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Forecasting tourism demand using search query data: A hybrid modelling approach
16 references·9 citing·36 second hop
Forecasting tourism demand using search query data: A hybrid modelling approach (2019)Forecasting tourism demand using search q…Predicting the Present with Google Trends (2012)Predicting the Present with G…Forecasting private consumption: survey-based indicators vs. Google trends (2011)Forecasting private consumpti…A novel hybridization of artificial neural networks and ARIMA models for time series forecasting (2010)A novel hybridization of arti…Automatic Time Series Forecasting: The forecast Package for R (2008)Automatic Time Series Forecas…Tourism demand modelling and forecasting—A review of recent research (2007)Tourism demand modelling and …A new statistic and practical guidelines for nonparametric Granger causality testing (2006)A new statistic and practical…Time series forecasting using a hybrid ARIMA and neural network model (2003)Time series forecasting using…Neural network forecasting for seasonal and trend time series (2003)Neural network forecasting fo…Forecasting with artificial neural networks: (1998)Forecasting with artificial n…Neural Network Models for Time Series Forecasts (1996)Neural Network Models for Tim…Statistical inference in vector autoregressions with possibly integrated processes (1995)Statistical inference in vect…Forecasting tourism demand: A review of empirical research (1995)Forecasting tourism demand: A…Testing for neglected nonlinearity in time series models (1993)Testing for neglected nonline…Testing the null hypothesis of stationarity against the alternative of a unit root (1992)Testing the null hypothesis o…Time Series Analysis Forecasting and Control. (1971)Time Series Analysis Forecast…Investigating Causal Relations by Econometric Models and Cross-spectral Methods (1969)Investigating Causal Relation…Retail Demand Forecasting: A Comparative Study for Multivariate Time Series (2023)Retail Demand Forecasting: A …Forecasting hourly attraction tourist volume with search engine and social media data for decision support (2023)Forecasting hourly attraction…Can multi-source heterogeneous data improve the forecasting performance of tourist arrivals amid COVID-19? Mixed-data sampling approach (2023)Can multi-source heterogeneou…Search query and tourism forecasting during the pandemic: When and where can digital footprints be helpful as predictors? (2022)Search query and tourism fore…Tourism demand nowcasting using a LASSO-MIDAS model (2021)Tourism demand nowcasting usi…Tourism demand forecasting: An ensemble deep learning approach (2021)Tourism demand forecasting: A…Forecasting daily attraction demand using big data from search engines and social media (2021)Forecasting daily attraction …Forecasting tourist arrivals: Google Trends meets mixed-frequency data (2019)Forecasting tourist arrivals:…Forecasting the number of inbound tourists with Google Trends (2019)Forecasting the number of inb…Time Series Analysis: Forecasting and Control (1977)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Managerial Applications of Neural Networks: The Case of Bank Failure Predictions (1992)Financial time series forecasting using support vector machines (2003)DeepAR: Probabilistic forecasting with autoregressive recurrent networks (2019)Distribution of the Estimators for Autoregressive Time Series with a Unit Root (1979)Distribution of the Estimators for Autoregressive Time Series With a Unit Root (1979)Testing for a Unit Root in Time Series Regression (1988)A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test (1999)Unit root tests for panel data (2001)Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework (2014)Time Series Analysis, Forecasting and Control. (1971)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)The accuracy of extrapolation (time series) methods: Results of a forecasting competition (1982)An artificial neural network (p,d,q) model for timeseries forecasting (2009)An Empirical Comparison of Machine Learning Models for Time Series Forecasting (2010)Forecasting: theory and practice (2022)Statistical analysis of cointegration vectors (1988)Tourism as a long-run economic growth factor: the Spanish case (2002)Energy consumption, income, and carbon emissions in the United States (2006)Energy consumption, carbon emissions, and economic growth in China (2009)Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root (1981)Another look at measures of forecast accuracy (2006)Statistical and Machine Learning forecasting methods: Concerns and ways forward (2018)Recurrent Neural Networks for Time Series Forecasting: Current status and future directions (2020)A hybrid ARIMA and support vector machines model in stock price forecasting (2004)Oil and the Macroeconomy since World War II (1983)Giving Content to Investor Sentiment: The Role of Media in the Stock Market (2007)Testing for Granger non-causality in heterogeneous panels (2012)In Search of Attention (2011)Text as Data (2019)Some recent development in a concept of causality (1988)Unit root tests in panel data: asymptotic and finite-sample properties (2002)
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References

  • 1969Investigating Causal Relations by Econometric Models and Cross-spectral Methods22,971 cited
  • 1992Testing the null hypothesis of stationarity against the alternative of a unit root12,634 cited
  • 1971Time Series Analysis Forecasting and Control.6,714 cited
  • 1995Statistical inference in vector autoregressions with possibly integrated processes6,097 cited
  • 2003Time series forecasting using a hybrid ARIMA and neural network model4,469 cited
  • 1998Forecasting with artificial neural networks:4,225 cited
  • 2008Automatic Time Series Forecasting: The forecast Package for R3,538 cited
  • 2012Predicting the Present with Google Trends2,353 cited
  • 2007Tourism demand modelling and forecasting—A review of recent research1,419 cited
  • 2003Neural network forecasting for seasonal and trend time series987 cited
  • 2006A new statistic and practical guidelines for nonparametric Granger causality testing962 cited
  • 1995Forecasting tourism demand: A review of empirical research896 cited
  • 2010A novel hybridization of artificial neural networks and ARIMA models for time series forecasting825 cited
  • 2011Forecasting private consumption: survey-based indicators vs. Google trends560 cited
  • 1993

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Testing for neglected nonlinearity in time series models
498 cited
  • 1996Neural Network Models for Time Series Forecasts468 cited
  • Cited by

    • 2022Search query and tourism forecasting during the pandemic: When and where can digital footprints be helpful as predictors?70 cited
    • 2019Forecasting tourist arrivals: Google Trends meets mixed-frequency data65 cited
    • 2021Forecasting daily attraction demand using big data from search engines and social media61 cited
    • 2023Can multi-source heterogeneous data improve the forecasting performance of tourist arrivals amid COVID-19? Mixed-data sampling approach58 cited
    • 2021Tourism demand forecasting: An ensemble deep learning approach57 cited
    • 2023Forecasting hourly attraction tourist volume with search engine and social media data for decision support49 cited
    • 2019Forecasting the number of inbound tourists with Google Trends41 cited
    • 2021Tourism demand nowcasting using a LASSO-MIDAS model39 cited
    • 2023Retail Demand Forecasting: A Comparative Study for Multivariate Time Series37 cited