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Safe deployment of time-series classifiers for real-world applications relieson the ability to detect the data which is not generated from the samedistribution as training data. This task is referred to as out-of-distribution(OOD) detection. We consider the novel problem of OOD detection for thetime-series domain. We discuss the unique challenges posed by time-series dataand explain why prior methods from the image domain will perform poorly.Motivated by these challenges, this paper proposes a novel {\em Seasonal RatioScoring (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 conditionallikelihoods of the input and remainder using deep generative models. Theseasonal ratio score is computed from these estimates. Third, a thresholdinterval is identified from the in-distribution data to detect OOD examples.Experiments on diverse real-world benchmarks demonstrate that the SRS method iswell-suited for time-series OOD detection when compared to baseline methods.Open-source code for SRS method is provided athttps://github.com/tahabelkhouja/SRS

Out-of-Distribution Detection in Time-Series Domain: A Novel Seasonal Ratio Scoring Approach