{
  "id": "1712.06428",
  "version": "v1",
  "published": "2017-12-18T14:40:23.000Z",
  "updated": "2017-12-18T14:40:23.000Z",
  "title": "A Shapelet Transform for Multivariate Time Series Classification",
  "authors": [
    "Aaron Bostrom",
    "Anthony Bagnall"
  ],
  "categories": [
    "cs.LG"
  ],
  "abstract": "Shapelets are phase independent subsequences designed for time series classification. We propose three adaptations to the Shapelet Transform (ST) to capture multivariate features in multivariate time series classification. We create a unified set of data to benchmark our work on, and compare with three other algorithms. We demonstrate that multivariate shapelets are not significantly worse than other state-of-the-art algorithms.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-12-18T14:40:23.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "multivariate time series classification",
      "shapelet transform",
      "phase independent subsequences",
      "capture multivariate features",
      "state-of-the-art algorithms"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}