| Summary: | Abstract As a comprehensive form of trade, tourism service trade has had a profound impact on the economies of various countries. This research mainly discusses the tourism service trade forecasting algorithm based on the PSO-optimized hybrid RVM model. This study extracts 8 indicators including gross national product, total fixed asset investment, total import and export, China's import and export tariff rate, the exchange rate of renminbi to the US dollar, and the global economic growth rate. The same as the impact indicators of tourism service trade, but there is a certain degree of redundancy and correlation in these indicators. In order to measure the correlation between the evaluation indicators, the autocorrelation evaluation function in MATLAB is used, and the principal component analysis method is used to extract the principal components that can represent the indicators in a larger percentage. In order to improve the prediction accuracy of the RVM model, based on the adaptive construction model structure and initial model weights, the PSO algorithm is used to optimize the RVM model weights. The optimization process takes the minimum error of the RVM model as the algorithm search target, and each represents the RVM model. The algorithm finds the value and threshold of the optimal RVM model through the particle swarm tracking search algorithm and then uses the original RVM model and the optimized RVM prediction respectively total amount of tourism service trade in City A, and compares the prediction errors of the single RVM method and the PSO-optimized RVM method, and analyzes the degree of model prediction error reduction after the PSO model optimizes the RVM model. According to the forecast result, the relative average error of 2020 is 5.7%, and the forecast result is relatively accurate. This research is helpful to provide scientific reference for my country's tourism service trade.
|
|---|
