Research On International Flight Price Forecasting Model Based On Big Data

With the rapidly development of society and the improvement of people's living standards,tourism has gradually become a necessity of life.At the same time,air tickets are an indispensable part of tourism products.Tickets price is an important element of tourism,which is changeable with the market sales.If a flight sells well,the airlines will increase its price,on the contrary,the airlines will cut the price.Furthermore,the airlines will sign a price protection agreement in addition to price competition.For example,the price within 3 days before takeoff should not be less than 20%off.Therefore,flight's price changes is a very complex issue and it is relatively difficult to forecast.At the same time,price forecasting technology has a huge market and a very good prospect.In this paper,we analyze the feasibility.of forecasting the tickets price from the perspective of data mining,especially the tickets price forecasting of international flight.Firstly,we describe the development of big data theories,the technology of big data,the data mining method and application based on big data.Moreover,we also elaborate the definition and development of tourism big data,the influence of flight price changes on the forecast results.Secondly,we introduce the artificial neural network model that applied in the international flight price and explain the basic principles and characteristics of artificial neural network.Finally,we focus on the deep learning network model based on big data,which is popular in recent years.In our method,we establish an international flight price forecasting model based on big data and artificial neural network,and use it to analyze and forecast the price of real international airline that from Beijing to Paris.The computer system automatically collected data every 6 hours from November 2016 to February 2017,then forty thousand historical data were collected.In this paper,we use a large number of real historical data as training samples,and then use the trained model to predict the real-time flight prices.We compare the results of the model prediction with the real situation,and the results show that the model is effective.From the results,we can see that the model accuracy rate can reach about 70%.On the whole,the research of this model has important practical significance and it has a great improvement space.