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A novel approach to model selection in tourism demand modeling · Study

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ArticleA novel approach to model selection in tourism demand modeling

Melda Akın2014

Article2014

A novel approach to model selection in tourism demand modeling

87 citations·0/0 files stored·Tourism Management

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Forecasting Techniques and Applications Diverse Aspects of Tourism Research Time Series Analysis and Forecasting

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References

n.d.Forecasting principles and practice 2008Automatic Time Series Forecasting: The forecast Package for R 2007Tourism demand modelling and forecasting—A review of recent research 2010An Empirical Comparison of Machine Learning Models for Time Series Forecasting 1997Review of international tourism demand models 2005Recent Developments in Econometric Modeling and Forecasting 2006Support vector regression with genetic algorithms in forecasting tourism demand 2010The tourism forecasting competition

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2019Spatial-temporal forecasting of tourism demand 2019Forecasting tourist arrivals: Google Trends meets mixed-frequency data 2019Forecasting hotel room prices in selected GCC cities using deep learning 2022Interpretable tourism demand forecasting with temporal fusion transformers amid COVID-19 2015Combination forecasts of tourism demand with machine learning models 2020Inbound tourism demand forecasting framework based on fuzzy time series and advanced optimization algorithm

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Year2014

Typearticle

Published2014-11-23

VenueTourism Management

ISSN-L0261-5177

Languageen

Citations87

Files0 public / 0

ShelfForecasting Techniques and Applications

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doi10.1016/j.tourman.2014.11.004

openalex_workW2025210352

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2021Forecasting Tourism Demand With a New Time-Varying Forecast Averaging Approach

2020A novel BEMD-based method for forecasting tourist volume with search engine data