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Visitor arrivals forecasts amid COVID-19: A perspective from the Africa team · Study

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Visitor arrivals forecasts amid COVID-19: A perspective from the Africa team

Nikolaos Kourentzes, Andrea Saayman, Philippe Jean‐Pierre, Davide Provenzano, Mondher Sahli, Neelu Seetaram, Serena Volo

2021-03-20articleAnnals of Tourism Research79 citationsgreen

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Forecasting Techniques and Applications Diverse Aspects of Tourism Research COVID-19 epidemiological studies

Abstract

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Citations in this library

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References

2011Thinking, Fast and Slow

2020Pandemics, tourism and global change: a rapid assessment of COVID-19

1999Simple Heuristics That Make Us Smart

2008Automatic Time Series Forecasting: The forecast Package for R

2000The M3-Competition: results, conclusions and implications

2007Tourism demand modelling and forecasting—A review of recent research

2002A state space framework for automatic forecasting using exponential smoothing methods

2006Exponential smoothing: The state of the art—Part II

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2022Search query and tourism forecasting during the pandemic: When and where can digital footprints be helpful as predictors?

2022Tourism demand forecasting with spatiotemporal features

2023Application of machine learning to cluster hotel booking curves for hotel demand forecasting

2023Can multi-source heterogeneous data improve the forecasting performance of tourist arrivals amid COVID-19? Mixed-data sampling approach

Details

Year2021

Typearticle

VenueAnnals of Tourism Research

Languageen

Files0 public / 0

Identifiers

doi10.1016/j.annals.2021.103197

openalex_workW3139511698

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