通过以上对旅游需求预测方法的比较，我们可知就传统的预测方法来说，带有季

Through the above comparison of tourism demand forecasting methods, we can know that as far as traditional forecasting methods are concerned, the combination of exponential smoothing method with seasonal analysis and autoregressive moving average method is a highly accurate tourism demand forecasting method; Although intelligent methods are still exploratory applications in tourism demand forecasting, it can be seen from the conclusions of some researchers that artificial intelligence methods such as neural networks are more accurate than traditional time series methods and causality analysis methods.

Through the above comparison of tourism demand forecasting methods, we can see that for the traditional forecasting methods, the combination of exponential smoothing method with seasonal analysis and autoregressive moving average method is a more accurate tourism demand forecasting method; For the artificial intelligence method, although it is still an exploratory application in tourism demand prediction, it can be seen from the conclusions of some researchers that the artificial intelligence methods such as neural network are more accurate than the traditional time series methods and causality analysis methods<br>

Through the above comparison of tourism demand forecasting methods, we can know that the combination of exponential smoothing method with seasonal analysis and autoregressive moving average method is a highly accurate tourism demand forecasting method. As for the artificial intelligence method, although it is still an exploratory application in tourism demand forecasting, it can be seen from the conclusions of some researchers that artificial intelligence methods such as neural networks are more accurate than traditional time series methods and causal analysis methods.