Essec\Faculty\Model\Contribution {#2188
  #_index: "academ_contributions"
  #_id: "14855"
  #_source: array:26 [
    "id" => "14855"
    "slug" => "fast-forecasting-of-unstable-data-streams-for-on-demand-service-platforms"
    "yearMonth" => "2024-05"
    "year" => "2024"
    "title" => "Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms"
    "description" => "HU, Y.J., ROMBOUTS, J. et WILMS, I. (2024). Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms. <i>Information Systems Research</i>, In press, pp. 1-20."
    "authors" => array:3 [
      0 => array:3 [
        "name" => "ROMBOUTS Jeroen"
        "bid" => "B00469813"
        "slug" => "rombouts-jeroen"
      ]
      1 => array:1 [
        "name" => "Hu Yu Jeffrey"
      ]
      2 => array:1 [
        "name" => "Wilms Ines"
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:4 [
      0 => "e-commerce"
      1 => "platform econometrics"
      2 => "streaming data"
      3 => "forecast breakdown"
    ]
    "updatedAt" => "2024-06-20 01:01:17"
    "publicationUrl" => "https://doi.org/10.1287/isre.2023.0130"
    "publicationInfo" => array:3 [
      "pages" => "1-20"
      "volume" => "In press"
      "number" => ""
    ]
    "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" => "On-demand service platforms face a challenging problem of forecasting a large collection of high-frequency regional demand data streams that exhibit instabilities. This paper develops a novel forecast framework that is fast and scalable and automatically assesses changing environments without human intervention. We empirically test our framework on a large-scale demand data set from a leading on-demand delivery platform in Europe and find strong performance gains from using our framework against several industry benchmarks across all geographical regions, loss functions, and both pre- and post-COVID periods. We translate forecast gains to economic impacts for this on-demand service platform by computing financial gains and reductions in computing costs."
      "en" => "On-demand service platforms face a challenging problem of forecasting a large collection of high-frequency regional demand data streams that exhibit instabilities. This paper develops a novel forecast framework that is fast and scalable and automatically assesses changing environments without human intervention. We empirically test our framework on a large-scale demand data set from a leading on-demand delivery platform in Europe and find strong performance gains from using our framework against several industry benchmarks across all geographical regions, loss functions, and both pre- and post-COVID periods. We translate forecast gains to economic impacts for this on-demand service platform by computing financial gains and reductions in computing costs."
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d’Information, Sciences de la Décision et Statistiques"
      "en" => "Information Systems, Decision Sciences and Statistics"
    ]
    "indexedAt" => "2024-07-16T09:22:06.000Z"
    "docTitle" => "Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms"
    "docSurtitle" => "Journal articles"
    "authorNames" => "<a href="/cv/rombouts-jeroen">ROMBOUTS Jeroen</a>, Hu Yu Jeffrey, Wilms Ines"
    "docDescription" => "<span class="document-property-authors">ROMBOUTS Jeroen, Hu Yu Jeffrey, Wilms Ines</span><br><span class="document-property-authors_fields">Information Systems, Decision Sciences and Statistics</span> | <span class="document-property-year">2024</span>"
    "keywordList" => "<a href="#">e-commerce</a>, <a href="#">platform econometrics</a>, <a href="#">streaming data</a>, <a href="#">forecast breakdown</a>"
    "docPreview" => "<b>Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms</b><br><span>2024-05 | Journal articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://doi.org/10.1287/isre.2023.0130" target="_blank">Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms</a>"
  ]
  +lang: "en"
  +"_type": "_doc"
  +"_score": 8.574781
  +"parent": null
}