Classification Et Prévision Avec Les Algorithmes d''auto-Organisation
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L'esprit de ce travail est à rapprocher d'une citation de Jean-Paul Benzécri, "le modèle doit suivre les données et non l'inverse." Ainsi, la méthode de prévision contient un pré processus d'analyse de données. Les cartes d'auto-organisation, privilégiées dans ce pré processus, ont été complétées par des outils aide à leur l'interprétation pour améliorer leur potentiel d'analyse. Ces outils se sont avérés performants pour analyser un grand nombre de données réelles issues de domaines variés tels que l'analyse de la consommation des canadiens ou du chômage. Ils ont été améliorés à mesure que leur champ d'application s'étendait pour aboutir à un ensemble d'outils complémentaires d'analyse et de visualisation des données dont la performance sera développée en première partie. La seconde partie présente la méthode de prévision qui s'écarte des méthodes récursives et s'adapte à des contextes nouveaux. En particulier, pour la prévision de la consommation électrique, elle solutionne des problèmes structurels aux séries chronologiques (comme la non- stationnarité) qui limitent les performances des techniques traditionnelles ou neuronales.
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