Essec\Faculty\Model\Contribution {#2216
  #_index: "academ_contributions"
  #_id: "13870"
  #_source: array:26 [
    "id" => "13870"
    "slug" => "tight-risk-bound-for-high-dimensional-time-series-completion"
    "yearMonth" => "2022-03"
    "year" => "2022"
    "title" => "Tight risk bound for high dimensional time series completion"
    "description" => "ALQUIER, P., MARIE, N. et ROSIER, A. (2022). Tight risk bound for high dimensional time series completion. <i>The Electronic Journal of Statistics</i>, 16(1), pp. 3001-3035."
    "authors" => array:3 [
      0 => array:3 [
        "name" => "ALQUIER Pierre"
        "bid" => "B00809923"
        "slug" => "alquier-pierre"
      ]
      1 => array:1 [
        "name" => "MARIE Nicolas"
      ]
      2 => array:1 [
        "name" => "ROSIER Amélie"
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:6 [
      0 => "Matrix completion"
      1 => "multivariate time series analysis"
      2 => "matrix factorization"
      3 => "high-dimensional time series"
      4 => "concentration inequalities"
      5 => "mixing"
    ]
    "updatedAt" => "2024-10-31 13:51:19"
    "publicationUrl" => "https://doi.org/10.1214/22-EJS2015"
    "publicationInfo" => array:3 [
      "pages" => "3001-3035"
      "volume" => "16"
      "number" => "1"
    ]
    "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" => "Initially designed for independent datas, low-rank matrix completion was successfully applied in many domains to the reconstruction of partially observed high-dimensional time series. However, there is a lack of theory to support the application of these methods to dependent datas. In this paper, we propose a general model for multivariate, partially observed time series. We show that the least-square method with a rank penalty leads to reconstruction error of the same order as for independent datas. Moreover, when the time series has some additional properties such as periodicity or smoothness, the rate can actually be faster than in the independent case."
      "en" => "Initially designed for independent datas, low-rank matrix completion was successfully applied in many domains to the reconstruction of partially observed high-dimensional time series. However, there is a lack of theory to support the application of these methods to dependent datas. In this paper, we propose a general model for multivariate, partially observed time series. We show that the least-square method with a rank penalty leads to reconstruction error of the same order as for independent datas. Moreover, when the time series has some additional properties such as periodicity or smoothness, the rate can actually be faster than in the independent case."
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2024-12-22T01:21:46.000Z"
    "docTitle" => "Tight risk bound for high dimensional time series completion"
    "docSurtitle" => "Journal articles"
    "authorNames" => "<a href="/cv/alquier-pierre">ALQUIER Pierre</a>, MARIE Nicolas, ROSIER Amélie"
    "docDescription" => "<span class="document-property-authors">ALQUIER Pierre, MARIE Nicolas, ROSIER Amélie</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2022</span>"
    "keywordList" => "<a href="#">Matrix completion</a>, <a href="#">multivariate time series analysis</a>, <a href="#">matrix factorization</a>, <a href="#">high-dimensional time series</a>, <a href="#">concentration inequalities</a>, <a href="#">mixing</a>"
    "docPreview" => "<b>Tight risk bound for high dimensional time series completion</b><br><span>2022-03 | Journal articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://doi.org/10.1214/22-EJS2015" target="_blank">Tight risk bound for high dimensional time series completion</a>"
  ]
  +lang: "en"
  +"_type": "_doc"
  +"_score": 8.543328
  +"parent": null
}