Title

The good, the bad and the ugly on COVID-19 tourism recovery

Document Type

Article

Source of Publication

Annals of Tourism Research

Publication Date

3-1-2021

Abstract

© 2020 Elsevier Ltd This paper is to produce different scenarios in forecasts for international tourism demand, in light of the COVID-19 pandemic. By implementing two distinct methodologies (the Long Short Term Memory neural network and the Generalized Additive Model), based on recent crises, we are able to calculate the expected drop in the international tourist arrivals for the next 12 months. We use a rolling-window testing strategy to calculate accuracy metrics and show that even though all models have comparable accuracy, the forecasts produced vary significantly according to the training data set, a finding that should be alarming to researchers. Our results indicate that the drop in tourist arrivals can range between 30.8% and 76.3% and will persist at least until June 2021.

ISSN

0160-7383

Publisher

Elsevier BV

Volume

87

First Page

103117

Disciplines

Business

Keywords

Coronavirus, Deep learning, Generalized additive model, Pandemia, Tourism demand

Scopus ID

85098188045

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Green: A manuscript of this publication is openly available in a repository

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