Research on Forecasting Tourism Consumption Trends Based on Data Analysis Techniques

Tourism consumption is one of the important driving forces for economic development, and the study predicts the future development of tourism consumption trends based on data analysis of past tourism consumption. Three data analysis models, namely, univariate regression, grey system and exponential smoothing, are integrated and optimized to construct an optimized combination prediction model, which is applied to the prediction of tourism consumption trend. The optimized combination forecasting model is compared with the single forecasting model to test the superiority of the optimized combination model. City A is chosen as an example research object, and its inbound tourism data from 2007 to 2023 are inputted into the optimal combination model to predict the number of inbound tourists and the foreign exchange income of international tourism in the next five years (2024-2028). The average relative error (1.65%) between the predicted value and the actual value of the optimized combination prediction model in this paper is much lower than that of the other single prediction models, and the prediction performance is the best. the number of inbound tourists of City A in 2024-2028 grows year by year, with an average growth rate of 6.40%, the average growth rate of the Southeast Asian tourists is the highest (13.43%), the average growth rate of the European tourists is the lowest (2.98%). From 2024 to 2028, the foreign exchange earnings of international tourism in City A are expected to increase from 108.66 million yuan to 265.24 million yuan, with annual incremental percentages of more than 20% in each of the five years.