Developing tourism demand forecasting models using machine learning techniques with trend, seasonal, and cyclic components

Abstract

—This paper proposes the deterministic generation of auxiliary variables, which outline the seasonal, cyclic and trend components of the time series associated with tourism demand for the machine learning models. To test the contribution of the deterministically generated auxiliary variables, we have employed multilayer perceptron (MLP) regression, and support vector regression (SVR) models, which are the well-known stateof- art machine learning models. These models are used to make multivariate tourism forecasting for Turkey respected to two data sets: raw data set and data set with deterministically generated auxiliary variables. The forecasting performances are compared regards to these two data sets. In terms of relative absolute error (RAE) and root relative squared error (RRSE) measurements, the proposed machine learning models have achieved significantly better forecasting accuracy when the auxiliary variables have been employed

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