{
  "id": "2207.04306",
  "version": "v1",
  "published": "2022-07-09T17:21:21.000Z",
  "updated": "2022-07-09T17:21:21.000Z",
  "title": "Out-of-Distribution Detection in Time-Series Domain: A Novel Seasonal Ratio Scoring Approach",
  "authors": [
    "Taha Belkhouja",
    "Yan Yan",
    "Janardhan Rao Doppa"
  ],
  "categories": [
    "cs.LG"
  ],
  "abstract": "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",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-07-09T17:21:21.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "novel seasonal ratio scoring approach",
      "time-series domain",
      "out-of-distribution detection",
      "ood detection"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}