Essec\Faculty\Model\Contribution {#2188
  #_index: "academ_contributions"
  #_id: "14844"
  #_source: array:26 [
    "id" => "14844"
    "slug" => "cross-temporal-forecast-reconciliation-at-digital-platforms-with-machine-learning"
    "yearMonth" => "2024-06"
    "year" => "2024"
    "title" => "Cross-temporal forecast reconciliation at digital platforms with machine learning"
    "description" => "ROMBOUTS, J., TERNES, M. et WILMS, I. (2024). Cross-temporal forecast reconciliation at digital platforms with machine learning. <i>International Journal of Forecasting</i>, In press."
    "authors" => array:3 [
      0 => array:3 [
        "name" => "ROMBOUTS Jeroen"
        "bid" => "B00469813"
        "slug" => "rombouts-jeroen"
      ]
      1 => array:1 [
        "name" => "Ternes Marie"
      ]
      2 => array:1 [
        "name" => "Wilms Ines"
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:5 [
      0 => "Hierarchical time series"
      1 => "Forecast reconciliation"
      2 => "Machine learning"
      3 => "Cross-temporal aggregation"
      4 => "Demand forecasting"
    ]
    "updatedAt" => "2024-06-13 01:01:18"
    "publicationUrl" => "https://doi.org/10.1016/j.ijforecast.2024.05.008"
    "publicationInfo" => array:3 [
      "pages" => ""
      "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" => "Platform businesses operate on a digital core, and their decision-making requires high-dimensional accurate forecast streams at different levels of cross-sectional (e.g., geographical regions) and temporal aggregation (e.g., minutes to days). It also necessitates coherent forecasts across all hierarchy levels to ensure aligned decision-making across different planning units such as pricing, product, controlling, and strategy. Given that platform data streams feature complex characteristics and interdependencies, we introduce a non-linear hierarchical forecast reconciliation method that produces crosstemporal reconciled forecasts in a direct and automated way through popular machine learning methods. The method is sufficiently fast to allow forecast-based high-frequency decision-making that platforms require. We empirically test our framework on unique, large-scale streaming datasets from a leading on-demand delivery platform in Europe and a bicycle-sharing system in New York City."
      "en" => "Platform businesses operate on a digital core, and their decision-making requires high-dimensional accurate forecast streams at different levels of cross-sectional (e.g., geographical regions) and temporal aggregation (e.g., minutes to days). It also necessitates coherent forecasts across all hierarchy levels to ensure aligned decision-making across different planning units such as pricing, product, controlling, and strategy. Given that platform data streams feature complex characteristics and interdependencies, we introduce a non-linear hierarchical forecast reconciliation method that produces crosstemporal reconciled forecasts in a direct and automated way through popular machine learning methods. The method is sufficiently fast to allow forecast-based high-frequency decision-making that platforms require. We empirically test our framework on unique, large-scale streaming datasets from a leading on-demand delivery platform in Europe and a bicycle-sharing system in New York City."
    ]
    "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" => "Cross-temporal forecast reconciliation at digital platforms with machine learning"
    "docSurtitle" => "Journal articles"
    "authorNames" => "<a href="/cv/rombouts-jeroen">ROMBOUTS Jeroen</a>, Ternes Marie, Wilms Ines"
    "docDescription" => "<span class="document-property-authors">ROMBOUTS Jeroen, Ternes Marie, 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="#">Hierarchical time series</a>, <a href="#">Forecast reconciliation</a>, <a href="#">Machine learning</a>, <a href="#">Cross-temporal aggregation</a>, <a href="#">Demand forecasting</a>"
    "docPreview" => "<b>Cross-temporal forecast reconciliation at digital platforms with machine learning</b><br><span>2024-06 | Journal articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://doi.org/10.1016/j.ijforecast.2024.05.008" target="_blank">Cross-temporal forecast reconciliation at digital platforms with machine learning</a>"
  ]
  +lang: "en"
  +"_type": "_doc"
  +"_score": 8.93827
  +"parent": null
}