针对时间序列领域的分布外检测这一新颖问题展开研究，探讨了时间序列数据带来的独特挑战，并阐释了为何图像领域的现有方法在此场景下表现欠佳。基于上述挑战，本文提出一种新型的"季节性比率评分"方法，该方法包含三个关键步骤：首先，将每个输入分解为类别相关的语义分量与余项；其次，利用深度生成模型基于分解结果估算输入与余项的类别条件似然，并据此计算季节性比率评分；最后，从分布内数据中确定阈值区间以检测分布外样本。在多个真实世界基准数据集上的实验表明，与基线方法相比，季节性比率评分方法更适用于时间序列分布外检测任务。本文已开放季节性比率评分方法的源代码，详见https://github.com/tahabelkhouja/SRS。