Prediction of Tourist Consumption Based on Bayesian Network and Big Data

Abstract

The boom of tourism has generated a huge amount of data on tourist consumption. The consumption trends of tourists could be mined out of these data, promoting the development of tourism. Taking air ticket price as an example of tourist consumption, this paper designs a novel model to predict the trend of tourist consumption in the next moment based on the historical data, rather than the traditional approach to analyze the influencing factors of tourist consumption. Our prediction model was established based on the Bayesian network (BN). Only two variables were considered in the model, i.e. the number of the remaining seats and the ticket price. Three different scoring functions were tested with our model. The effectiveness of our model was confirmed through experiments and a comparative analysis with the neural network (NN). The results show that our BN model achieved an accuracy greater than 80% in predicting the air ticket price in the next moment, and outperformed the NN model on the same dataset; the fluctuation of the air ticket price in the next moment can be predicted accurately based on the price trends in the previous two days. The research results lay the basis for predicting the price volatility of the other product/service of tourism.

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