Modelling tourism demand to Spain with machine learning techniques. The impact of forecast horizon on model selection
This work addresses forecasting challenges for tourism planners in Spain, but it is incremental as it applies existing methods to a specific domain.
The study examined how forecast horizon affects the accuracy of machine learning models for predicting tourism demand to Spain, finding that Support Vector Regression with a Gaussian kernel outperformed other models for longer horizons and that machine learning methods improved over linear models as horizons increased.
This study assesses the influence of the forecast horizon on the forecasting performance of several machine learning techniques. We compare the fo recast accuracy of Support Vector Regression (SVR) to Neural Network (NN) models, using a linear model as a benchmark. We focus on international tourism demand to all seventeen regions of Spain. The SVR with a Gaussian radial basis function kernel outperforms the rest of the models for the longest forecast horizons. We also find that machine learning methods improve their forecasting accuracy with respect to linear models as forecast horizons increase. This result shows the suitability of SVR for medium and long term forecasting.