Digital marketing strategy for regional tourism demand forecasting using artificial intelligence algorithms

Fast growth within information technologies significantly widens the deployment of Artificial Intelligence (AI) in the tourism industry. In the given context, this research will focus on some advanced AI methods in the prediction of regional tourism demand and the formulation of efficient digital marketing strategies. The predictive models that were developed and tested included one using support vector regression with Long Short-Term Memory (LSTM) networks. The outcomes of the experiments reveal that the LSTM model outperforms the support vector regression model on the test set with a mean squared error of 0.0203, a root mean square error of 0.1425, a mean absolute error of 0.1056 and an R²-score of 0.9785. It significantly improved the predictive performance by contemplating the respective merits of both models, hence delivering relevant analytical tools concerning the management of this tourist destination.