Model selection on tourism forecasting: A comparison between Bayesian model averaging and Lasso | Zendy

Jiyuan Wang | Zendy; Peng Geng | Zendy; Shouyang Wang | Zendy

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Model selection on tourism forecasting: A comparison between Bayesian model averaging and Lasso

Author(s) -

Jiyuan Wang,

Peng Geng,

Shouyang Wang

Publication year - 2017

Publication title -

african journal of business management

Language(s) - English

Resource type - Journals

ISSN - 1993-8233

DOI - 10.5897/ajbm2016.8249

Subject(s) - lasso (programming language) , bayesian probability , selection (genetic algorithm) , computer science , tourism , model selection , econometrics , bayesian inference , data mining , artificial intelligence , machine learning , mathematics , geography , archaeology , world wide web

This study tries to tackle the tourism forecasting problem using online search queries. This recent-developed methodology is subject to several criticisms, one of which is how to choose satisfying search queries to be built in the forecasting model. This study compares two popular candidates, which are the Bayesian Model Averaging (BMA) approach and the Least Absolute Shrinkage and Selector Operator (Lasso) approach. Evidence shows that the two approaches produce similar forecasting performance but different query selection results. Key words: Tourism forecasting, query selection, Bayesian model averaging, Lasso, Baidu query data.

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