Forecasting Tourism Demand for Lisbon's Region. A Data Mining Approach

Project Report from the year 2017 in the subject Economics - Statistics and Methods, , language: English, abstract: Portugal is conscious that the economic growth and development of its regions can be attained by investing in everything that boosts international tourism activity. The Government Program and the National's Strategic Plan for Tourism shows that, besides the government, other tourism stakeholders such as passenger transport companies, accommodation establishments, restaurants, recreational businesses, among others, rely on tourism demand indicator's forecasts to make decisions. Most of tourism demand forecasting models are time-series and econometric based. A real-world system like tourism industry is dynamic, thus not linear. Machine Learning methods have proven 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 project work is to enhance the available public sources of tourism forecast information and contribute to the tourism stakeholder s strategy in Portugal. More specifically, to develop a multivariate model to forecast international tourism demand through a Data Mining approach. The model development was constrained to publicly available data and machine learning methods. 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. Instead of revealing a best forecasting method or model, as most of previous research sought to, the current project aimed at building the most accurate multivariate forecasting model, based on a database with minimum data assumptions. The objectives were achieved, as the selected model (SMOReg) was successful in generalization capability. The accuracy of the produced forecasts provides some evidence of the reliability of the proposed forecasting 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.