  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
