Essec\Faculty\Model\Contribution {#2216 ▼
#_index: "academ_contributions"
#_id: "14855"
#_source: array:26 [
"id" => "14855"
"slug" => "14855-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. 
HU, Y.J., ROMBOUTS, J. et WILMS, I. (2024). Fast Forecasting of Unstable Data Streams for On-Demand "
"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" => "2025-03-24 16:09:00"
"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. On-demand service platforms face a challenging problem of forecasting a large collection of high-fre "
"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. On-demand service platforms face a challenging problem of forecasting a large collection of high-fre "
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-04-03T22:21:40.000Z"
"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, Data Analytics and Operations</span> | <span class="document-property-year">2024</span> <span class="document-property-authors">ROMBOUTS Jeroen, Hu Yu Jeffrey, Wilms Ines</span><br><span c "
"keywordList" => "<a href="#">e-commerce</a>, <a href="#">platform econometrics</a>, <a href="#">streaming data</a>, <a href="#">forecast breakdown</a> <a href="#">e-commerce</a>, <a href="#">platform econometrics</a>, <a href="#">streaming data</a>, < "
"docPreview" => "<b>Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms</b><br><span>2024-05 | Journal articles </span> <b>Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms</b><br><span>2024-05 | "
"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> <a href="https://doi.org/10.1287/isre.2023.0130" target="_blank">Fast Forecasting of Unstable Data S "
]
+lang: "en"
+"_type": "_doc"
+"_score": 8.499925
+"parent": null
}
