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

Authors : S CANKURT, A SUBASİ

Pages : 42-49

View : 15 | Download : 9

Publication Date : 2015-02-27

Article Type : Research Paper

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 insert ignore into journalissuearticles values(MLP); regression, and support vector regression insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(RAE); and root relative squared error insert ignore into journalissuearticles values(RRSE); measurements, the proposed machine learning models have achieved significantly better forecasting accuracy when the auxiliary variables have been employed
Keywords : Developing, tourism, demand