An Apache Spark Methodology for Forecasting Tourism Demand in Greece

Abstract

Tourism constitutes a vital sector for all countries’ economy and especially for countries like Greece where it holds a significant proportion of the economy. Nowadays, it is crucial for tourism stakeholders to be able to forecast several tourism indicators in order to take appropriate and most profitable decisions. The traditional forecasting models used in tourism are time-series and econometric. In this paper, we propose a methodology which utilizes a data mining technique based on Decision Trees with the aim of providing forecasts for tourism demand taking into account the contribution of explanatory variables. The proposed approach is based on Apache Spark, a robust analytics engine, along with an integrated machine learning library for predicting tourism demand in Greece. The dataset was constructed from publicly available sources and the forecasted (target) variable is the tourist arrivals in Greece for date range 2006 to 2015.