Essec\Faculty\Model\Contribution {#2233
  #_index: "academ_contributions"
  #_id: "10538"
  #_source: array:26 [
    "id" => "10538"
    "slug" => "dynamic-clustering-of-energy-markets-an-extended-hidden-markov-approach"
    "yearMonth" => "2014-12"
    "year" => "2014"
    "title" => "Dynamic clustering of energy markets: An extended hidden Markov approach"
    "description" => "RAMOS, S. et DIAS, J. (2014). Dynamic clustering of energy markets: An extended hidden Markov approach. <i>Expert Systems with Applications</i>, 41(17), pp. 7722-7729."
    "authors" => array:2 [
      0 => array:3 [
        "name" => "RAMOS Sofia"
        "bid" => "B00683001"
        "slug" => "ramos-sofia"
      ]
      1 => array:1 [
        "name" => "DIAS José"
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:4 [
      0 => "Hidden Markov models (HMMs)"
      1 => "Clustering"
      2 => "Time series"
      3 => "Energy markets"
    ]
    "updatedAt" => "2021-07-13 14:31:37"
    "publicationUrl" => "https://doi.org/10.1016/j.eswa.2014.05.030"
    "publicationInfo" => array:3 [
      "pages" => "7722-7729"
      "volume" => "41"
      "number" => "17"
    ]
    "type" => array:2 [
      "fr" => "Articles"
      "en" => "Journal articles"
    ]
    "support_type" => array:2 [
      "fr" => "Revue scientifique"
      "en" => "Scientific journal"
    ]
    "countries" => array:2 [
      "fr" => null
      "en" => null
    ]
    "abstract" => array:2 [
      "fr" => """
        This paper studies the synchronization of energy markets using an extended hidden Markov model that\n
        captures between- and within-heterogeneity in time series by defining clusters and hidden states,\n
        respectively. The model is applied to U.S. data in the period from 1999 to 2012. While oil and natural\n
        gas returns are well portrayed by two volatility states, electricity markets need three additional states:\n
        two transitory and one to capture a period of abnormally high volatility. Although some states are\n
        common to both clusters, results favor the segmentation of energy markets as they are not in the same\n
        state at the same time.
        """
      "en" => """
        This paper studies the synchronization of energy markets using an extended hidden Markov model that\n
        captures between- and within-heterogeneity in time series by defining clusters and hidden states,\n
        respectively. The model is applied to U.S. data in the period from 1999 to 2012. While oil and natural\n
        gas returns are well portrayed by two volatility states, electricity markets need three additional states:\n
        two transitory and one to capture a period of abnormally high volatility. Although some states are\n
        common to both clusters, results favor the segmentation of energy markets as they are not in the same\n
        state at the same time.
        """
    ]
    "authors_fields" => array:2 [
      "fr" => "Finance"
      "en" => "Finance"
    ]
    "indexedAt" => "2024-11-21T08:21:48.000Z"
    "docTitle" => "Dynamic clustering of energy markets: An extended hidden Markov approach"
    "docSurtitle" => "Articles"
    "authorNames" => "<a href="/cv/ramos-sofia">RAMOS Sofia</a>, DIAS José"
    "docDescription" => "<span class="document-property-authors">RAMOS Sofia, DIAS José</span><br><span class="document-property-authors_fields">Finance</span> | <span class="document-property-year">2014</span>"
    "keywordList" => "<a href="#">Hidden Markov models (HMMs)</a>, <a href="#">Clustering</a>, <a href="#">Time series</a>, <a href="#">Energy markets</a>"
    "docPreview" => "<b>Dynamic clustering of energy markets: An extended hidden Markov approach</b><br><span>2014-12 | Articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://doi.org/10.1016/j.eswa.2014.05.030" target="_blank">Dynamic clustering of energy markets: An extended hidden Markov approach</a>"
  ]
  +lang: "fr"
  +"_type": "_doc"
  +"_score": 8.613594
  +"parent": null
}