A propos de l’algorithme Microsoft Time Series…

MTS en quelques mots…

MTS ou série temporelle en français est un algorithme qui utilise un ensemble d’algorithmes de régression qui permettent de livrer des prédictions sur un jeu de données.

mts_fren

Pour se mettre dans le contexte, un algorithme de régression est une technique de calcul de survenue d’un évènement.

Les séries temporelles sont beaucoup utilisées pour prédire…

  • l’économie
  • les ventes
  • les budgets
  • les marchés
  • les populations
  • etc…

Ci-dessous, voilà à quoi ressemble une série temporelle :

mts_serietemporelle

La différence avec l’arbre de décision ?

Il ne nécessite pas de colonnes supplémentaires avec de nouvelles informations pour prédire une tendance.

Son fonctionnement ?

Deux algorithmes entrent en jeu :

  • Algorithme ARTXP : optimisé pour les prédictions à court terme.
  • Algorithme ARIMA : optimisé pour les prédictions à long terme.

Le modèle se base sur un jeu de données d’origine et un intervalle de temps régulier entre chaque valeur. Ces deux jeux de données sont utilisés pour créer le modèle (série).

Il est possible d’ajouter de nouvelles données au modèle lorsque l’on fait des prédictions et de les intégrer automatiquement aux données de base. Il est important de faire évoluer les prédictions en fonctions des nouveaux jeux de données.

Pour les premières prédictions, l’algorithme ARTXP (court terme) est utilisé. Plus les prédictions s’éloignent dans le temps, plus ces dernières seront déterminées par l’algorithme ARIMA (long terme). Ces algorithmes détectent également les répétitions saisonnières des valeurs pour améliorer les prédictions (pratique pour les ventes etc…).

ARTXP peut se définir comme un modèle d’arborescence autoregressif.

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