@prefix mdl: <http://data.loterre.fr/ark:/67375/MDL> .
@prefix skos: <http://www.w3.org/2004/02/skos/core#> .

mdl: a skos:ConceptScheme .
mdl:-CHRVCFHM-4
  skos:prefLabel "time series analysis"@en, "analyse des séries temporelles"@fr ;
  a skos:Concept ;
  skos:related mdl:-R4ZKNJ3S-T .

mdl:-R4ZKNJ3S-T
  a skos:Concept ;
  skos:hiddenLabel "autoregressive moving average models"@en, "autoregressive moving average model"@en, "modèle de Box Jenkins"@fr, "modèles de Box Jenkins"@fr, "Processus ARMA"@fr, "autoregressive moving average processes"@en, "autoregressive-moving-average processes"@en, "autoregressive moving average process"@en, "Autoregressive moving average processes"@en, "autoregressive-moving-average models"@en, "modèles de Box-Jenkins"@fr ;
  skos:exactMatch <https://en.wikipedia.org/wiki/Autoregressive%E2%80%93moving-average_model>, <https://fr.wikipedia.org/wiki/ARMA> ;
  skos:prefLabel "Modèle ARMA"@fr, "autoregressive-moving-average model"@en ;
  skos:definition "In the statistical analysis of time series, autoregressive–moving-average (ARMA) models provide a parsimonious description of a (weakly) stationary stochastic process in terms of two polynomials, one for the autoregression (AR) and the second for the moving average (MA). The general ARMA model was described in the 1951 thesis of Peter Whittle, Hypothesis testing in time series analysis, and it was popularized in the 1970 book by George E. P. Box and Gwilym Jenkins. Given a time series of data X_t, the ARMA model is a tool for understanding and, perhaps, predicting future values in this series. The AR part involves regressing the variable on its own lagged (i.e., past) values. The MA part involves modeling the error term as a linear combination of error terms occurring contemporaneously and at various times in the past. The model is usually referred to as the ARMA(p,q) model where p is the order of the AR part and q is the order of the MA part (as defined below). (Wikipedia, The Free Encyclopedia, <a href=\"https://en.wikipedia.org/wiki/Autoregressive%E2%80%93moving-average_model\" target=\"_blank\">https://en.wikipedia.org/wiki/Autoregressive%E2%80%93moving-average_model</a>)"@en, "En statistique, les modèles ARMA (modèles autorégressifs et moyenne mobile), ou aussi modèle de Box-Jenkins, sont les principaux modèles de séries temporelles. Étant donné une série temporelle X_t, le modèle ARMA est un outil pour comprendre et prédire, éventuellement, les valeurs futures de cette série. Le modèle est composé de deux parties : une part autorégressive (AR) et une part moyenne-mobile (MA). Le modèle est généralement noté ARMA(p,q), où p est l'ordre de la partie AR et q l'ordre de la partie MA. (Wikipedia, L'Encylopédie Libre, <a href=\"https://fr.wikipedia.org/wiki/ARMA\" target=\"_blank\">https://fr.wikipedia.org/wiki/ARMA</a>)"@fr ;
  skos:altLabel "processus ARMA"@fr, "ARMA model"@en, "autoregressive-moving-average process"@en, "modèle de Box-Jenkins"@fr, "ARMA process"@en ;
  skos:inScheme mdl: ;
  skos:related mdl:-CHRVCFHM-4 ;
  skos:broader mdl:-RR85CG1F-3 .

mdl:-RR85CG1F-3
  skos:prefLabel "processus stochastique"@fr, "stochastic process"@en ;
  a skos:Concept ;
  skos:narrower mdl:-R4ZKNJ3S-T .