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Out-of-distribution Detection in Time-series Domain: A Novel Seasonal Ratio Scoring Approach.
- Source :
-
ACM Transactions on Intelligent Systems & Technology . Feb2024, Vol. 15 Issue 1, p1-24. 24p. - Publication Year :
- 2024
-
Abstract
- Safe deployment of time-series classifiers for real-world applications relies on the ability to detect the data that 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 article proposes a novel 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *TIME series analysis
*DATA distribution
*DEEP learning
Subjects
Details
- Language :
- English
- ISSN :
- 21576904
- Volume :
- 15
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- ACM Transactions on Intelligent Systems & Technology
- Publication Type :
- Academic Journal
- Accession number :
- 174955417
- Full Text :
- https://doi.org/10.1145/3630633