Essec\Faculty\Model\Contribution {#2216
  #_index: "academ_contributions"
  #_id: "13910"
  #_source: array:26 [
    "id" => "13910"
    "slug" => "model-selection-for-weakly-dependent-time-series-forecasting"
    "yearMonth" => "2012-08"
    "year" => "2012"
    "title" => "Model selection for weakly dependent time series forecasting"
    "description" => "ALQUIER, P. et WINTENBERGER, O. (2012). Model selection for weakly dependent time series forecasting. <i>Bernoulli: A Journal of Mathematical Statistics and Probability</i>, 18(3), pp. 883-913."
    "authors" => array:2 [
      0 => array:3 [
        "name" => "ALQUIER Pierre"
        "bid" => "B00809923"
        "slug" => "alquier-pierre"
      ]
      1 => array:1 [
        "name" => "WINTENBERGER Olivier"
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:8 [
      0 => "adaptative inference"
      1 => "aggregation of estimators"
      2 => "autoregression estimation"
      3 => "model selection"
      4 => "randomized estimators"
      5 => "statistical learning"
      6 => "time series prediction"
      7 => "weak dependence"
    ]
    "updatedAt" => "2023-03-22 10:26:35"
    "publicationUrl" => "https://projecteuclid.org/journals/bernoulli/volume-18/issue-3/Model-selection-for-weakly-dependent-time-series-forecasting/10.3150/11-BEJ359.full"
    "publicationInfo" => array:3 [
      "pages" => "883-913"
      "volume" => "18"
      "number" => "3"
    ]
    "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" => "Observing a stationary time series, we propose a two-steps procedure for the prediction of its next value. The first step follows machine learning theory paradigm and consists in determining a set of possible predictors as randomized estimators in (possibly numerous) different predictive models. The second step follows the model selection paradigm and consists in choosing one predictor with good properties among all the predictors of the first step. We study our procedure for two different types of observations: causal Bernoulli shifts and bounded weakly dependent processes. In both cases, we give oracle inequalities: the risk of the chosen predictor is close to the best prediction risk in all predictive models that we consider. We apply our procedure for predictive models as linear predictors, neural networks predictors and nonparametric autoregressive predictors."
      "en" => "Observing a stationary time series, we propose a two-steps procedure for the prediction of its next value. The first step follows machine learning theory paradigm and consists in determining a set of possible predictors as randomized estimators in (possibly numerous) different predictive models. The second step follows the model selection paradigm and consists in choosing one predictor with good properties among all the predictors of the first step. We study our procedure for two different types of observations: causal Bernoulli shifts and bounded weakly dependent processes. In both cases, we give oracle inequalities: the risk of the chosen predictor is close to the best prediction risk in all predictive models that we consider. We apply our procedure for predictive models as linear predictors, neural networks predictors and nonparametric autoregressive predictors."
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2024-11-21T09:21:53.000Z"
    "docTitle" => "Model selection for weakly dependent time series forecasting"
    "docSurtitle" => "Journal articles"
    "authorNames" => "<a href="/cv/alquier-pierre">ALQUIER Pierre</a>, WINTENBERGER Olivier"
    "docDescription" => "<span class="document-property-authors">ALQUIER Pierre, WINTENBERGER Olivier</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2012</span>"
    "keywordList" => "<a href="#">adaptative inference</a>, <a href="#">aggregation of estimators</a>, <a href="#">autoregression estimation</a>, <a href="#">model selection</a>, <a href="#">randomized estimators</a>, <a href="#">statistical learning</a>, <a href="#">time series prediction</a>, <a href="#">weak dependence</a>"
    "docPreview" => "<b>Model selection for weakly dependent time series forecasting</b><br><span>2012-08 | Journal articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://projecteuclid.org/journals/bernoulli/volume-18/issue-3/Model-selection-for-weakly-dependent-time-series-forecasting/10.3150/11-BEJ359.full" target="_blank">Model selection for weakly dependent time series forecasting</a>"
  ]
  +lang: "en"
  +"_type": "_doc"
  +"_score": 8.608349
  +"parent": null
}