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Tourism Demand Foreacsting using Machine Learning Methods

Tourism demand forecasting has attracted the attention of researchers in the last decade. However, most of research focused on traditional quantitative forecasting techniques, such as ARIMA, exponential smoothing, etc. Although these traditional methods have achieved certain levels of success in the tourism research, it would be useful to study the performance of alternative models such as machine learning methods. This is the topic considered in this paper. The goal is to investigate how different machine learning models can be applied in the tourism prediction problem and to assess the performance of seven well known machine learning methods. Furthermore, we investigate the effect of including the time index as an input variable. Speciﬁcally, we consider the tourism demand time series for Hong Kong inbound travel.