Abstract
Tourism stakeholders such as the government, passenger transport companies, accommodation establishments, restaurants, recreational businesses, among others, rely on tourism demand indicators’ forecasts to make decisions. Most of tourism demand forecasting models are time-series and econometric based. Machine learning methods are emerging and have been proved to be quite suitable for non-linear modelling. These methods are part of an interdisciplinary field named “Data Mining” which is known by the process of knowledge discovery in databases (KDD). The core drive of this work is to enhance the available public sources of tourism forecast information and to contribute to the tourism stakeholders’ strategy in Portugal. More specifically, a multivariate model to forecast international tourism demand was developed through a Data Mining approach, which assessed models derived by Regression Trees (Random Forests), Artificial Neural Networks and, Support Vector Machines (SVM). The model development was constrained to machine learning methods, publicly available data, and minimum data assumptions. The forecasted demand variable was the nights spent at tourist accommodation establishments in Lisbon’s region, one of the country’s main foreign tourist destinations. The objectives were achieved, as the selected model (SMOReg, support vector regression) was successful in generalization capability. The accuracy of the produced forecasts provides some evidence of the reliability of the proposed model. If institutions and decision makers have information regarding the evolution of the explanatory variables used in this model, the impact on Lisbon’s tourism demand can be assessed, even in case of an emerging recession, as shown using three future plausible scenarios.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 11th IADIS International Conference Information Systems 2018, IS 2018 |
| Editors | Pedro Isaias, Miguel Baptista Nunes, Philip Powell, Luis Rodrigues |
| Publisher | IADIS Press |
| Pages | 58-66 |
| Number of pages | 9 |
| ISBN (Print) | 9789898533746 |
| Publication status | Published - 1 Jan 2018 |
| Event | 11th IADIS International Conference Information Systems 2018, IS 2018 - Lisbon, Portugal Duration: 14 Apr 2018 → 16 Apr 2018 |
Conference
| Conference | 11th IADIS International Conference Information Systems 2018, IS 2018 |
|---|---|
| Country | Portugal |
| City | Lisbon |
| Period | 14/04/18 → 16/04/18 |
Fingerprint
Keywords
- Forecast
- Knowledge discovery
- Lisbon
- Machine learning
- Tourism
Cite this
}
Forecasting tourism demand for Lisbon’s region through a data mining approach. / Ricardo, Hugo; Gonçalves, Ivo; Costa, Ana Cristina.
Proceedings of the 11th IADIS International Conference Information Systems 2018, IS 2018. ed. / Pedro Isaias; Miguel Baptista Nunes; Philip Powell; Luis Rodrigues. IADIS Press, 2018. p. 58-66.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Forecasting tourism demand for Lisbon’s region through a data mining approach
AU - Ricardo, Hugo
AU - Gonçalves, Ivo
AU - Costa, Ana Cristina
N1 - Ricardo, H., Ivo, G., & Costa, A. C. (2018). Forecasting tourism demand for Lisbon’s region through a data mining approach. In M. B. Nunes, P. Isaías, & P. Powell (Eds.), Proceedings of the 11th IADIS International Conference Information Systems 2018 (pp. 58-66). IADIS Press. ISBN: 978-989-8533-74-6
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Tourism stakeholders such as the government, passenger transport companies, accommodation establishments, restaurants, recreational businesses, among others, rely on tourism demand indicators’ forecasts to make decisions. Most of tourism demand forecasting models are time-series and econometric based. Machine learning methods are emerging and have been proved to be quite suitable for non-linear modelling. These methods are part of an interdisciplinary field named “Data Mining” which is known by the process of knowledge discovery in databases (KDD). The core drive of this work is to enhance the available public sources of tourism forecast information and to contribute to the tourism stakeholders’ strategy in Portugal. More specifically, a multivariate model to forecast international tourism demand was developed through a Data Mining approach, which assessed models derived by Regression Trees (Random Forests), Artificial Neural Networks and, Support Vector Machines (SVM). The model development was constrained to machine learning methods, publicly available data, and minimum data assumptions. The forecasted demand variable was the nights spent at tourist accommodation establishments in Lisbon’s region, one of the country’s main foreign tourist destinations. The objectives were achieved, as the selected model (SMOReg, support vector regression) was successful in generalization capability. The accuracy of the produced forecasts provides some evidence of the reliability of the proposed model. If institutions and decision makers have information regarding the evolution of the explanatory variables used in this model, the impact on Lisbon’s tourism demand can be assessed, even in case of an emerging recession, as shown using three future plausible scenarios.
AB - Tourism stakeholders such as the government, passenger transport companies, accommodation establishments, restaurants, recreational businesses, among others, rely on tourism demand indicators’ forecasts to make decisions. Most of tourism demand forecasting models are time-series and econometric based. Machine learning methods are emerging and have been proved to be quite suitable for non-linear modelling. These methods are part of an interdisciplinary field named “Data Mining” which is known by the process of knowledge discovery in databases (KDD). The core drive of this work is to enhance the available public sources of tourism forecast information and to contribute to the tourism stakeholders’ strategy in Portugal. More specifically, a multivariate model to forecast international tourism demand was developed through a Data Mining approach, which assessed models derived by Regression Trees (Random Forests), Artificial Neural Networks and, Support Vector Machines (SVM). The model development was constrained to machine learning methods, publicly available data, and minimum data assumptions. The forecasted demand variable was the nights spent at tourist accommodation establishments in Lisbon’s region, one of the country’s main foreign tourist destinations. The objectives were achieved, as the selected model (SMOReg, support vector regression) was successful in generalization capability. The accuracy of the produced forecasts provides some evidence of the reliability of the proposed model. If institutions and decision makers have information regarding the evolution of the explanatory variables used in this model, the impact on Lisbon’s tourism demand can be assessed, even in case of an emerging recession, as shown using three future plausible scenarios.
KW - Forecast
KW - Knowledge discovery
KW - Lisbon
KW - Machine learning
KW - Tourism
UR - http://www.scopus.com/inward/record.url?scp=85052508965&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9789898533746
SP - 58
EP - 66
BT - Proceedings of the 11th IADIS International Conference Information Systems 2018, IS 2018
A2 - Isaias, Pedro
A2 - Nunes, Miguel Baptista
A2 - Powell, Philip
A2 - Rodrigues, Luis
PB - IADIS Press
ER -