Machine-learning for price prediction in the online tourism sector.

Summary We are interested in the problem of predicting the occurrence of a price decrease in order to provide advice for the immediate or deferred purchase of a trip on a price comparison website. The proposed methodology is based on the statistical learning of a price evolution model from the joint information of attributes of the considered trip and past observations of its price and "popularity". The main originality consists in representing the price evolution by the inhomogeneous point process of its jumps. From a database constituted by liligo.com, we implement a learning method of a price evolution model. This model allows us to provide a predictor of the occurrence of a price drop over a given future period and thus to provide a purchase or waiting advice to the customer.