Machine-learning pour la prédiction des prix dans le secteur du tourisme en ligne

Abstract : The goal of this paper is to consider the design of decision-making tools in the context of varying travel prices from the customer’s perspective. Based on vast streams of heterogeneous historical data collected through the internet, we describe here two approaches to forecasting travel price changes at a given horizon, taking as input variables a list of descriptive characteristics of the flight, together with possible features of the past evolution of the related price series. Though heterogeneous in many respects ( e.g. sampling, scale), the collection of historical prices series is here represented in a unified manner, by marked point processes (MPP). State-of-the-art supervised learning algorithms, possibly combined with a preliminary clustering stage, grouping flights whose related price series exhibit similar behavior, can be next used in order to help the customer to decide when to purchase her/his ticket.
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Till Wohlfarth. Machine-learning pour la prédiction des prix dans le secteur du tourisme en ligne. Analyse de données, Statistiques et Probabilités [physics.data-an]. Télécom ParisTech, 2013. Français. ⟨NNT : 2013ENST0090⟩. ⟨tel-01310537⟩

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