{
  "id": "1507.08011",
  "version": "v1",
  "published": "2015-07-29T04:15:02.000Z",
  "updated": "2015-07-29T04:15:02.000Z",
  "title": "UFCORIN: A Fully Automated Predictor of Solar Flares in GOES X-Ray Flux",
  "authors": [
    "Takayuki Muranushi",
    "Takuya Shibayama",
    "Yuko Hada Muranushi",
    "Hiroaki Isobe",
    "Shigeru Nemoto",
    "Kenji Komazaki",
    "Kazunari Shibata"
  ],
  "comment": "43 pages, 7 figures, submitted to Space Weather",
  "categories": [
    "astro-ph.SR"
  ],
  "abstract": "We have developed UFCORIN, a platform for studying and automating space weather prediction. Using our system we have tested 6,160 different combinations of SDO/HMI data as input data, and simulated the prediction of GOES X-ray flux for 2 years (2011-2012) with one-hour cadence. We have found that direct comparison of the true skill statistics (TSS) is ill-posed, and used the standard scores ($z$) of the TSS to compare the performance of the various prediction strategies. The best strategies we have found for predicting X, $\\geq$M and $\\geq$C class flares are better than the average of the 6,160 strategies by 2.3$\\sigma$, 2.1$\\sigma$, 3.8$\\sigma$ confidence levels, respectively. The best three's TSS values were $0.745\\pm0.072$, $0.481\\pm0.017$, and $0.557\\pm0.043$, respectively.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2015-07-29T04:15:02.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "fully automated predictor",
      "x-ray flux",
      "solar flares",
      "best threes tss values",
      "true skill statistics"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 43,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}