{
  "id": "1802.06095",
  "version": "v1",
  "published": "2018-02-16T19:30:19.000Z",
  "updated": "2018-02-16T19:30:19.000Z",
  "title": "Mining Sub-Interval Relationships In Time Series Data",
  "authors": [
    "Saurabh Agrawal",
    "Saurabh Verma",
    "Gowtham Atluri",
    "Anuj Karpatne",
    "Stefan Liess",
    "Angus Macdonald III",
    "Snigdhansu Chatterjee",
    "Vipin Kumar"
  ],
  "categories": [
    "stat.ML",
    "cs.IR",
    "cs.LG"
  ],
  "abstract": "Time-series data is being increasingly collected and stud- ied in several areas such as neuroscience, climate science, transportation, and social media. Discovery of complex patterns of relationships between individual time-series, using data-driven approaches can improve our understanding of real-world systems. While traditional approaches typically study relationships between two entire time series, many interesting relationships in real-world applications exist in small sub-intervals of time while remaining absent or feeble during other sub-intervals. In this paper, we define the notion of a sub-interval relationship (SIR) to capture inter- actions between two time series that are prominent only in certain sub-intervals of time. We propose a novel and efficient approach to find most interesting SIR in a pair of time series. We evaluate our proposed approach on two real-world datasets from climate science and neuroscience domain and demonstrated the scalability and computational efficiency of our proposed approach. We further evaluated our discovered SIRs based on a randomization based procedure. Our results indicated the existence of several such relationships that are statistically significant, some of which were also found to have physical interpretation.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-02-16T19:30:19.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "time series data",
      "mining sub-interval relationships",
      "traditional approaches typically study relationships",
      "climate science",
      "entire time series"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}