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A meta-analysis of international tourism demand forecasting and implications for practice
16 references·16 citing·41 second hopsampled
A meta-analysis of international tourism demand forecasting and implications for practice (2014)A meta-analysis of international tourism …Tourism demand modelling and forecasting—A review of recent research (2007)Tourism demand modelling and …The role of transport infrastructure in international tourism development: A gravity model approach (2007)The role of transport infrast…Support vector regression with genetic algorithms in forecasting tourism demand (2006)Support vector regression wit…Another look at measures of forecast accuracy (2006)Another look at measures of f…Time series forecasting using a hybrid ARIMA and neural network model (2003)Time series forecasting using…A comparison of three different approaches to tourist arrival forecasting (2003)A comparison of three differe…Price elasticities of demand for passenger air travel: a meta-analysis (2002)Price elasticities of demand …Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention (2002)Modeling and forecasting tour…Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting (2000)Back-propagation learning in …Forecasting: Methods and Applications (1999)Forecasting: Methods and Appl…Review of international tourism demand models (1997)Review of international touri…Forecasting tourism demand: A review of empirical research (1995)Forecasting tourism demand: A…Forecasting methods and applications (1993)Forecasting methods and appli…An Almost Ideal Demand System (1980)An Almost Ideal Demand SystemAccuracy of Forecasting: An Empirical Investigation (1979)Accuracy of Forecasting: An E…Smoothing, Forecasting and Prediction of Discrete Time Series. (1964)Smoothing, Forecasting and Pr…Using machine learning and big data for efficient forecasting of hotel booking cancellations (2020)Using machine learning and bi…Tourism Demand Forecasting: A Decomposed Deep Learning Approach (2020)Tourism Demand Forecasting: A…Machine Learning in Internet Search Query Selection for Tourism Forecasting (2020)Machine Learning in Internet …Improving Tourist Arrival Prediction: A Big Data and Artificial Neural Network Approach (2020)Improving Tourist Arrival Pre…Forecasting tourism demand with multisource big data (2020)Forecasting tourism demand wi…A decomposition-ensemble approach for tourism forecasting (2020)A decomposition-ensemble appr…Forecasting tourist arrivals at attractions: Search engine empowered methodologies (2018)Forecasting tourist arrivals …Asymmetric impact of oil prices, exchange rate, and inflation on tourism demand in Pakistan: new evidence from nonlinear ARDL (2018)Asymmetric impact of oil pric…New developments in tourism and hotel demand modeling and forecasting (2017)New developments in tourism a…Google Trends and tourists' arrivals: Emerging biases and proposed corrections (2017)Google Trends and tourists' a…Forecasting tourism demand with Google trends: Accuracy comparison of countries versus cities (2017)Forecasting tourism demand wi…Short-term forecasting of Japanese tourist inflow to South Korea using Google trends data (2016)Short-term forecasting of Jap…Remodeling international tourism demand: Old theory and new evidence (2016)Remodeling international tour…Forecasting Tourism Demand with Google Trends For a Major European City Destination (2016)Forecasting Tourism Demand wi…Forecasting Destination Weekly Hotel Occupancy with Big Data (2016)Forecasting Destination Weekl…Mapping time series into networks as a tool to assess the complex dynamics of tourism systems (2015)Mapping time series into netw…Distribution of the Estimators for Autoregressive Time Series with a Unit Root (1979)Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root (1981)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Climate change and international tourism: A simulation study (2005)Analysis of international tourist arrivals in China: The role of World Heritage Sites (2009)Time Series Analysis, Forecasting and Control. (1971)Time Series Analysis: Forecasting and Control (1977)Forecasting Economic Time Series (2000)Social Media in Tourism and Hospitality: A Literature Review (2013)Progress and prospects for event tourism research (2015)Big data in tourism research: A literature review (2018)Theory and Measurement (1984)A Simple Nonparametric Test of Predictive Performance (1992)Error measures for generalizing about forecasting methods: Empirical comparisons (1992)Revisiting the tourism-led-growth hypothesis for Turkey using the bounds test and Johansen approach for cointegration (2008)Tourism supply chain management: A new research agenda (2009)Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations (1991)Initial conditions and moment restrictions in dynamic panel data models (1998)Testing for unit roots in heterogeneous panels (2003)A Spatial Econometric Approach to Model Spillover Effects in Tourism Flows (2012)Gravity models for tourism demand: theory and use (2014)The effects of natural disasters on international tourism: A global analysis (2020)Forecasting with artificial neural networks: (1998)A hybrid ARIMA and support vector machines model in stock price forecasting (2004)A novel hybridization of artificial neural networks and ARIMA models for time series forecasting (2010)Economics and Consumer Behavior (1980)Panel data from time series of cross-sections (1985)Quadratic Engel Curves and Consumer Demand (1997)Theory of Cost and Production Functions (2015)Consumption inequality and partial insurance (2017)Low frequency filtering and real business cycles (1993)An overview of methods for determining OWA weights (2005)Exponential smoothing: The state of the art—Part II (2006)Automatic Time Series Forecasting: The forecast Package for R (2008)Statistical and Machine Learning forecasting methods: Concerns and ways forward (2018)Recurrent Neural Networks for Time Series Forecasting: Current status and future directions (2020)The accuracy of extrapolation (time series) methods: Results of a forecasting competition (1982)25 years of time series forecasting (2006)The theta model: a decomposition approach to forecasting (2000)Improved supply chain management based on hybrid demand forecasts (2005)Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach (2014)
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References

  • 2006Another look at measures of forecast accuracy5,736 cited
  • 2003Time series forecasting using a hybrid ARIMA and neural network model4,469 cited
  • 1980An Almost Ideal Demand System4,298 cited
  • 1993Forecasting methods and applications2,393 cited
  • 2007Tourism demand modelling and forecasting—A review of recent research1,419 cited
  • 1964Smoothing, Forecasting and Prediction of Discrete Time Series.968 cited
  • 1995Forecasting tourism demand: A review of empirical research896 cited
  • 1997Review of international tourism demand models776 cited
  • 2007The role of transport infrastructure in international tourism development: A gravity model approach669 cited
  • 1999Forecasting: Methods and Applications626 cited
  • 1979Accuracy of Forecasting: An Empirical Investigation514 cited
  • 2002Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention424 cited
  • 2006Support vector regression with genetic algorithms in forecasting tourism demand378 cited
  • 2002Price elasticities of demand for passenger air travel: a meta-analysis357 cited
  • 2000Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting

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344 cited
  • 2003A comparison of three different approaches to tourist arrival forecasting322 cited
  • Cited by

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    • 2016Remodeling international tourism demand: Old theory and new evidence208 cited
    • 2018Asymmetric impact of oil prices, exchange rate, and inflation on tourism demand in Pakistan: new evidence from nonlinear ARDL207 cited
    • 2020Tourism Demand Forecasting: A Decomposed Deep Learning Approach179 cited
    • 2020Improving Tourist Arrival Prediction: A Big Data and Artificial Neural Network Approach165 cited
    • 2016Short-term forecasting of Japanese tourist inflow to South Korea using Google trends data139 cited
    • 2017Google Trends and tourists' arrivals: Emerging biases and proposed corrections135 cited
    • 2017Forecasting tourism demand with Google trends: Accuracy comparison of countries versus cities127 cited
    • 2018Forecasting tourist arrivals at attractions: Search engine empowered methodologies115 cited
    • 2020Using machine learning and big data for efficient forecasting of hotel booking cancellations114 cited
    • 2020Machine Learning in Internet Search Query Selection for Tourism Forecasting106 cited
    • 2020A decomposition-ensemble approach for tourism forecasting99 cited
    • 2016Forecasting Tourism Demand with Google Trends For a Major European City Destination93 cited
    • 2015Mapping time series into networks as a tool to assess the complex dynamics of tourism systems90 cited