Modelling tourism demand to Spain with machine learning techniques. The impact of forecast horizon on model selection
This study assesses the influence of the forecast horizon on the forecasting performance of several machine learning (ML) techniques. We compare the forecast accuracy of Support Vector Regression (SVR) to Neural Network (NN) models, using a linear model as a baseline. We focus on international touri...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2016 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:2445/105829 |
| Acceso en línea: | https://hdl.handle.net/2445/105829 |
| Access Level: | acceso abierto |
| Palabra clave: | Previsió econòmica Turisme Desenvolupament econòmic Xarxes neuronals (Informàtica) Economic forecasting Tourism Economic development Neural networks (Computer science) |
| Sumario: | This study assesses the influence of the forecast horizon on the forecasting performance of several machine learning (ML) techniques. We compare the forecast accuracy of Support Vector Regression (SVR) to Neural Network (NN) models, using a linear model as a baseline. 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 ML methods improve their forecasting accuracy with respect to linear models as forecast horizons increase. This results shows the suitability of SVR for medium and long term forecasting. |
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