Application and Accuracy Improvement of Big Data Analytics in Market Demand Forecasting in Tourism Economy

Abstract The development of tourism economy can benefit a series of tourism supporting industries, which is one of the grips to enhance China’s economic vitality, and grasping the market demand of tourism economy is the key to the development of tourism. For this reason, this paper predicts the market demand of tourism economy based on tourism big data. Systematic clustering is used to preprocess tourism data, and a seasonal product tourism prediction model is constructed based on the seasonal characteristics of tourism to realize the accurate prediction of tourism demand. The clustering results show that the inbound sources of Fujian Province come from Hong Kong, Macao and Taiwan, China, as well as Japan, Korea, Malaysia, the United States, Australia and several countries in Western Europe. The seasonal fluctuation of tourism data can be reduced by taking logarithmic and seasonal difference, which is conducive to the construction of seasonal product demand prediction model. The ARIMA model constructed in this paper has a better real-value fitting effect and successfully predicts the number of inbound travelers in Fujian Province between 2014 and 2019, which improves the prediction accuracy of the tourism demand market and proves the value of the application of big data in tourism market analysis.